JP6925896B2 - Community comprehensive care business system - Google Patents

Description

An embodiment of the present invention relates to a community-based comprehensive care business system for executing a community-based comprehensive care business including a long-term care insurance business.

In general, local governments such as municipalities hold basic data such as residents' addresses in order to realize various businesses and businesses that are closely related to the lives of residents. In addition, the local government implements the long-term care insurance business and holds a lot of data related to long-term care insurance. In addition, various data on medical insurance are also available. In addition to these routine tasks and projects, specific measures related to community-based comprehensive care for residents are being implemented, and specific data such as measures are generated and are also possessed.

Each of the above-mentioned businesses and businesses was carried out independently and individually, and various data associated therewith were also managed individually for each business and business. Therefore, it was difficult for the local government to implement a community-based comprehensive care project that supervised these projects.

In addition, in the community-based comprehensive care business implemented by local governments, predetermined measures are implemented for each business area, but it is necessary to set multiple indicators in order to achieve these measures. These indicators need to be set appropriately because they are related to the accuracy of quantitative evaluation such as extraction of issues in the comprehensive community care business and extraction of effective measures by measuring the effects.

For example, the following three methods have been proposed as examples of indicators of mental and physical condition, but each of them has a problem.

1) Indexing the difference in mental and physical condition between 2 time points 2) Indexing the rate of change in mental and physical condition between 2 time points 3) Aggregating the number of transitions by month for newly certified persons at a certain point in time of need for long-term care and certification survey items 1) Problems in indexing the difference in mental and physical condition between two time points ・ In indexing the difference in mental and physical condition between two time points, the period to be aggregated is unclear, and each person has a different time-series change (valid for certification). It is inappropriate as an index based on the period, etc.).

・ Where the certification is renewed varies from person to person, and it is inaccurate to set two time points uniformly.

・ Even if you say that your physical and mental condition has deteriorated, improved, or maintained at two points, you do not know whether or not your healthy life expectancy has been extended. I don't get used to it.

2) Problems in indexing the rate of change in mental and physical condition between two time points ・ The rate of change in mental and physical condition (intuition) between two different time points is used as an index. In addition, it is difficult for local government officials to intuitively understand the absolute value of the maintenance duration of each stage. Therefore, it is unknown whether healthy life expectancy is extended. Therefore, the degree of benefit cost restraint is unknown, and it does not fit into the benefit cost restraint simulation.

3) Problems of counting the number of transitions by month for newly certified persons at a certain point in the degree of long-term care required and certification survey items (Visualization of how long the number of people in each stage will transition to which stage Problems with how to do it)
・ Different results will be obtained depending on how long the evaluation takes.

・ In principle, it is not known how long each target person stays at each stage.

For these reasons, it is not known whether the healthy life expectancy of individual subjects is extended, and therefore, the degree of benefit cost restraint is unknown, and it does not fit into the benefit cost restraint simulation.

Furthermore, as a problem of the conventional method, the length of the maintenance duration of the physical and mental condition of each subject (shortening of extension), the content (frequency, amount, etc.) of various services that affect them, and the implementation during the same period. There was a problem that the relationship between the efforts of the service establishments, etc. (enhancement of qualified staff, participation in various trainings, etc.) was unclear. Therefore, it was not possible to quantitatively extract and verify what kind of measures (services, business establishments, etc.) should be taken to maintain and improve the physical and mental condition.

The above-mentioned problems relate to evaluation methods and solutions for the purpose of suppressing the aggravation of the mental and physical condition of certified persons who have been certified as requiring long-term care (7 stages from support required 1 to long-term care 5). However, for those who have not been certified (healthy people, frail people, non-certified people who use the long-term care prevention / daily life support comprehensive business (new comprehensive business), etc.), concrete and early detection is possible. It is difficult to say that effective evaluation methods and solutions have yet been found.

In particular, for non-certified persons, care managers and various long-term care service establishments do not intervene (there is no systematic existence), so the existing national standard big data such as long-term care certification data and long-term care benefit performance data cannot be utilized. For this reason, the efforts of the area where the person lives (especially small areas such as the daily living area) (expansion and maintenance of contact salons and care prevention classes for the elderly, etc.) and the person's own daily activities (exercise habits, going out, etc.) Communication habits, self-realization contents by hobbies and work, participation in life support services by volunteer points, etc.) It will affect the results.

In this way, until now, it has not been possible to quantitatively extract and verify what kind of measures (small areas and the elderly's own efforts) should be taken for care prevention and early detection.

All of the explanations so far have been for the long-term care-related business area, but it can be seen that the same problem exists even if this is replaced with the medical-related business area. That is, while the mental and physical condition items related to long-term care include the category of long-term care required, the degree of independence of dementia, the degree of independence of disability, and each certification survey item, the mental and physical condition items related to medical treatment include medical examinations and medical examinations by disease. It can be considered that various blood test items measured at the time of medical examination / treatment correspond to each other. However, in the case of long-term care, the elderly are targeted, but in the case of medical care, all generations (elderly, working generation, schoolchildren) are targeted.

The problems related to "deterrence of aggravation by disease" or "disease prevention / early detection" are described below. First, the risk of illness is divided into four groups: healthy, low-risk, medium-risk, and high-risk. Here, a low-risk person is a person who has a high possibility of returning to a healthy person by early detection. Medium-risk individuals are those who have already developed the disease and are progressing, and a drastic solution is difficult. It is possible. High-risk individuals are those who are in a terminal state due to severe illness and those who have difficulty recovering to medium-risk.

It is necessary to assume that this division method and the threshold value of each risk are different for each disease and each test item. Corresponding these risk stages with the state of long-term care can be considered as follows.

Healthy care ⇔ Healthy medical care Frail care (new general business users, etc.) ⇔ Low-risk medical care Persons requiring long-term care or mild care (1 and 2 requiring long-term care) ⇔ Medium-risk medical care Severe long-term care required (3 to 5 long-term care required) ⇔ High-risk medical care It was not possible to quantitatively extract and verify what kind of measures should be taken to realize the above. In this case, the measures include medical examinations, specific health guidance, voluntary efforts by patients or patient reserves, services such as chronic hospitals aimed at preventing aggravation, health insurance associations, and wide-area medical care for the elderly. These are the efforts of various medical insurers such as the Union in the workplace and in the community.

Another problem is the problem of home medical care / long-term care cooperation. As a national medium- to long-term policy, it is certain that the number of patients admitted to chronic hospitals, etc. on the order of hundreds of thousands will shift to home by 2025. However, for patients receiving such home medical care and long-term care (elderly people requiring long-term care), the period of stay at home can be extended (avoidance of reversion to hospitalization) due to close information gathering of staff related to home medical care and long-term care. As a result, it is difficult to say that a concrete and effective evaluation method / solution method that makes it possible to control long-term care benefit costs and medical costs (especially hospitalization costs) has not yet been found.

The above needs to be approached by disease, and integrated index aggregation and PDCA support analysis by literal matching of big data such as medical data such as medical receipts and medical examinations and long-term care data such as long-term care receipts are certified as requiring long-term care. Although essential, so far no effective method has been realized to solve the above problems.

Japanese Patent No. 5567046

The present invention is to provide a community-based comprehensive care business system capable of appropriately setting various indicators and solving the above-mentioned various problems by using a centralized database.

The community-based comprehensive care business system according to the embodiment of the present invention uses a database having unique data such as basic data of residents, nursing care insurance data, medical insurance data, and measures including measures executed by local governments, and determines predetermined measures. It is a community-based comprehensive care business system that can calculate the values of a plurality of indicators preset in order to achieve the above for each predetermined aggregation unit, and the indicators include the implementation status of the measures of the implementer who implements the measures. A structure index, a process index including the service provision status from the implementer to the user of the measure, and an outcome index including the maintenance period of the physical and mental condition of the user are set, and are determined from a preset aggregation time. If the data representing the mental and physical condition stage of the nursing care insurance data of the user changes during the check period retroactively, the numerical value representing the duration until the change of the user is used as the outcome index of the user. The outcome index calculation unit calculated as, and the numerical value representing the performance of the service that the user received from the implementer during the continuation period, are the insurance receipt data of the nursing care insurance data and the medical receipt data of the medical insurance data. The process index calculation unit that calculates as the user's process index using the service provision data by, and the measures that the implementer who provided the service to the user worked on during the continuation period from the unique data such as the measures. It has a structure index calculation unit that calculates a numerical value representing the actual results of the above as a structure index.

According to the above configuration, the linked OPS index in which the outcome index, process index, and structure index are interrelated enables accurate problem extraction by objective and quantitative data analysis, and further measures can be taken. Since the effectiveness is concretely shown, it is possible to effectively extend the period until the deterioration of the same stage duration of the mental and physical condition or shorten the period until the improvement.

It is a schematic diagram explaining the outline of the community-based comprehensive care business system which concerns on embodiment of this invention. It is a figure which shows the structure of the server and the client system used in embodiment of this invention. It is a figure explaining the O index calculation unit, the P index calculation unit, and the S index calculation unit used in the embodiment of the present invention. It is a figure explaining the calculation logic of the continuation period for each mental and physical state stage of the O index calculation part. In the time chart explaining the calculation logic of the duration for each mental and physical state stage of the O index calculation unit, (a) explains the standard operation, and (b) explains the operation when there is no past data. It is a figure explaining the aggregation target determination logic in O index calculation part. It is explanatory drawing which considered hospitalization and discharge in O index calculation part. It is a figure explaining the P index calculation logic of the P index calculation part. It is a figure explaining the S index calculation logic of the S index calculation part. It is a figure explaining eight methods as the O index calculation method in embodiment. It is a figure explaining the improvement case as the O index calculation method in embodiment. It is a figure explaining the effect of extending the period until deterioration and the effect of shortening the period until improvement of the duration of the same stage of mental and physical condition with respect to the O index in the embodiment. It is a figure explaining the deterioration gradient and improvement gradient as O index in an embodiment. It is a figure which shows the present state analysis, problem extraction result by embodiment of this invention. It is a figure which shows the effective measure extraction result by embodiment of this invention. It is an image diagram explaining the difference in the duration of each mental and physical state stage due to the difference in the quality of service according to the embodiment of the present invention. It is a figure explaining the logic which extracts the care service establishment which was predominantly involved in the continuation period for each mental and physical state according to the embodiment of this invention. It is an image diagram explaining the difference in the period until the new certification due to the difference in the efforts of the elderly and the small area according to the embodiment of the present invention. It is an image diagram explaining the handling of data up to the new authorization by embodiment of this invention. It is a figure which shows the relationship between the main illness in the embodiment of this invention, and the mental and physical state of a long-term care system. It is an image diagram which shows the difference (difference in hospitalization period) of the period of stay at home due to the difference in the approach of home medical care / long-term care cooperation in the embodiment of the present invention. It is a figure which shows the example of the case where hospitalization and discharge are repeated with respect to home medical care / long-term care cooperation in embodiment of this invention. It is a functional block diagram explaining the system configuration for the outcome index visualization in embodiment of this invention. It is a functional block diagram explaining the internal processing of the outcome index element processing part shown in FIG. It is a functional block diagram explaining another internal processing of the outcome index element processing part shown in FIG. It is a functional block diagram explaining the internal processing of the element 2D expansion part shown in FIG. It is a figure explaining the application part to the figure | chart of the data developed by the element 2D development part shown in FIG. It is a figure which lists the effective chart and the outcome index element processing method in embodiment of this invention. No. in FIG. 21 1 The histogram which is a chart is shown. No. in FIG. 21 2 The bar graph which is a chart is shown. No. in FIG. 21 3 The bar graph which is a chart is shown. No. in FIG. 21 4 The line graph which is a chart is shown. No. in FIG. 21 5 A line graph which is a chart is shown. It is a figure explaining the 1st stage of the reliability check of the accreditation investigation item in embodiment of this invention. It is a figure explaining the 2nd stage of the reliability check of the accreditation investigation item in embodiment of this invention. It is a figure explaining another method of the reliability check of the accreditation investigation item in embodiment of this invention. It is a functional block diagram explaining the severity prevention priority item selection function in embodiment of this invention. FIG. 30 is a diagram showing a part of a tree shape model diagram used in the time extraction unit for each item of the tree shape model diagram shown in FIG. It is a figure explaining the selection function of the item which has a large contribution rate to the degree of care required in the embodiment of this invention. It is a schematic diagram explaining the outline of the community-based comprehensive care business system which concerns on other embodiment of this invention. It is a figure which shows the structure of the server and the client system used in the other embodiment of this invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

An embodiment of the present invention comprehensively implements and operates various businesses and businesses including various insurers operated by local governments such as municipalities and long-term care insurance businesses in prefectures and countries that are higher-ranking organizations of local governments. To obtain a comprehensive long-term care business system.

Therefore, the community-based comprehensive care business system implemented and operated by the insurer is defined as the lower-level community-based comprehensive care business system, and the community-based comprehensive care business system implemented and operated by the upper-level prefectures and countries is defined as the higher-level community-based comprehensive care. The business system will be described separately for each embodiment.

<< Embodiment of lower-level community-based comprehensive care system >>
As shown in FIG. 1, a database 10 that unifies data related to various businesses and businesses is created. FIG. 1 is a schematic diagram showing the data types configured in the database 10, the solution function 15 provided by the database 10 and the community-based comprehensive care business system, and the target business area 16 of the community-based comprehensive care business.

As described above, local governments such as municipalities have data such as resident address data 111 shown in FIG. 1, resident common number (tentative name) 112, regional characteristic data 113, and regional efforts 114 as basic data 11. ing. In addition, the local government is implementing a long-term care insurance business, and has data such as long-term care certification data 121, long-term care receipt data 122, and long-term care establishment ledger 123 as long-term care insurance data 12. Further, as the medical insurance data 13, data such as the elderly specific medical examination data 131, the National Health Insurance / late-stage elderly medical receipt data 132, and the medical institution ledger 133 are also available.

The various operations and businesses described above have been carried out for many years, and various data, that is, basic data 11, long-term care insurance data 12, and medical insurance data 13, are managed individually in a fixed format.

In addition to these, the local government has implemented measures related to comprehensive community care unique to the local government for residents, and as the data 14 specific to the measures, etc., the basic checklist (results of the daily living area needs survey) 141, subregions, We have data such as policy plans and achievements 142 for business establishments and users. The specific data 14 for these measures and the like are in various formats according to each measure, and are currently stored in a format unique to each department in charge.

Therefore, in the embodiment of the present invention, the database 10 that unifies the necessary parts of these data 11, 12, 13, and 14 is created.

In this database 10, the related parts of these basic data 11, long-term care insurance data 12, medical insurance data 13, and measures-specific data 14 are linked and unified, and these data are unified as a basis for analysis. The database 10 is obtained.

In addition, by creating a database of unique data 14 for measures unique to local governments, it is possible to quantitatively verify the effect of implementing measures. In particular, it will be possible to quantitatively verify the state image of subregions, business establishments, and users, which was difficult in the past, and the effect of implementing a wide variety of measures that have been invested.

As shown in the figure, the comprehensive community care business has multiple business areas 16 as the target business areas: "Nursing care insurance business plan" 161, "Preventive benefits / Nursing care benefit business" 162, "Dementia measures" 163, "Nursing care prevention / daily life support" We have "Comprehensive business" 164, "Home medical care / long-term care cooperation promotion business" 165, and "Community care conference" 166, and various measures are implemented in each of these business areas 161, 162, 163, 164, 165, 166. NS. Therefore, indicators for achieving these measures are set for each business area 161, 162, 163, 164, 165, 166, and the solution function 15 has an index menu 151 in which these indicators are registered.

The solution function 15 is realized by a computer, and each business area 161, 162, 163, 164, 165, as described later, is used by using the data 11, 12, 13, 14 centralized in the database 10. The achievement status and implementation status of the measures implemented in 166 are analyzed by aggregating the values of the indicators set for each measure. For this purpose, the aggregation unit 152 and various analysis functions 153 are provided.

FIG. 2 shows the configuration of a computer system that creates the database 10 and executes the above-mentioned community-based comprehensive care business.

This computer system has a client computer (hereinafter, simply referred to as a client) 31 and a server computer (hereinafter, simply referred to as a server) 32 connected to the client 31, and the database 10 uses the server 32 for data. Storage / reading is controlled.

The client 31 is a computer provided in each business department of the local government, and the data acquisition unit 311 is unique to the basic data 11, long-term care insurance data 12, medical insurance data 13, measures, etc. regarding the residents owned by the local government. The data 14 is taken in and provided to the server 32.

The server 32 stores the above-mentioned data 11, 12, 13, and 14 in the original data storage unit 101 of the database 10 as unified data by the database creation unit 321. The unification by the database creation unit 321 encrypts the unique number that identifies the individual or business establishment held in the basic data 11, the long-term care insurance data 12, and the medical insurance data 13, or the unique number that identifies the individual or business establishment. Using the code (for example, a conversion code by hash conversion), the measure-specific data 14 is created and collected by adding a unique number that identifies the business establishment.

As described above, the basic data 11, the long-term care insurance data 12, and the medical insurance data 13 are in individual but standard formats. On the other hand, the measure-specific data 14 has various formats according to each measure, and each time the department in charge implements the measure, an input sheet and a database 10 format (data table) for collecting data. ) Is created. Therefore, the functions of the client 31 and the server 32 create an input sheet and a database 10 format (data table) for collecting the measure-specific data 14, and store the measure-specific data 14 in the database 10. ..

In addition, the server 32 has an index calculation unit 322, an aggregation unit 323, and an analysis unit 324 in order to realize the solution function of the community-based comprehensive care business. The index calculation unit 322 calculates the value of the index registered for each business area in the index menu 151 shown in FIG. 1 by a method described later. The aggregation unit 323 aggregates the index value for each corresponding aggregation unit determined from the aggregation unit menu (subregion unit, user unit, business establishment unit, etc.) registered in the aggregation unit 152 shown in FIG. .. The analysis unit 324 executes each of the analysis functions 153 shown in FIG. Details of these functions, that is, the solution functions of the server 32 will be described later.

Next, the development of indicators used in the business plan of the community-based comprehensive care business will be explained. This index includes a structure index (hereinafter referred to as S index) that includes the implementation status of measures by the implementer of the comprehensive community care business, and a process that includes the service provision status from the implementer of each measure of the comprehensive community care business to the user. An index (hereinafter referred to as P index) and an outcome index (hereinafter referred to as O index) showing improvement results such as the maintenance period of the physical and mental condition of the user will be prepared. The details of these will be described below.

The S index is related to the implementation status of measures and environmental risks by subregion or business establishment that implements each measure for each business area of the comprehensive community care business, and is related to the user (surrounding) business establishment unit or residence. It is defined on a regional basis. For example, as shown in FIG. 9 (details will be described later) explaining the implementation results of the “measures for maintaining / improving the physical and mental condition” in the business domain “preventive benefit / long-term care benefit business” 162, “recognition of the condition maintenance / improvement project”. "Rate" and "Physical and physical condition maintenance / improvement seminar training participation rate" are set. These are set based on the facility-specific data 14 shown in FIG.

The P index is related to the service usage status of the user, medical care for the same person, etc., and the cooperation status of multiple entities, and the service provision data (medical / long-term care receipt data, etc.) provided for each user is the user, It is aggregated in each aggregation unit such as business establishments and regions. The P index in FIG. 9 is an “outpatient care service provision enhancement addition type (0 to 4)” and an “outpatient rehabilitation service provision enhancement addition type (0 to 4)”. These are set based on the office system data shown in FIG. 1, that is, the corresponding data of the basic data 11, the long-term care insurance data 12, and the medical insurance data 13.

The O index is data (certification data, etc.) that represents the effect of implementing measures such as the physical and mental condition of each user and costs, such as the degree of long-term care required, the degree of independence, and the duration of the basic checklist score for each stage. , Region, etc., are aggregated in each aggregation unit. The O index in FIG. 9 is "maintenance / improvement period according to the degree of long-term care required" and "maintenance / improvement period according to the degree of disability independence". These are set based on the corresponding data of the basic data 11, the long-term care insurance data 12, and the medical insurance data 13 shown in FIG.

In addition, when there are multiple items of two choices, applicable or not applicable, as questionnaire items, such as the basic checklist of the activities of daily living needs survey, for each meaningful item group, for example, ADL, IADL ( Instrumental Activity of Daily Living: A method of calculating the duration of each mental and physical condition stage by regarding the value calculated by counting the corresponding items for each item group as the risk stage of the item group. Can be considered. This method makes it possible to evaluate the duration of the mental and physical state stage from the perspective of those groups as well as individual details.

Furthermore, even if the above questionnaire items are not two choices but three or more choices, by allocating them to predetermined risk stages and adding the number of risk applicable items for each of those same item groups, the mind and body are the same as above. A method of calculating the duration for each state stage may also be used.

In addition, even for items that have continuous numerical values, such as the intermediate evaluation item score of the accredited survey item and the sample test value of the medical examination item, those numerical values are divided into predetermined risk stages and code conversion is performed. In the same manner as above, the duration of each stage of mental and physical condition can be calculated. This makes it possible to calculate the duration of diabetes by risk stage, for example, by reconsidering the value of HbA1C, which is a sample test item for medical examination, as a risk stage. Similarly, by converting the values of the test items that should be noted and followed for each target disease into each risk stage, it is possible to calculate the duration for each risk stage for each disease.

In other words, for any mental and physical state item with various data attributes, by converting it into a discrete code value that represents the optimum risk stage for each and defining the risk stage, maintenance and continuation by mental and physical state stage is continued. It is possible to calculate the O index, which is the period.

These indicators are set not only for "measures for maintaining and improving mental and physical condition" in the "preventive benefit / long-term care benefit business" shown in Fig. 9, but also for measures in other business areas. Is omitted.

In the embodiment of the present invention, these S-index, P-index, and O-index are configured as a linked OP-S-index in which the duration of each stage of mental and physical condition, which is an O-index, is set as a key in relation to each other. It is characterized by that. The setting and calculation of the linked O / P / S index will be described later.

These indexes are set and registered for each of a plurality of business areas in the index menu of the index calculation unit 322 that realizes the above-mentioned solution function.

The index calculation unit 322 calculates the value of the index for each business area using the data stored in the original data storage unit 101 of the database 10. The calculated index value is stored in the index storage unit 102 of the database 10.

The aggregation unit 323 aggregates the index values calculated for each index for each index and for each predetermined aggregation unit. The aggregation unit is a subregion or business establishment that implements comprehensive community care, or a user of a comprehensive community care business, and is determined according to the content of the index. In FIG. 9 described above, an example is shown in which the values of each index are calculated for each aggregation unit (here, outpatient nursing care establishments and outpatient rehabilitation establishments).

The analysis unit 324 executes a current state analysis function for each index, which captures the magnitude of the disparity between the aggregation units from the numerical value for each aggregation unit and the deviation from the average value of each aggregation unit. Then, the index having a large disparity is extracted from the disparity between the aggregation units and the magnitude of the deviation from the average, and the aggregation unit having many indexes with a large deviation from the average is extracted to clarify the problem.

Figure 9 summarizes the results of these current situation analysis and problem extraction. The vertical axis indicates the indicators for achieving the above measures, and the horizontal axis indicates the aggregation unit (here, outpatient care establishments and outpatient rehabilitation). Business establishments) are listed. In the cell where the vertical axis and the horizontal axis intersect, the value of the index calculated for each aggregation unit is written for each index. In addition, the average value and the disparity of the values of these indexes are also described in the cells for each index.

In the display of the disparity, for example, the index having a large disparity (= maximum value / minimum value) is displayed in orange on the background of the cell, and the index with a medium disparity is displayed in yellow. In this way, the index to be corrected becomes clear as shown in the display column A.

Further, the value of the index that greatly deviates from the average in a good direction displays the background of the cell in blue, for example. In this way, as shown in the display column B, it can be easily determined that the establishment having a large value of the blue index may be a best practice candidate. On the contrary, the value of the index that greatly deviates from the average in the bad direction displays the cell background in orange. In this way, as shown in the display column C, it can be easily determined that the business establishment having many orange colors may be a candidate for the priority guidance target.

In addition, the analysis unit 324 calculates the correlation coefficient between each index by using the value of each index in order to extract effective measures. Then, effective measures are a combination with a P index having a high correlation coefficient with the O index, a combination with an S index having a high correlation coefficient with the O index, and a combination with an S index having a high correlation coefficient with the P index. Extract as. FIG. 10 shows the analysis results by this measure extraction function, and summarizes the correlation coefficient calculation results for the “mental and physical condition maintenance / improvement measures” in the same “preventive benefit / long-term care benefit business” as in FIG. That is, the indicators for achieving the above measures are listed in the vertical axis direction, and the indicators combined with the indicators listed in the vertical axis direction (the same as the indicators listed in the vertical axis direction) are listed in the horizontal axis direction. Listed. In the cell where the vertical axis and the horizontal axis intersect, the value of the correlation coefficient by the combination of the indexes described on the intersecting vertical axis and the horizontal axis is written. In FIG. 10, the portion surrounded by the frame line D is a case where the maintenance / improvement period for each stage of mental / physical condition tends to increase as the implementation rate of the mental / physical condition maintenance / improvement efforts increases. It can be said that these two indexes have a positive correlation (correlation coefficient approaches +1).

In addition, the analysis unit 324 executes a cost-effectiveness simulation function. In other words, the amount of investment required for effective measures is organized, and the amount of restraint of long-term care benefit costs due to the implementation of each effective measure is calculated. Then, the difference between the investment amount and the restraint amount is calculated, the difference is compared for each effective measure, and the effective measure is prioritized based on the comparison result. This simulation function makes it possible to prioritize cost-effective and effective measures.

Further, the analysis unit 324 executes the index monitoring function and the aggregation unit monitoring function. In the index monitoring function, an index with a large disparity is targeted for monitoring, and the value of the corresponding index is detected for each of a plurality of aggregation units for each monitoring cycle. Then, by calculating the disparity between aggregation units, the transition of the disparity over time is captured. Therefore, it is possible to monitor how the indicators with large disparities change by implementing effective measures.

In the aggregation unit monitoring function, the aggregation unit that has many indicators with a large deviation from the average is targeted for monitoring, and the values of multiple indicators related to this aggregation unit are detected for each monitoring cycle and compared with the target value and the reference value. By doing so, the determination results of each index in chronological order can be obtained in chronological order. Therefore, it is possible to monitor how the index with a large deviation from the average changes by implementing effective measures for each aggregation unit.

These analysis results are stored in the analysis result storage unit 103 of the database 10. Then, it is output to the client 31 in response to a request from the client 31, and is used for analysis by the client 31.

Next, the setting and calculation of the above-mentioned connected OPS index will be described with reference to FIGS. 2, 3, 4, and 8A.

The system configuration of the index calculation unit 322 shown in FIG. 2 includes a setting unit 3220, an O index calculation unit 3221, a P index calculation unit 3222, and an S index calculation unit 3223 shown in FIG.

FIG. 4 shows the selection logic of the record candidates to be aggregated for the O index (mental and physical index: duration for each mental and physical state stage).

Here, the consolidated O / P / S index is a service content for an individual who is the target of aggregation of this O index, using the duration of each stage of the mental and physical condition, which is an O index obtained for each individual, as a key. The P index and the S index such as the aggregation unit measures of the area or business establishment to which this individual belongs are set and calculated.

As the mental and physical condition evaluation items, the long-term care status classification of the certification data, the degree of independence of dementia & disability, the certification survey items 1st to 5th groups, etc. are selected by business area. Further, as a value indicating the stage of the mental and physical condition, the code value of each mental and physical condition evaluation item data described above (in the case of certified data, normal is increased from 1 to an integer value for each deterioration of the mental and physical condition).

The target business areas are maintenance / improvement of physical function of persons requiring long-term care, dementia measures of certified persons, and maintenance / improvement of physical function of persons requiring support. The target business establishments are outpatient care, outpatient rehabilitation, group homes, small-scale multifunctional, special nursing homes, nursing homes, pay nursing homes, etc., and differ depending on the business area. Among the services provided by these business establishments, service codes, etc. centered on additions that contribute to maintaining and improving the physical and mental condition are targeted.

For the target service type, select one that tends to be provided predominantly, excluding visiting services.

For the target service type, select one that tends to provide only the service type excluding the care plan (in some cases, for each target person).

Hereinafter, the flow of processing for setting and calculating the connected O / P / S index will be described for each functional block diagram of FIG. 3 and the following flow numbers corresponding thereto. The following example describes a linked OPS index related to deterrence of aggravation of mental and physical condition in long-term care.

1. 1. Common to indicators 1.1 Determination of basic conditions for index aggregation (processing by setting unit 3220 in Fig. 3)
1.1.1 Determination of business domain: As business domains related to this OPS index, maintenance / improvement of physical function of care recipients, dementia measures of certified persons (maintenance / improvement of cognitive function), support required Select from the maintenance / improvement of the physical function of the person, the maintenance / improvement of the physical function and cognitive function of the non-certified person (included in the new comprehensive business and dementia measures).

1.1.2 Determination of aggregation unit: Determine the type of service establishment (service type) for each business area.

1.1.3 Determining the time of aggregation: Set as appropriate according to the aggregation execution interval (3-month unit, 6-month unit, 1-year unit, etc.) (corresponding points are indicated by the relevant flow number in Fig. 4).

1.1.4 Determining the accreditation renewal check period: Although it is determined based on the aggregation implementation interval and other factors, the basic period is one year, which has the longest accreditation validity period. (In FIG. 4, the flow number is shown and the corresponding range is surrounded by a black frame).

1.1.5 Determining the period of retroactive mental and physical condition: Determine the maximum data retention period. Although the period up to April 2012 is displayed in FIG. 4, it is preferable to start from the second half of 2009 when the certification survey items and the primary judgment logic were last revised.

1.2 Set of data to be analyzed (processing in the data acquisition unit 311 of the client 31 and the database creation unit 321 of the server 32 in FIG. 2)
1.2.1 Basic data set: Basic data is a resident number, personal gender, date of birth, address, and no name is required. Long-term care insurance insured number linked to resident number, National Health Insurance insured number (under 75 years old), late-stage elderly medical insured person number (75 years old or older), etc. Only the necessary items corresponding to the above-mentioned mental and physical state retroactive period are backed up from the basic data 11 on the client 31 side shown in FIG. These basic data are required when dealing with subregions and medical data.

1.2.2 Set of long-term care data: Data corresponding to the above-mentioned mental and physical condition retroactive period is backed up on the client side only for necessary items. Certification data and long-term care receipt data (only for the service type) are eligible.

1.2.3 Medical data set: The data corresponding to the above-mentioned mental and physical condition retroactive period is backed up from the medical insurance data 13 on the side of the client 31 shown in FIG. 2 only for the necessary items. Medical examination data and medical receipt data (only for the disease or treatment) are covered. Medical receipt data is required to detect hospitalization and discharge events, and medical receipt data related to hospitalization, such as DPC (diagnosis procedure combination) data of acute care hospitals, is used.

1.2.4 Set of measures-specific data: The data corresponding to the above-mentioned mental and physical state retroactive period is backed up from the measures-specific data 14 on the client 31 side shown in FIG. 2 only for the necessary items. As the data specific to measures, etc., the data such as the basic checklist (results of the daily living area needs survey) and other user's mental and physical information data, the efforts for subregions / business establishments, and the measure plans / results are targeted. For example, the former is aggregated as an O index related to the mental and physical condition mainly in the non-certified phase for each candidate (individual) to be aggregated (only when the data is collected and managed), and the latter is a subregion, business establishment, etc. It will be aggregated as an S index related to the efforts of.

Each of the above-mentioned picked-up data is stored in the original data storage unit 101 of the database 10 by the database creation unit 321 of the server 32 from the data acquisition unit 311 of the client 31.

Next, the processing of the O index calculation unit 3221 shown in FIG. 3 of the server 32 will be described.

2. Calculation of O index 2.1 Target person extraction 21.1 Certification renewal target person extraction: Of the index calculation target person candidates (target persons 1 to 10) shown in Fig. 4A, the certification renewal check period (flow number 1. Extract the subjects for whom the certification renewal event (black inverted triangle mark) has occurred in the area where 1.4 is marked and surrounded by a black frame). If there is an application date for the change application within the same period, it will be extracted as the target person in the same way. In FIG. 4A, all 10 people have an update event during the same period.

2.1.2 Narrowing down the subjects whose mental and physical condition changes: Before and after the above update or change event, only the subjects whose mental and physical condition stages have changed (improvement direction or deterioration direction) are narrowed down and extracted. In FIG. 4, the candidates marked with a circle in the frame in which the flow number is written and surrounded by a black frame are narrowed down as the target persons. In FIG. 4A, since the stage of the mental and physical condition of the subject candidate 1.2.4.5.8.9 has changed (in the direction of deterioration), the subject was selected as the subject of aggregation.

2.2 Calculation of the same stage duration of the mental and physical condition 2.2.1 Calculation of the same stage duration of the mental and physical condition: The same stage as the latest stage where the stage of the mental and physical condition changes by going back to the certified record within the retroactive period. From the difference between the certification start date of the oldest record and the certification end date of the latest record, the duration of the same stage of mental and physical condition is calculated. In the example of FIG. 4, the duration (value of the O index) of the target candidate 2 is 24 (months), and the flow number is described and described in the corresponding cell in the frame surrounded by the black frame.

In addition, the mental and physical condition stage value within this period (the value of the target candidate 2 is 3), the number of deteriorations (stage rises) within the analysis target data preparation period (the value of the target candidate 2 is 3), and improvement (the value of the target candidate 2 is 3). Calculate the number of steps (step down) (the value of the target candidate 2 is 0). Each of these values is recorded in the corresponding cell. Each of these values is also calculated for each of the other candidate candidates 1.4.5.8.9, and the calculated values are described in the corresponding cells.

The calculation of the duration (value of the O index) will be further described by specifying the users A and B with reference to FIG. 4B.

The example of user A shown in FIG. 4B (a) shows a basic pattern for calculating the duration. In the basic pattern, first, "with renewal" is found during the above-mentioned certification renewal check period (procedure A1). In the example of the figure, since "updated" is found three times during the renewal period, user A finds "changed" in the degree of long-term care required. (Procedure A2). Find the time when there was a step change retroactively from the time of this "changed" (procedure A3). The period is calculated as the continuation period until the time when this step change occurs (procedure A4).

In this way, for user A, a step change from the previous "long-term care required 2" to "long-term care required 3" was found, and the period during which the mental and physical condition continued (the duration of "long-term care required 3") was calculated. Can be identified. In order to perform such a calculation, it is an indispensable condition that the past data about the user A exists.

Next, regarding user B, it is assumed that past care-requiring certification information is missing. In the case of a pattern without previous information like this, there is a possibility that, like Mr. B, the previous step change of the mental and physical condition to be aggregated cannot be found due to the lack of data. There are two possible reasons for the lack of previous information.

B1: When the data itself is missing B2: When the previous data does not exist because of a new application B1: When the data itself is missing as described above, the information source is missing. The duration of mental and physical condition cannot be determined. Therefore, in this case, the contents of the mid-term aggregation are deleted and excluded from the aggregation target. This occurs when the past data is not managed correctly and cannot be stored, or when the previous data of the mental and physical condition to be aggregated cannot be obtained due to the move.

On the other hand, B2: Because it is a new application, if the previous data does not exist, the certification for long-term care will be newly valid, so the period from the effective start date at the time of the new application to the change in mental and physical condition. Can be determined for the duration of the mental and physical condition.

In this way, when the previous data is missing, the accuracy of the duration (value of the O index) can be maintained by performing the above-mentioned exception handling.

Regarding the duration (value of O index), for example, if the fraction is less than 15 days, it is rounded down, and if it is 15 days or more, it is rounded up and converted into monthly units.

When improvement and deterioration are repeated (target candidate 9), and the same stage of mental and physical condition occurs in multiple periods, the duration of the stage of mental and physical condition immediately before the time of aggregation (hereinafter referred to as the target stage) As shown in FIG. 8A, the following eight methods can be considered as a method of obtaining.

In method 1, when the target period is divided and occurs, only the duration immediately before the aggregation time is aggregated as an O index, and the divided periods before that are not added up as an O index (this is "the continuation period for each stage of mental and physical condition"). Called).

In method 2, when the target period occurs separately, the duration immediately before the time of aggregation and the period of the same stage before that are all aggregated as an O index (this is the "duration of mental and physical condition by stage (cumulative 1)). ").

In method 3, when the mental and physical condition is improved, the stage of the mental and physical condition immediately before the improvement is likely to return to the same stage (worse and return) in the future. The period of O index is excluded from the calculation. On the other hand, when the stage of mental and physical condition deteriorates, the period of the same and earlier stages as the target stage immediately before the deterioration is added up as an O index (this is "the duration of each stage of mental and physical condition (cumulative 2)". ) ”).

In the method 4, when the mental and physical condition of the method 3 is improved, the period of the target stage is excluded from the O index calculation target, and the mental and physical condition is repeatedly improved and deteriorated, and the same stage of the mental and physical condition is divided into a plurality of periods. Even if there is the same stage as the target stage before the stage where the degree of long-term care was worse than the target stage, the period is not added up as an O index, and the degree of care required is worse than the target stage. The total of the periods of the same stage as the target stage after the stage is calculated as the maintenance period (O index) of the same stage of mental and physical condition (this is called "stage-specific maintenance period of mental and physical condition").

In the method 5, the period from the time when a different stage shifts to the same stage as the target stage to the time when the target stage first deteriorates is calculated as an O index. In this case, the period of the stage improved from the target stage is also included (this is called "stage-based maintenance / improvement period 1 of mental and physical condition"). That is, the time goes back from the time when it worsens from the target stage, and it is the longest period that does not include the period when the degree of long-term care is worse than the target stage. The time when the shift to is started is the start time of O index calculation.

In method 6, the period from the time when the degree of long-term care required is mild compared to the target stage to the time when it first shifts to the same stage as the target stage to the time when it first deteriorates from this target stage is calculated as an O index. After the improvement of the long-term care level, the deterioration of the long-term care level from the target stage (including the case where the long-term care level shifts to a more severe stage than the target stage without going through the target stage) is the second time or later. Is not included in the calculation (this is called "stage-based maintenance / improvement period 2 of mental and physical condition").

In methods 7 and 8, the long-term care level changed first from the time when the long-term care level changed (either worse or improved) from the previous stage, while the maintenance period of the previous long-term care level was traced back to the past. When is greater than the degree of need for long-term care (that is, when it is improving at that time), it is to be calculated as the maintenance period of the need for long-term care by going back to the past. In the method 7, the maintenance period is calculated as long as the stage equal to or greater than the degree of long-term care required continues, and the calculation period ends at the timing of deterioration. Method 8 calculates the maintenance period as long as the stage equal to or greater than the degree of long-term care is continued, but the calculation period ends even if the stage before that is the same as the degree of long-term care required once it deteriorates.

Hereinafter, a specific calculation method of the continuation period, the maintenance period, and the maintenance / improvement period (hereinafter referred to as “duration period, etc.”) from the above-mentioned methods 1 to 8 will be described with reference to FIG. 8A.

The vertical axis of the upper graph (a) of FIG. 8A is the long-term care-requiring state classification (hereinafter referred to as “the degree of long-term care”) representing the mental and physical condition, and the horizontal axis is the medium- to long-term period. In the example of the target person in FIG. 8A, the degree of long-term care required is 1 in the period A, 2 in the period B, 1 in the period C, 2 in the period D, 1 in the period E, and 1 in the period E. F indicates that support is required 2, period G indicates that support is required 1, period H indicates that support is required 2, period I indicates that care is required 1, and period J indicates that care is required 2. The lower table (b) of FIG. 8A shows the results of calculating the duration of support required 2 as the degree of long-term care required for the subject by each method from method 1 to method 8. Here, the calculation result of the continuation period and the like will be described from the aggregation time 1 to the aggregation time 4, which is the aggregation time when the degree of long-term care is changing from the support required 2.

In the method 1, the duration of the support required 2 is calculated as period B at the time of aggregation 1, period D at the time of aggregation 2, period F at the time of aggregation 3, and period H at the time of aggregation 4.

In the method 2, the duration (cumulative 1) of the support required 2 is the sum of the period B at the aggregation time 1, the total of the period B and the period D at the aggregation time 2, and the total of the period B, the period D, and the period F at the aggregation time 3. At the time of aggregation 4, the total of period B, period D, period F, and period H is calculated, respectively.

In method 3, the duration of support 2 (cumulative 2) is not included in the calculation because the degree of long-term care required improved from support 2 to support 1 at the time of aggregation 1, and the total of period B and period D at time of aggregation 2. At the time of aggregation 3, the degree of need for long-term care improved from 2 requiring support to 1 requiring support, so it is not subject to calculation. At the time of aggregation 4, the total of period B, period D, period F, and period H is calculated.

In method 4, the maintenance period of support 2 is not included in the calculation because the degree of long-term care required has improved from support 2 to support 1 at the time of aggregation 1. Then, since the degree of long-term care required improved from 2 to 1 requiring long-term care, it is not included in the calculation. And the sum of the period H are calculated respectively.

In the method 5, the maintenance / improvement period 1 of the support required 2 is not included in the calculation because the degree of long-term care required is improved from the support required 2 to the support required 1 at the time of aggregation 1, and the transition from the support required 1 to the support required 2 at the time of aggregation 2. The total of period B, period C, and period D from the time when it was done to the time when it deteriorated to the degree of long-term care 1. The total of the period F, the period G, and the period H from the time when the support required 1 is changed to the time required for the long-term care 2 to the time when the degree of long-term care deteriorates to 1 is calculated respectively.

In the method 6, the maintenance / improvement period 2 of the support required 2 is not included in the calculation because the degree of long-term care required is improved from the support required 2 to the support required 1 at the time of aggregation 1, and is required from the time when the support required 2 is reached at the time of aggregation 2. The total of period B, period C, and period D until the time when the long-term care level deteriorates to 1. This is the second time that the degree of long-term care required has deteriorated, so it is not included in the calculation.

In the method 7, the maintenance / deterioration period 1 of the support required 2 is the sum of the period D, the period E, and the period F, paying attention only to the case of the aggregation time point 3. Since the period D is the same stage as the support required 2 in the period F, it is included in the calculation.

In the method 8, the maintenance / deterioration period 2 of the support required 2 is the sum of the period E and the period F, paying attention only to the case of the aggregation time 3 as in the method 7. Period D is not included in period E because it has deteriorated from support 2 to long-term care 1.

Which of these eight methods should be adopted may be arbitrarily determined according to the actual conditions of each community-based comprehensive care business.

2.2.2 Time-series extraction of attribute information, etc. during the same stage continuation period of mental and physical condition: The duration of each of the target candidate 1.2.4.5.8.9 selected as the aggregation target in FIG. ), The following information is extracted in chronological order by month. FIG. 5A shows the target person candidate 2 selected as the aggregation target, and the period in the black frame with the flow number 2.2.2 is the continuation period.

・ Gender code ・ Age classification (early and late) code based on average age within the duration period ・ Name and district code of residential area 1 (administrative district, branch) ・ Name of district 2 (daily living area, town name, etc.) District code ・ Business office number and business office name of service establishment (including abbreviation)
・ Business office number and business office name (including abbreviations) of the home care support office
・ Care manager number of the same care manager, etc. The average age within the above duration shall be calculated from the monthly weighted average.

2.2.3 Same aggregation unit check during the continuation period: The same aggregation unit (subregion or business establishment) is checked within the entire duration of the continuation period. In the case of area analysis, the target person whose residential area code does not change within the duration and the area concerned are the final analysis targets. In the case of establishment analysis, the target person whose establishment number does not change within the continuation period and the establishment concerned are the final analysis targets.

That is, FIG. 5A shows the case where the target person candidate 2 selected as the aggregation target is acceptable and the case where the aggregation target is not possible. For example, if the aggregation unit is a subregion and the code for "Ground 1" does not change on a monthly basis during the target period, the column for the duration for each stage is "continuation" and the column for detection of the aggregation target is "○". (Yes).

On the other hand, if the subregion code changes from "Ground 1" to "Ground 2" in the middle of the target period (black triangle mark), the column of duration for each stage is "Change". The column for detecting the aggregation target is "x" (impossible). This same check is performed in the same manner for other aggregation units.

Furthermore, if hospitalization (black diamond) occurs in the target candidate 2 during the target period, the column for detecting the target of aggregation will be "x" (impossible).

Here, in evaluating the quality of service of the business establishment, which is the aggregation unit, when the cause of the deterioration of the user's long-term care status classification is not due to the business establishment, for example, when an hospitalization / discharge event occurs, the business establishment is informed. Since it corresponds to the deterioration of the long-term care required status classification, it is necessary to extract this and exclude it from the aggregation target.

In other words, the deterioration of the long-term care status classification regardless of the place of business is when hospitalization occurs due to a disease such as stroke, acute myocardial infarction, or fracture due to a fall. If hospitalization occurs, the user's long-term care status classification will naturally worsen. However, this deterioration often does not depend on the quality of service of the business establishment and needs to be excluded from the business establishment evaluation.

Therefore, as shown in FIG. 5B, the hospitalization / discharge event check period is set before and after the renewal date of the certification for long-term care when the long-term care status classification has deteriorated, and the hospitalization / discharge event within that period is searched for. Specifically, look for the addition related to hospitalization and discharge of medical receipts, and if there is no such addition, look for the addition related to hospitalization and discharge of nursing care receipts.

In the case of medical receipts, the additions for hospitalization and discharge are as follows.

Discharge support addition 1
Discharge support addition 2
Joint guidance fee at discharge 1
Joint guidance fee at discharge 2
Long-term care support cooperation guidance fee Pre-discharge visit guidance fee Medical information provision fee In addition, in the case of long-term care receipts, there are the following.

Home support information linkage addition at admission Home support discharge discharge addition Home support emergency conference addition If these additions are calculated, it means that the user has been admitted to or discharged from the hospital due to illness, and the status of long-term care does not depend on the place of business. It turns out that this certification data is not suitable for the evaluation of business establishments because of the suspected deterioration of.

In this way, if there is an addition related to medical receipts or hospitalization / discharge of long-term care receipts before and after certification for long-term care, it is suspected that the classification of long-term care status may have deteriorated due to illness. It is not suitable for, and it is necessary to take measures such as excluding it from the total.

However, users with a high risk of hospitalization are likely to receive some kind of medical service (home medical service), so there is a possibility that the occurrence of hospitalization / discharge events may be due to poor service at the business establishment. be. Therefore, for users with a high risk of hospitalization, it is conceivable to use the reason for hospitalization as an extraction condition (condition for evaluation by business establishments).

As for the check of hospitalization / discharge events, as described above, there are cases where medical receipt data is used and cases where there is no such data and only long-term care receipt data can be used. When using medical receipt data, hospitalization / discharge events are detected in the DPC data of acute care hospitals. If an admission / discharge event occurs during the duration, the duration is not subject to analysis, as described above.

If there is no medical receipt data and only the long-term care receipt data is available, an admission / discharge event will occur if the home care support office service addition item requests information provision addition at admission or joint guidance addition at discharge. Therefore, the duration is not included in the analysis.

2.2.4 Aggregation unit aggregation: The value of the O index of the aggregation target candidate (individual) selected as the aggregation target is aggregated in the aggregation unit (subregion, business establishment, etc.) to which the individual belongs, and the average of the aggregation values. A value is calculated, and the calculated value is used as the value of the O index of the aggregation unit.

2.2.5 Gender / Age Classification Adjustment: Multiply the gender / age classification by a predetermined correction coefficient in order to compare the aggregation units (small areas, business establishments, etc.) that aggregated the aggregation targets obtained as described above. Make adjustments.

In addition to gender and age, four attributes (income (poor and rich), region (topography, etc.), and employment type (in the case of the elderly), which are considered to be factors of health inequalities defined by the WHO (World Health Organization), It is advisable to apply a predetermined correction coefficient to the social participation and purpose of life) and family composition (living alone, living together / not living together, etc.).・ There is a high possibility that unnecessary bias will be added to the evaluation of PS indicators. Therefore, it is preferable to consider adjustment items according to gender and age. These four attributes are the comprehensive community care business. It should be noted that the attributes that should be considered (important) differ for each business area, and the significance between groups for each attribute should be aggregated and tested in advance.

In the above embodiment, the O index is obtained by calculating the period from the deterioration of the physical and mental condition of the user to the deterioration in the next stage, and obtaining the average value (average duration for each stage) for each aggregation unit, and this is O. It was used as an index. However, in reality, as shown in FIG. 8A (a), the mental and physical condition may be improved between the deterioration of the mental and physical condition and the deterioration to the next stage, and the stage may shift to a lower level. Even in such a case, as described in FIG. 8A (b), the period until the next deterioration is basically totaled. It is evaluated that the longer the value of the O index, which represents the period until the change to this deterioration stage (this is called the “duration until deterioration”), the better.

However, this "duration until deterioration" alone does not evaluate the improvement of the physical and mental condition on the way.

Therefore, in this embodiment, as the O index, the "average duration until deterioration" based on the "duration until deterioration" of the individual user unit described above, the "average duration until improvement" and "improvement" described in detail later. "Rate" is set for each, and these three O indexes are used.

FIG. 8B shows the case where the long-term care required state classification (long-term care required degree) representing the physical and mental condition of the individual user shifts (deteriorates) to the next stage without improvement, and is improved during the transition to the next stage. The cases and cases are shown respectively. That is, in the upper graph (a) of FIG. 8B, the long-term care-requiring state classification (long-term care-requiring degree) representing the physical and mental state of the individual user deteriorates from the support-requiring 1 to the support-requiring 2, and the support-requiring 2 to the next. This is the case where the state of support required 2 is maintained until the stage shifts (deteriorates) to the stage of long-term care required 1. On the other hand, in the lower graph (b) of FIG. 8B, the degree of care required shifts (deteriorates) from support required 1 to support required 2, and shifts from this support required 2 to the next stage of care required 1 (deterioration). This is a case where the mental and physical condition is improved from the state of needing support 2 to the stage of needing support 1 before (deterioration).

In this embodiment, in the case of FIG. 8B (a), as in the above-described embodiment, the period from the start of the stage of support 2 to the deterioration to the stage of long-term care 1 is measured, and this duration is measured. , The value of the O index of "duration until deterioration" at the stage of support required 2.

On the other hand, in the case of FIG. 8B (b), the duration is measured from the time when the stage of support required 2 starts until the stage is improved to the stage of support required 1, and this continuation period is referred to as "the stage of support required 2". It is the value of the O index of "duration until improvement". After the improvement to support required 1, it is processed as an O index at the stage of support required 1. For example, when it deteriorates to the stage of support required 2 again, it is treated as "duration until deterioration" in support required 1.

Here, as described above, the longer the O index of the “duration until deterioration” is, the better it is evaluated. On the other hand, it is evaluated that the shorter the O index T2 of the “duration until improvement” is, the better. The reason will be described below with reference to FIG. 8C.

FIG. 8C (a) shows a case where the mental and physical condition deteriorates, and the stage of the long-term care 1 generally deteriorates to the long-term care 2 at the time point a1 and further deteriorates to the long-term care 3 at the time point a2 as shown by the broken line. It shall be exacerbated.

On the other hand, various services at the stage of long-term care 1 were successful, and the duration of the stage of long-term care 1 was extended to time point a2 as shown by the solid arrow, and at this point a2, long-term care 2 was reached. When it deteriorates, the benefit cost can be reduced by the area surrounded by the broken line as compared with the case where the duration at the stage of requiring long-term care 1 shown by the broken line is short. That is, the longer the duration of the mental and physical condition until deterioration (the longer), the more the benefit cost can be reduced, which is evaluated as preferable.

On the other hand, FIG. 8C (b) shows a case where the mental and physical condition is improved, and the stage of long-term care 2 generally continues until the time point b2 as shown by the broken line.

On the other hand, various services at the stage of requiring long-term care 2 have been successful, and as shown by the solid line arrow, the stage may be improved to the stage of requiring long-term care 1 at the time b1 after a short duration. In this case, the time up to the time point b1 indicated by the solid arrow is the duration until improvement in the stage of requiring long-term care 2. As described above, when the duration until the improvement up to the time point b1 is shorter than the time until the general time point b2, the benefit cost can be reduced by the area surrounded by the broken line. In other words, the shorter the duration of the mental and physical condition until improvement, the more the benefit cost can be reduced, which is evaluated as preferable.

The value of the O index of each individual unit is aggregated for each aggregation unit such as business establishments, and the average value for each aggregation unit, for example, "average duration until deterioration" and "average duration until improvement" for each establishment. Are obtained as O indexes.

As the O index for each aggregation unit, in addition to the "average duration until deterioration" and "average duration until improvement", as described above, the "improvement rate" (or "deterioration rate") for each stage is provided. .. For example, 90% of all users requiring support 2 at a certain business establishment deteriorated to requiring long-term care 1 as shown in FIG. 8B (a), and 10% of all users requiring support 2 in FIG. 8B (b). As shown, when the improvement is made to support required 1, the O index of the improvement rate at the stage of support required 2 of the business establishment is 10% (deterioration rate is 90%). That is, in a predetermined aggregation unit such as a business establishment, the ratio of the users whose mental and physical condition has changed in the improvement direction to all the users whose mental and physical condition has changed in either the deterioration direction or the improvement direction is the improvement rate in this aggregation unit. Calculated.

This improvement rate is calculated for each stage of the degree of long-term care required. Since the O index of this improvement rate is an improvement rate, it is evaluated that the larger the value, the better.

As described above, in this embodiment, three types of O index are used: "average duration until deterioration", "average duration until improvement", and "improvement rate" (or "deterioration rate"). For these three types of O indexes, in the processing block 2.2.1 in FIG. 3, the duration of the individual user unit is calculated by the method described above, and in the processing block 2.2.4, the aggregation unit of the business establishment or the like is calculated. Aggregate separately and calculate each.

The O index in the above embodiment uses the duration until the mental and physical condition changes, that is, the "duration until deterioration" and the "duration until improvement". It is not possible to grasp whether the degree has changed (degree of deterioration or degree of improvement). Therefore, in the following examples, the "deterioration gradient of the mental and physical condition" and the "improvement gradient of the mental and physical condition" indicating the degree of change in the mental and physical condition are obtained from the above-mentioned duration and the magnitude of the change, and these values are calculated. Used as an O index. Hereinafter, how to obtain this O index will be described with reference to FIG. 8D.

FIG. 8D (a) shows the case of the user A whose mental and physical condition deteriorates at the business establishment X, which is the aggregation unit. After the stage of the long-term care required 3 continues for 24 months (2 years) from the time point a1 to the time point a2, the user A has deteriorated in the mental and physical condition by two stages to the long-term care required 5. In this case, the deterioration gradient of the user A is a slope from the time point a1 at which the stage of requiring long-term care 3 starts to the time point a2 when the mental and physical condition deteriorates by two stages.

FIG. 8D (b) shows the case of the user B whose mental and physical condition deteriorates at the business establishment X. After the stage of the need for long-term care 3 continues for 12 months (1 year) from the time point b1 to the time point b2, the user B is in a state of mental and physical condition worsening by one stage to the long-term care required 4. In this case, the deterioration gradient of the user B is a slope from the time point b1 when the stage of the long-term care 3 starts to the time point b2 when the mental and physical condition deteriorates by one stage.

FIG. 8D (c) shows the case of the user C whose mental and physical condition is improved at the business establishment X. User C has improved his / her mental and physical condition by one stage to the need for long-term care 2 after the stage of the need for long-term care 3 has continued for 36 months (3 years) from the time point c1 to the time point c2. In this case, the improvement gradient of the user C is an inclination gradient from the time c1 when the stage of the need for long-term care 3 starts to the time c2 when the mental and physical condition is improved by one stage.

FIG. 8D (d) shows the case of the user D whose mental and physical condition is improved at the business establishment X. After the stage of the need for long-term care 3 continues for 12 months (1 year) from the time point d1 to the time point d2, the user D improves his / her mental and physical condition by two stages to the time point d1. In this case, the improvement gradient of the user D is an inclination gradient from the time d1 when the stage of the long-term care 3 starts to the time d2 when the mental and physical condition is improved by two stages.

Next, an example of the calculation method of the deterioration gradient and the improvement gradient of these mental and physical conditions will be described. These gradients are obtained from the duration of the same mental and physical condition at the same stage and the amount of change at the end of the gradient. Here, the duration is on a yearly basis. The amount of change is expressed as a minus in the case of deterioration and a plus in the case of improvement using the number of steps.

In the case of user A, the duration of the long-term care 3 is 2 years, and the amount of change at the terminal portion thereof is in two stages in the deterioration direction, so the deterioration gradient is -2/2 = -1.

In the case of user B, the duration of the long-term care 3 is one year, and the amount of change at the terminal portion thereof is one step in the deterioration direction, so the deterioration gradient is -1/1 = -1.

Assuming that there are two users A and B whose mental and physical condition deteriorates at the business establishment X, the average mental and physical condition deterioration gradient of the long-term care required 3 at the business establishment X is (-2/2 + -1 / 1) / 2 = -1. It becomes.

In the case of user C, the duration of the long-term care 3 is 3 years, and the amount of change at the terminal portion thereof is one step in the improvement direction, so the improvement gradient is 1/3.

In the case of user D, the duration of the long-term care 3 is one year, and the amount of change at the terminal portion thereof is in two stages in the improvement direction, so the improvement gradient is 2/1.

Assuming that there are two users C and D whose mental and physical condition has improved at the business establishment X, the average mental and physical condition improvement gradient of the long-term care required 3 at the business establishment X is (1/3 + 2/1) / 2 = 1.17.

The values of the average mental and physical condition deterioration gradient and the average mental and physical condition improvement gradient of the long-term care required 3 obtained in this way are the values of the O index of the establishment X. This O index is calculated for each stage of mental and physical condition.

This O index is also calculated in the processing block 2.2.1 in FIG. 3 using the duration of each stage of the mental and physical condition of each user and the amount of change in the stage, and in the processing block 22.4. , Business establishments, etc. are aggregated and calculated for each aggregation unit.

In the above embodiment, the O index is defined for each stage of the mental and physical condition items, but since there are usually a plurality of mental and physical condition items and the O index for each stage is calculated for each of the plurality of mental and physical condition items, the number of O indexes is large. It is expected to be enormous.

If the number of indicators is larger than necessary, there may be too much information, and it may be difficult to grasp the trends and issues of each nursing care facility based on this information. Therefore, when the number of indicators is large, it is necessary to aggregate them into an appropriate amount. At that time, since the ratio of the total number of people in each stage of the mental and physical condition items is different for each stage, it is not possible to make a comparison between business establishments by their simple average. Therefore, a weighted average based on the aggregated number of people ratio at each stage of the same mental and physical condition item is used as an O index that aggregates all stages of the mental and physical condition item.

Hereinafter, how to obtain this O index will be described.

When the index value for each stage of the mental and physical condition items to be aggregated is V1, V2, V3 ... Vn, and the total number of people (number of records) for each stage index is N1, N2, N3 ... Nn, the index after aggregation The value U is calculated using the following formula.

U = (V1 x N1 + V2 x N2 + V3 x N3 + ... + Vn x Nn)
÷ (N1 + N2 + N3 + ... + Nn)
For example, when the three indexes A, B, and C are aggregated, when the index values of the indexes A, B, and C are 10, 25, and 40, respectively, and the number of records to be aggregated is 3, 2, 1, respectively, the index after aggregation is used. The value is 20 as shown in the following formula.

U = (10 × 3 + 25 × 2 + 40 × 1) / (3 + 2 + 1) = 20
Regarding the degree of aggregation, not only each stage of the same mental and physical condition items, but also related mental and physical condition items such as the first group to the fifth group of the accredited survey items are used as the O index aggregated in the same manner as described above. It is also possible.

That is, the O index calculation unit has a plurality of items of the mental and physical state, and when the O index value of each stage is calculated for each of the plurality of items of the mental and physical state, the number of index values becomes large. Therefore, for each item of mental and physical condition, the weighted average value obtained by summarizing all the stages by using the index value at each stage of the user belonging to the aggregation unit and the number of records of the index value at each stage is obtained. Then, this weighted average value is used as an O index of an item of a certain mental and physical condition in the aggregation unit.

As a result, when the number of indicators is large, it is possible to aggregate the contents of the indicators and the number of indicators that can be understood by the nursing care establishments in the field and lead to concrete practices for business improvement.

As a specific effect, when an insurer such as a local government provides information to a long-term care establishment or a user of a long-term care service, it is possible to realize an information particle size that is easy for them to understand. In addition, it will be easier for insurers such as local governments to grasp the strengths and weaknesses of business establishments, and expansion to incentive performance indicators will be smooth.

The above aggregation example can be similarly applied to the P index and the S index. In addition, all business areas of the community-based comprehensive care business (long-term care severity deterrence business (mental and physical condition improvement / maintenance continuation business), new comprehensive business (long-term care prevention / life support), home medical care / nursing care cooperation, acute exacerbation hospitalization / discharge medical care / nursing care It can be applied to cooperation, disease prevention, and prevention of severity).

Next, the processing of the P index calculation unit 3222 will be described.

3. 3. Calculation of P index value 3.1 Set of service items related to P index: Set items of service contents that contribute to maintenance and improvement of mental and physical condition as P index. As the P index, as listed in the vertical direction of FIG. 6, whether or not a certain service type is provided / month, the number of times a certain service type is provided / month, the total number of units provided for a certain service type / month, etc. Is.

3.2 Time-series extraction of service content: Service content for each month within each duration of the above-mentioned O index (the period in the black frame with the flow number in FIG. 6), that is, the duration for each mental and physical condition stage. (Achievements) are extracted in chronological order for each candidate (individual) to be aggregated. For example, looking at whether or not a certain service type is provided / month, "1" is set in each month during the above-mentioned continuation period, which means that the service was implemented in each month.

3.3 Calculation of P index within the continuation period (before adjustment): Calculate the monthly weighted average of the service content (actual) of each month within the continuation period according to the mental and physical condition stage, and P index for each candidate (individual) to be aggregated. As the value of, it is set in the corresponding cell in the black frame with the flow number.

3.4 Aggregation unit Aggregation: The values of each P index calculated in the above-mentioned flow number 3.3 are aggregated in the aggregation unit (subregion, business establishment, etc.) to which this target candidate 2 belongs, and the average of the aggregated values. A value is calculated, and the calculated value is used as the value of the P index of the aggregation unit.

3.5 Gender / age classification adjustment: In order to compare aggregation units (subregions, business establishments, etc.), the values of each P index calculated in flow numbers 3 and 4 are corrected according to the gender / age classification. Adjust by multiplying by a coefficient.

Here, as described above, the P index quantitatively represents what kind of service was provided to the user during the staged duration of the mental and physical condition representing the O index. As described above, the O index includes a duration until the stage of mental and physical condition deteriorates and a duration until improvement, and a deterioration gradient and an improvement gradient based on these durations.

Therefore, in obtaining the P index, the O index is divided into a group of users showing a tendency of deterioration of mental and physical condition (hereinafter, deterioration group) and a group showing a tendency of improvement of mental and physical condition (hereinafter, improvement group). , It is preferable to obtain the P index for each deterioration group and improvement group.

In this way, when the P index is obtained for each of the deterioration group and the improvement group, it is possible to know what kind of service was provided to the user during the continuation period until the deterioration and during the continuation period until the improvement. Therefore, it is possible to analyze and extract services that are effective in extending the duration until deterioration and services that are effective in shortening the duration until improvement.

Next, the processing of the S index calculation unit 3223 shown in FIG. 3 of the server 32 will be described.

4. Calculation of S index value 4, 1 Set of action items such as business establishments related to S index: Set the action items of business establishments that contribute to maintenance and improvement of mental and physical condition as S index. As the S index, as listed in the vertical direction of FIG. 7, the recognition rate of the mental and physical condition maintenance / improvement PJ, the participation rate of the mental / physical condition maintenance / improvement PJ, etc. Time-series extraction: Efforts (actual results) of business establishments, etc. in each month within each duration of the above-mentioned O index (the period in the black frame with the flow number in FIG. 7) Is extracted in chronological order for each candidate (individual) to be aggregated.

4.3 Calculation of S index within the continuation period (before adjustment): Calculate the monthly weighted average of the business establishment efforts (actual results) of each month within the continuation period by mental and physical condition stage, and S for each candidate (individual) to be aggregated. Find the value of the index.

If the efforts of business establishments, etc. are limited to gender, age group, or region, only users who meet those conditions will be subject to S index aggregation.

4.4 Aggregation unit Aggregation: The values of each S index calculated in the above-mentioned flow number 4.3 are aggregated in the aggregation unit (subregion, business establishment, etc.) to which this target candidate 2 belongs, and the average of the aggregated values. A value is calculated, and the calculated value is used as the value of the S index of the aggregation unit.

4.5 Gender / age classification adjustment: In order to compare aggregation units (subregions, business establishments, etc.), the values of each S index calculated in flow numbers 4 and 4 are corrected according to the gender / age classification. Adjust by multiplying by a coefficient.

Here, as described above, the S index quantitatively represents what kind of measures and efforts were taken for each aggregation unit during the staged duration of the mental and physical condition representing the O index. As described above, the O index includes a duration until the stage of mental and physical condition deteriorates and a duration until improvement, and a deterioration gradient and an improvement gradient based on these durations.

Therefore, regarding the S index, in obtaining this S index, the O index is divided into a deterioration group of users showing a tendency of deterioration of mental and physical condition and an improvement group showing a tendency of improvement of mental and physical condition. It is preferable to obtain the S index for each improvement group.

In this way, if the S index is obtained for each of the deterioration group and the improvement group, it is possible to know what kind of measures and efforts were implemented during the continuation period until deterioration and during the continuation period until improvement. It is possible to analyze and extract measures and initiatives that are effective in extending the duration until deterioration, and measures and initiatives that are effective in shortening the duration until improvement.

In this way, using the period of each stage of mental and physical condition, which is the O index obtained for each individual, as a key, the P index, which is the service content for the individual who is the target of this O index, the area and business to which this individual belongs. Since the S index related to the aggregation unit measures of places, etc. is calculated within the duration, the consolidated OP / S index is used, so the value of each index can be calculated with high accuracy, and the community-based comprehensive care business. The PDCA cycle of the system can be accurately implemented.

As described above, each data that has been managed individually will be centralized in a database, and this database will be used, and the PDCA cycle of the community-based comprehensive care business will be implemented using the consolidated OPS index. Therefore, objective and quantitative data analysis enables accurate problem extraction, concretely shows the effectiveness of measures, and enables accurate implementation of community-based comprehensive care projects using highly accurate indicators. Become.

Here, FIG. 11 shows how the duration of each stage of mental and physical condition such as the degree of long-term care required and ADL is extended by improving the quality of the long-term care service in the measures to prevent the aggravation of the mental and physical condition. The vertical axis of FIG. 11 is the long-term care-requiring state classification (mental and physical state, hereinafter referred to as the long-term care-requiring degree), and the horizontal axis is the medium- to long-term period (unit: months).

In FIG. 11, the number of stairs in the figure expresses that the stage of the degree of long-term care is increased, and the height of the stairs at each degree of long-term care (vertical axis) is the average of the degree of long-term care of a certain user. The monthly amount of long-term care benefits (yen) is shown. The area of the shaded area in the long-term care required degree is the average monthly long-term care benefit cost (yen) x period (months) in the long-term care required degree, and the total long-term care benefit cost (yen) in the long-term care required degree of the user. Is shown.

When the above graph is created for the entire long-term care certification period of the user, the integral in the time axis direction (total area of the shaded area) represents the lifetime long-term care benefit cost (yen) of the user.

If the content of the long-term care service matches the physical and mental condition of the user who receives the long-term care service, the mental and physical condition is maintained and improved, the deterioration of the mental and physical condition is delayed, and the time when the level of long-term care is raised is delayed and the same. The duration of the degree of long-term care required will be extended. The graph expressing the situation is 101.

On the other hand, if the content of the long-term care service does not match the physical and mental condition of the user who receives the long-term care service, the physical and mental condition deteriorates quickly, the stage of the degree of need for long-term care rises earlier, and the same degree of need for long-term care is maintained. The period of time is shortened. The graph expressing the situation is 103. Further, 102 is a graph expressing an intermediate state between 101 and 103. As can be seen from FIG. 11, the lifetime long-term care benefit cost for the user is the smallest in the case of 101, the largest in the case of 103, and the middle in the case of 102 among the three patterns.

For this reason, it is important for the community-based comprehensive care business to extend the duration of the physical and mental condition of the user at the same stage for a long time or to generate an improvement pattern. By using the above-mentioned linked OPS index, it is possible to accurately extract issues by objective and quantitative data analysis, and the effectiveness of the measures is concretely shown, so the same stage of mental and physical condition. The extension of the duration can be effectively realized.

Next, another method related to the same aggregation unit check during the duration of 22.3 in the above-mentioned example of suppressing the aggravation of the mental and physical condition will be described.

2.2.3 In the same aggregation unit check during the continuation period in the above-mentioned embodiment, the target user (user) whose aggregation unit (subregion or business establishment) has changed within the entire duration of the continuation period is the final. Not included in the analysis. However, even if the aggregation unit (subregion or business establishment) changes temporarily for some reason during the continuation period, it will not be subject to final analysis.

Therefore, in the following example, if the aggregation unit (establishment A), which is the implementer of the measures for the comprehensive community care business, is involved in the target person for most of the duration, for some reason on the way. Even if the company temporarily changes to another business establishment B, if the ratio of business establishment A involved in the target person during the continuation period is equal to or higher than the preset threshold value, this target person and the business establishment are considered as the final analysis target. do. It will be described in detail below.

In FIG. 12, No. In the "duration" column of row 1, it is shown that the mental and physical condition same stage continuation period extracted by the calculation logic of the mental and physical state same stage continuation period of 2.2.1 above was extracted as 12 months. The stage of the mental and physical condition at this time is "needs care 1", and the state of "needs care 1" is maintained from April 2015 of "continuation start" to February 2016 of "continuation end". Represents.

No. In the second line, "Total number of units by month for each specified service type and establishment", No. 3 to No. The lines up to 10 represent the information related to the total number of units per month for each business establishment of the predetermined service type. The prescribed service type represents the prescribed service type to be aggregated, and is outpatient (outpatient care, outpatient rehabilitation, etc.), residential (group home, pay nursing home, small-scale multifunctional, etc.), facility (special). Nursing homes, elderly health, etc.), home-visit service combinations (based on care plans), etc. are equivalent. A business establishment is a business establishment that provides a predetermined service type. The total number of units per month is the total number of units billed by the business establishment that provided the prescribed service type.

That is, No. The establishment A of the third bank has a total number of units of 108,000 in the "duration" in the predetermined service type. This is the total number of units billed monthly from April 2015 for "continuation start" to February 2016 for "continuation end". Similarly, No. In line 4, regarding establishment B, No. In line 5, the total number of units (10000 and 16000) in the "duration period" and the monthly bill from April 2015 for "continuation start" to February 2016 for "continuation end" for business establishment C, respectively. The number of units is shown.

No. Line 6 shows the number of medical receipt billing establishments in the same month, and in each month from April 2015 of "continuation start" to February 2016 of "continuation end", monthly billing for a specific service type Shows the number of establishments. In the example shown in the figure, there were two receipt requesting establishments in October 2015 and January 2016, and one receipt requesting establishment in the other months, and these establishments provided a specific service type. It represents that.

No. Line 7 billed 500 units each month for the total number of other service types (6000 units) billed from April 2015 for "continuation start" to February 2016 for "continuation end". It represents that. The other service type is a service type other than the predetermined service type. In this case, the place of business is not specified, and it is the total number of billing units for other service types provided to individual beneficiaries.

No. Line 8 represents the number of billing units for each month of the total number of units (140000 units) for all service types billed from April 2015 for "continuation start" to February 2016 for "continuation end". ing. All service types are a combination of a predetermined service type and other service types. It is the total number of billing units for all types of services provided to individual beneficiaries without specifying the place of business.

The purpose of extracting the total number of units of all the service types described above is to calculate the ratio with the number of units of the predetermined service type and determine whether or not the predetermined service type is implemented at a certain ratio or more. When the billing of the predetermined service type is less than a certain ratio, it is judged that the predetermined service type has a small contribution to the continuation of the same stage of mental and physical condition, and the period of continuation of the same stage of mental and physical condition from the viewpoint of the predetermined service type is excluded from the aggregation.

No. The ratio of the total number of units of the prescribed service type establishments in the 9th line is No. 1 of each month in "continuation start" to "continuation end". It represents the ratio of the total number of units of the predetermined service types requested by the establishment A, the establishment B, and the establishment C to the total number of units of all the service types of 8. For example, in October 2015, the total number of units of the predetermined service type is the sum of the number of units of the establishment A of 10000 and the number of units of the establishment C of 2000. The total number of units for all service types in October 2015 is No. Since it is 12500 in 8 rows, the ratio of the total number of units of the predetermined service type to the total number of units of all service types is 96%.

Similarly, from April 2015 to September 2015, the ratio of the total number of units of the prescribed service type by establishment B or A is 95%, from November 2015 to December 2015 and from February 2016 to 2016. In March, the ratio of the total number of units of the prescribed service type by establishments A or C was 96%, and in October 2015, the ratio of the total number of units of the prescribed service type by establishments A and C was 97%. The ratio of the total number of units of a predetermined service type to the total number of units of all service types becomes clear.

No. Line 10 shows the comparison result between the lower limit threshold value (assumed to be 90%) of the total number of units ratio of the predetermined service type establishment and the lower limit threshold value by month by a flag. That is, when the ratio of the total number of units in each month is 90% or more of the lower limit threshold value, it is indicated by the flag ●. In the example of the figure, the flag is ● in all months between April 2015 and March 2016, and the ratio of the total number of units of the specified service type to the total number of units of all service types is all. It shows that it is 90% or more in a month. As a result, No. Proceed to the process after 11. If it is less than 90%, it will not be counted for the same stage duration of mental and physical condition in the predetermined service type. The above-mentioned lower limit threshold value can be defined according to the aggregation.

No. In the flag of the establishment with the maximum total number of units in 11 lines, No. 12 to No. It is shown that the lines up to 13 represent the information related to the maximum number of business establishment flags. The business establishment with the maximum total number of units is the business establishment with the largest number of billing units among the business establishments that provide the predetermined service type to be aggregated.

No. The maximum total number of units in the 12 lines is the prescribed service requested by establishment A, establishment B, and establishment C in each month from April 2015 of "continuation start" to February 2016 of "continuation end". It represents the total number of business establishments with the largest number of billing units among the total number of units of each type. For example, in the total number of units in October 2015, establishment A has 10,000 units, establishment B has 0, and establishment C has 2000 units, so 10000 units of establishment A is the largest number of billing units. Therefore, 10000 units of establishment A are extracted as the maximum total number of units in October 2015.

The purpose of extracting the maximum total number of units is to determine whether or not one of the establishments claiming the predetermined service type occupies a certain ratio or more with respect to all the establishments. .. If the number of billing units of one extracted business establishment falls below a certain ratio, it is judged that the one business establishment has a low contribution to the continuation of the same mental and physical condition, and the continuation of the same mental and physical condition from the viewpoint of the predetermined service type. Excludes the period from being aggregated.

No. The ratio of the total number of units of the maximum total unit establishments of 13 banks is the establishment A, establishment B, and establishments in each month from April 2015 of "continuation start" to February 2016 of "continuation end". Of the total number of units of the predetermined service type requested by C, No. It represents the ratio of the maximum total number of units in 12. For example, the total value of the total number of units of the predetermined service type in October 2015 is 12000 units, which is the total value of 10000 units of the establishment A and 2000 units of the establishment C. On the other hand, the maximum total number of units in the same month is 10,000 units of establishment A. Therefore, the ratio of the maximum total number of business establishments A is 83%.

The same is required for other months. That is, April 2015-September 2015, November 2015-December 2015, February 2016-March 2016 100%, October 2015 83%, January 2016 86% Is the ratio of the maximum total number of units of the corresponding business establishment to the total number of units of the predetermined service type requested by the business establishment A, the business establishment B, and the business establishment C. The lower threshold of this total unit number ratio is No. As shown by 14, it was set to 80%. The value can be defined according to the aggregation.

No. 15, No. 16, No. The maximum number of business establishment flags for each of A, B, and C in line 17 is the No. 1 of each month. The total number of units ratio shown in No. 13 is No. A flag ● indicates that the establishment occupies the ratio of the lower limit threshold value of 80% or more shown in No. 14 and charges the largest number of units among the predetermined service types. In the example of the figure, establishment A has a flag ● from May to February 2016, establishment B has a flag ● in April 2015, and establishment C has a flag ● in March 2016. ..

No. The monthly main offices of 18 banks are No. 15-No. In 17, the establishments marked with ● are represented by the names of establishment A, establishment B, and establishment C.

No. The number of months of the main business establishment within the continuation period of 19 banks is the number following No. 20 to No. It shows the information related to the number of months of the main business establishment within the continuation period shown in the "duration" column of the row up to 25. No. 20 to No. The "duration" column of row 22 shows the number of months of the main business establishments of each business establishment A, B, and C. That is, No. In the 20th line, the number of months of the main business establishment of business establishment A is 10 months. In the 21st line, the number of months of the main business establishment of business establishment B is 1 month. Line 22 shows the number of months of the main business establishment of business establishment C, which is one month. The largest value among these is No. It is shown in line 23 as the maximum number of months, indicating that it is 10 months.

The purpose of extracting the maximum number of months is to determine whether or not the number of months of the establishment that charges the most for the predetermined service type occupies a certain ratio or more with respect to the continuation of the same mental and physical condition as the lower limit threshold value described later. This is because it is judged by the comparison of. If this judgment result is less than a certain ratio, it is judged that the establishment that makes the most requests for the predetermined service type has a low contribution to the continuation of the same stage of mental and physical condition, and the duration of the same stage of mental and physical condition from the viewpoint of the predetermined service type. Exclude from aggregation.

No. The maximum number of months ratio of 24 rows is No. The maximum number of months shown in line 23: 10 months is the ratio of the same mental and physical condition duration: 12 months (April 2015 to February 2016), and 83% is calculated. This maximum monthly ratio of 83% is No. It is compared with the lower threshold of 80% shown in 25. In this case, since the lower limit threshold value of 80% is exceeded, the establishment A is No. It will be the main business establishment for the duration shown in 26.

By the above logic, that is, the combination of the operations in each stage of FIG. 12, the main business establishment that contributes to the mental and physical condition same stage duration period of 12 months in the predetermined service type is assumed to be business establishment A, and the total is performed. In this way, the subject represented by FIG. 12 is subject to aggregation for the same mental and physical state continuation period, and the aggregation unit is No. It becomes the establishment A shown in 26.

In the above, the O index is explained as the duration of the same stage of mental and physical condition. In relation to this O index, the following index (method) for evaluating this can also be considered.

Method 1: The average benefit cost of an individual within the same period of the same mental and physical condition is totaled for each business establishment.

Method 2: The duration per unit amount of the individual obtained by dividing the above duration by the average benefit cost is totaled for each business establishment.

Method 1 aggregates the average benefit costs invested to realize the same duration, and the lower the average benefit cost, the higher the cost performance of the service. On the other hand, the larger the average benefit cost (the larger the amount of input service and the more generous the service), the longer the duration of the same stage of mental and physical condition may be. Therefore, it can be said that the business establishments that realize the same duration with a smaller average benefit cost are superior in service content and business establishment efforts while such a correlation is assumed.

Method 2 is an index showing how much the investment of 10,000 yen contributes to the extension of the duration. It can be said that the larger this value is, the better the service content and the efforts of the business establishment are.

The index values according to the methods 1 and 2 have the above-mentioned calculation function of the index calculation unit 322 of the server 32 by using the long-term care receipt data stored in the original data storage unit 101 of the database 10 shown in FIG. It can be calculated by setting.

In addition, as another example of the condition for narrowing down the data to be aggregated in the O index aggregation (function of 2.1.2 in Fig. 3), the accreditation data whose accreditation survey content may be doubtful (there is a problem with the survey quality) is used. It may be excluded from the total. For example, the accredited survey item data, which is the main item data of the long-term care certification data, is surveyed and collected by accredited investigators on the order of hundreds of thousands nationwide. The survey is conducted based on the survey criteria of the certified surveyor manual issued by the Ministry of Health, Labor and Welfare, but in reality, the skill levels of certified surveyors (mostly care managers) vary.

For this reason, there is a tendency to guide (enclose) to the nursing care service establishment of the same corporation as the investigative institution to which the investigator belongs (mostly home care support establishments). In addition, there may be problems with the accuracy and quality of the accredited survey data, such as the possibility of arbitrarily selecting a severe survey option that results in a higher primary judgment result. That is, there is a risk that the degree of independence of dementia, the degree of independence of disability, and the duration of each stage of the accredited survey items of each group included in such accredited survey data will be inaccurate.

In order to overcome these problems, the conditions for quantitatively checking the reliability of accreditation survey data will be described below.

[Check conditions]
・ The length of the career as a certified investigator ・ The primary judgment is higher than the secondary judgment (numerical grasp of the tendency to choose a severe option in the certification survey)
・ The number of items in common with the attending physician's opinion (short-term memory, cognitive ability, communication ability, eating behavior, dementia independence), and the certified survey items are selected more severely than the items in the attending physician's opinion.・ The number of items that differ in the selection of 5 items (short-term memory, cognitive ability, communication ability, eating behavior, dementia independence) that are common to the doctor's opinion is more than the specified number ・ Certified research institutes and home care support offices Is the same business establishment, and the nursing care service business establishment, etc. are the same corporation. Countermeasures in certain cases, the fourth above corresponds to a discrepancy with the written opinion (the attending physician), and the fifth above corresponds to risk countermeasures by enclosing the same corporation.

In this way, quantitative conditions that raise doubts in the certification survey are set, and the long-term care certification data stored in the original data storage unit 101 of the database 10 is used to exclude the subjects who meet these conditions from the aggregation target. do. As a result, only the higher quality certification survey item data is aggregated, and the accuracy of the O index can be improved.

In the above explanation, the judgment of the certification data, which may have doubtful contents of the certification survey (there is a problem with the survey quality), was made by focusing on the skills of the certified surveyor, but the survey results are based on the certification survey items themselves. It is also possible that there are factors that cause variations. That is, among the certified survey items, there are items in which almost the same survey results can be obtained even if the surveyors are different for the same survey target, and items in which the survey results vary greatly depending on the surveyor. Such items can be grasped by analyzing the survey results.

In other words, the selection status of the certification survey item options in the certification data is aggregated by gender and age group, and items with large variations in the selection status are items for which the investigator's judgment varies and are considered to be unreliable. For example, FIG. 27 tabulates the ratio of the number of selected cases by the research institution for each option of the certified survey item of the long-term care certification data for the degree of long-term care 1. Next, the standard deviation of the selection number ratio aggregated for each research institution is calculated for each option, and the degree of variation in the selection number ratio is clarified. FIG. 27 explains this check, and in the example of the figure, a circle is added to the items of “turning over: not possible” and “getting up: not possible” as options for the accredited survey items with large variation. That is, in the results of the certification survey for long-term care 1, the items "turning over: not possible" and "getting up: not possible" were extracted as options for the certification survey items with large variations.

The variation check of this certification survey is performed not only in the above-mentioned long-term care 1 but also in each stage from the support 1 to the long-term care 5, and items with large variations are extracted according to the degree of long-term care. Therefore, the degree of variation according to the degree of long-term care is tabulated for each option of the certification survey item of the long-term care certification data. FIG. 28 illustrates the variation check according to the degree of long-term care required. Items with a large degree of variation according to the degree of long-term care are displayed as 1, and the total for each item is displayed in the total number column. An item having a large degree of variation is an item having a large standard deviation shown in FIG. 27 and marked with a circle.

Next, the number of items with a large variation according to the degree of long-term care is counted, and the item with a large number of cases is regarded as an item with low reliability. In the example, the threshold value of the number of cases is set to 3, and ○ is added to the items above the threshold value. The degree of variability can be tabulated not only by the degree of long-term care required, but also by gender and age (age classification).

In this way, items whose variation in the survey results is equal to or greater than the set value are extracted and selected, and the O index for such items is excluded or processed so as to be used as a reference.

In addition, as mentioned above, the accreditation survey is conducted based on the survey criteria of the accredited investigator text issued by the Ministry of Health, Labor and Welfare. Can be done. That is, as shown in FIG. 29, the contents of the text 2901 are organized into the certification survey item-specific information 2902. Then, the reliability is judged by paying attention to the following points.

・ Some number of choices ・ Some selection criteria ・ Complex selection criteria ・ Differences in evaluation axes Generally, the smaller the number of choices and selection criteria and the simpler the selection criteria, the more accurate survey results can be obtained. Therefore, judgment criteria are set in advance for each item, such as the number of options described above, the number of selection criteria, the complexity of the selection criteria, and the evaluation axis, and the reliability is ranked for each item (A, B, C). ). Then, the O index for items with a low reliability rank is excluded or processed so as to be a reference level.

In these cases as well, quantitative conditions that raise doubts in the certification survey will be set, and the data stored in the original data storage unit 101 of the database 10 will be used to select the target persons who meet these conditions from the aggregation target. It is possible to take measures such as excluding, and only higher quality certification survey item data is aggregated, and the accuracy of the O index can be improved.

Further, it is preferable to select items by combining the item selection method described with reference to FIGS. 27 and 28 and the item selection method described with reference to FIG. 29.

Regarding the data of the certification survey results, there are some items that have a great influence on the stage change of the mental and physical condition, so care must be taken when handling them. That is, out of the 65 items in the certification survey, items that greatly contribute to determining the degree of long-term care are selected as priority items for prevention of aggravation. Therefore, the calculation unit of the O index shown in FIG. 3 is provided with a function of selecting a priority item for preventing severity as shown in FIG. 30 in 3221. This severity prevention priority item selection function is realized by the time extraction unit 3001 for each item according to the tree shape model diagram and the severity prevention item selection unit 3002.

The time extraction unit 3001 for each item of the tree shape model diagram is based on the tree shape model diagram (estimation method of reference time for long-term care certification, etc .: a part of which is shown in Fig. 31) of the revised version of the text 2009 of the members of the long-term care certification examination committee. The maximum time, minimum time, and difference time are extracted for each item of the shape model diagram.

FIG. 31 shows the item of "under the hood" in "meal intake". In the dendrogram model diagram, it is "possible" when it branches to the left in the figure, and "cannot" when it branches to the right in the figure.

From FIG. 31, the maximum value and the minimum value under "Enshita" are extracted, and the difference value is obtained as follows.

Maximum value: 71.4
Minimum value: 1.1
Difference: 70.3
The severity prevention item selection unit 3002 greatly contributes to the severity prevention as shown in FIG. 32 based on the item-specific maximum time, minimum time, and difference time extracted by the item-specific time extraction unit 3001 for each tree shape model diagram. Select the item to be used. Here, the contribution is large = the difference is large, and the contribution is small = the difference is small. This means that the item with a large difference has a large change in the care-requiring state category of the primary judgment selected depending on the selection time, and the item with a small difference has a small change in the care-requiring state classification of the primary judgment depending on the selection time. Determine the contribution level based on the difference and select the item. In FIG. 31, “meal intake” and “enshita”, which have high contribution levels, are selected.

When there are a plurality of the same items (item of "living function" in FIG. 31), the one with the larger maximum value and the one with the smaller minimum value are adopted. In the example of FIG. 32, the largest maximum value and the smallest minimum value of the two "living functions" are described in the "living function (synthesis)" line. Regarding the items of the intermediate evaluation points, one intermediate evaluation point item was determined by a plurality of mental and physical condition items, and one item could not be selected, so it was excluded.

In this way, items that greatly contribute to determining the degree of long-term care required have been selected as priority items for prevention of aggravation. Therefore, if the care services are prioritized by paying attention to these items, it will be effective in preventing the aggravation. ..

In the above examples, the duration of maintenance of the mental and physical condition of the service user is adopted as the outcome index, but from another point of view, the method of using the duration of the care burden of the caregiver of the service user as the outcome index. Can be considered.

That is, about the trial survey in the latest information on long-term care insurance issued by the Ministry of Health, Labor and Welfare No. 555 (June 10, 2016) "Survey and research project on how to grasp the ideal way of long-term care services including the viewpoint of long-term care turnover" (information provision) ) ”As described in the future plan, at the time of the certification survey, the investigator may hear the general condition of the certified person and the physical and mental condition of the certified person, as well as the general condition of the caregiver and items related to the burden of long-term care (nursing care). Items related to the long-term care burden of the person may also be added as items of certification data).

Specifically, the following items are assumed. Below, the numbers are those of the above material.

(1) Actual use of support and services A-Q8 Whether or not to use services not covered by long-term care insurance A-Q12 Whether or not to use home-visit medical care A-Q13 Whether or not to use long-term care insurance services A-Q14 Whether or not to use long-term care insurance services Reasons for non-use B-Q6 Whether or not there is a counselor for the caregiver Certification-4 Usage status of long-term care insurance services (2) Needs for support / services A-Q9 Support / services required to continue living at home B-Q 3 Support from the workplace that seems to be effective (3) Attributes of the main caregiver A-Q2 Frequency of caregiving by the caregiver A-Q3 Caregiver (relationship with the person)
A-Q4 Gender of the caregiver A-Q5 Age of the caregiver A-Q6 Care provided by the caregiver (4) Employment status of the main caregiver B-Q1 Work style of the caregiver B-Q 2 System / mechanism of balance support used by caregivers A-Q7 Whether or not there are any employees who have left their jobs in their families / relatives (5) Continuation of living at home A-Q10 Examination status of facilities, etc. B-Q5 Nursing care Long-term care that makes people feel uneasy (6) Continued employment of caregivers

B-Q4 Awareness regarding whether or not caregivers can continue to work There are various options for the above survey items, such as single selection, multiple selection, and numerical values. These are quantified in stages, and their scores are determined. It is possible to define the long-term care turnover risk stage by accumulating for the item group (assuming multiple groups consisting of multiple viewpoints). On the other hand, in the same way as the above-mentioned outcome index related to the physical and mental condition of the user, the duration until the risk level worsens or improves is defined as the outcome index related to long-term care turnover. It is possible to evaluate the care burden of the caregiver and the risk of long-term care turnover.

Furthermore, by defining process indicators (individual measures, etc.) and structure indicators (workplace of the main caregiver, efforts on the care manager side, etc.) that affect the reduction of the risk of long-term care turnover, the same evaluation is possible. become.

Next, long-term care prevention that extends the period until new certification of long-term care in long-term care will be described. FIG. 13A is an image diagram showing the difference in the period until new certification due to the difference in the efforts of the small area and the elderly themselves. That is, FIG. 13A shows a state in which the newly certified age is pushed back (health to extension of the non-certified period) due to the enhancement of subregional measures in care prevention and life support, daily care prevention efforts for the elderly, and improvement of purpose of life. Is shown.

The vertical axis of FIG. 13A is the degree of long-term care required (mental and physical condition), and the horizontal axis is the medium- to long-term period (unit: months). The starting point on the horizontal axis is a preset time, for example, the birth month of the user at the age of 65, and the non-certification elapsed period from that point is calculated as the O index.

This will be specifically described with reference to FIG. 13B. Since there is no certification data for the period until the new certification of the long-term care status category, as shown in FIG. 13B (a), the starting point is the user's 65th birthday, and the period until the new certification is independent. It is added as simulated certification data representing. Then, the period up to the time when the new certification is made (the time when the long-term care certification data is newly generated) is calculated as the duration of independence (O index) in the long-term care status category.

For the mental and physical condition items of the certified data system other than the long-term care status category, as shown in FIG. 13B (b), the duration of independence from the user's 65th birthday is calculated as an O index. This independence continuation period is a continuation period from the time when the mental and physical condition deteriorates from independence to the next stage, and may overlap with the long-term care-requiring state category of support 1 or more.

The number of stairs in FIG. 13A expresses that the stage of the degree of long-term care is increased, and the height of the stairs at each degree of long-term care (vertical axis) is the average long-term care benefit cost for a certain user at the degree of long-term care. The monthly amount (yen) is shown. The area of the shaded area in the long-term care required degree is the average monthly long-term care benefit cost (yen) x period (months) in the long-term care required degree, and the total long-term care benefit cost (yen) in the long-term care required degree of the user. Is shown.

When the above graph is created for the entire long-term care certification period of the user, the integral in the time axis direction (total area of the shaded area) represents the lifetime long-term care benefit cost (yen) of the user.

If subregional measures are enhanced, or if the elderly's daily care prevention efforts and purpose of life are improved, the duration of their health condition will be longer and the time when support (long-term care required) is required will be delayed ( The non-certification period will be extended), and the total long-term care certification period of the certified person will be shorter than the average period of all insurers, for example. The graph expressing the situation is 121.

On the other hand, if the subregional measures do not work and the elderly do not have or are inadequate in their daily care prevention efforts and purpose of life, the duration of their health condition is short and there is a time when support (long-term care required) is required. It arrives early (the non-certification period is shortened), and the total long-term care certification period of the certified person is longer than the average period of all the above insurers. The graph expressing the situation is 123. Moreover, the graph which expressed the state in between these is 122. As can be seen from FIG. 13, the lifetime long-term care benefit cost for the user is the smallest in the case of 121, the largest in the case of 123, and the middle in the case of 122 among the three patterns.

In this case, the non-certified elapsed period from the age of 65 of the user becomes the O index, and the amount of various services received by the user from a subregion during the non-certified elapsed period becomes the P index, which is also small in the non-certified elapsed period. Various measures for users in the community (for example, provision of contact salons, etc.) are the S indicators, and the comprehensive community care business is implemented by the combined OP / S indicators by combining these.

Next, a specific example of the OPS index in the above-mentioned business related to long-term care prevention and early detection is compared with the above-mentioned OPS index in the business related to deterrence of aggravation of mental and physical condition in long-term care. I will explain.

First, we will explain the aggregation conditions for deterring the aggravation of the mental and physical condition of long-term care.

[Aggregation target area]
Each long-term care insurance service type: Single service type for outpatient / residential / facility type, combined service type for visit / short-term admission type [Target person]
Certified person requiring long-term care (support 1 and 2, long-term care 1 to 5)
[Aggregation unit]
Nursing care service establishments (visiting, outpatient, residential, facility)
[Data source]
・ Long-term care certification data (common throughout Japan)
・ Nursing care receipt data (common throughout Japan)
・ Data on business establishment efforts (questionnaire data common to business establishments by each service type unique to insurers)
・ Data such as business affiliation ・ Data such as user attributes by business establishment (questionnaire data common to business establishments by each service type unique to insurers)
Here, the business affiliation data is data such as the scale of the business establishment, the type of corporation, etc., and a part of the data is also included in the nursing care business establishment ledger. In other words, it is the basic attributes of business establishments that are difficult to control by the efforts of business establishments.

Data such as user attributes by business establishment is not included in the data requiring nursing care such as lifestyle, life philosophy, home environment, work environment, neighborhood environment and economic situation of the user, and it is difficult to control by user efforts. This is data that aggregates the basic attributes of various users for each business establishment.

As the data such as each of these attributes, the code that leads to good or bad is defined in consideration of the magnitude relationship.

As a result, there is a possibility that it will be useful for cause analysis between business establishments that have the same services and business establishment efforts but have different effects.

It should be noted that the data of one long-term care insurer is not enough to thoroughly evaluate the long-term care service establishment. The reason is that there are many cases where the location of the business is outside the insured, especially in the case of visiting or outpatient services, and there is a possibility that services are being provided to the insured of other adjacent insurers. There is. In that case, it is necessary to aggregate the long-term care insurance data that integrates multiple insurers and perform an aggregate analysis by business establishment. However, the logic for aggregating various indicators at that time is the same as for one insurer.

Specific examples of the OPS index in suppressing the aggravation of the mental and physical condition of long-term care based on the above-mentioned aggregation conditions and the like will be described below.

O index (when improvement of mental and physical condition is not considered, only "average duration until deterioration" is used as O index, but when improvement is considered, "average duration until deterioration" and "up to improvement" are used as O index. The following example, which uses the "average duration" and "improvement rate", is when improvement is considered. Further, as another O index when improvement is considered, the "average deterioration gradient" and the "average deterioration gradient" described in FIG. 8C are used. There is an "average improvement gradient", which is also shown).

In the following description, the symbol × does not mean multiplication, but represents a combination of items before and after it.

[By service type x by business establishment x by mental and physical condition item stage x average duration until deterioration (month)
By service type x establishment x mental and physical condition item stage x average duration until improvement (month)
By service type x establishment x mental and physical condition item stage x improvement rate (%)
Service type x business establishment x mental and physical condition item stage x average deterioration gradient Service type x business establishment x mental and physical condition item stage x average improvement gradient]
・ By service type × By business establishment × By long-term care status classification By stage × Average duration until deterioration (month)
・ By service type × By business establishment × By long-term care status classification By stage × Average duration until improvement (month)
・ By service type x by business establishment x by stage of long-term care required x improvement rate (%)
・ Service type x Business establishment x Nursing care status classification Stage x Average deterioration gradient ・ Service type x Business establishment x Nursing care status classification Stage x Average improvement gradient ・ Service type x Business establishment x Dementia independence By degree x average duration until deterioration (month)
・ By service type × by business establishment × by dementia independence stage × average duration until improvement (month)
・ By service type x establishment x dementia independence stage x improvement rate (%)
・ Service type × Business establishment × Dementia independence stage × Average deterioration gradient ・ Service type × Business establishment × Dementia independence stage × Average improvement gradient ・ Service type × Business establishment × Disability independence Stage x Average duration until deterioration (month)
・ By service type × By business establishment × By disability independence stage × Average duration until improvement (month)
・ By service type × by business establishment × by disability independence stage × improvement rate (%)
・ By service type × By establishment × By disability independence stage × Average deterioration gradient ・ By service type × By establishment × By disability independence stage × Average improvement gradient ・ By service type × By establishment × By certification survey item stage × Average duration until deterioration (month)
・ By service type × By business establishment × By certification survey item stage × Average duration until improvement (month)
・ By service type × by business establishment × by accredited survey item stage × improvement rate (%)
・ By service type × by establishment × certification survey item stage × average deterioration gradient ・ By service type × establishment × certification survey item stage × average improvement gradient Of the above O indicators, the average duration until deterioration The average deterioration gradient is indexed for the deterioration group. In addition, the average duration until improvement, the improvement rate, and the average improvement gradient shall be indexed for the improvement group.

That is, regarding the O index, users showing a tendency of deterioration of mental and physical condition are grouped as a deterioration group, and users showing a tendency of improvement of mental and physical condition are grouped as an improvement group, and each index described above is classified into a deterioration group and an improvement group. Aggregate the values.

The average duration, which is these index values, is adjusted by gender and age group. As the age classification, the early-stage elderly classification and the late-stage elderly classification will be adopted. In addition, the judgment timing of the age classification shall be judged from the age in the certification month when each stage of each mental and physical condition was first reached.

Here, it is effective to use the duration of the mental and physical condition that constitutes the O index for evaluation by service type such as home care service and outpatient care service, and evaluation of business establishments that implement these services. It is clear from the explanation up to. As described above, there are many items of mental and physical condition, and when the O index for each stage is calculated for each of these many items of mental and physical condition, the number of O indexes becomes enormous. Therefore, it was considered to aggregate the number of indicators into an appropriate amount as described above. That is, it was considered that the weighted average based on the aggregated number of people ratio at each stage of the same mental and physical condition items was used as the O index that aggregated the mental and physical condition items.

Further examination of this aggregation reveals that the O index is defined as [service type x business establishment x mental and physical condition stage x average duration (month)], in addition to the average duration, which is the index value. In addition, it has three types of elements as attributes: service type, business establishment, and mental and physical condition stage. Each of these elements can be individually narrowed down (selected), subdivided, aggregated, or otherwise processed. That is, in addition to the above-mentioned aggregation of mental and physical condition items, processing processing including aggregation is possible for three types of elements including this mental and physical condition item, by service type, by business establishment, and by mental and physical condition stage. ..

For example, when looking at the items of mental and physical condition, there are items such as "walking" and "turning over", but when these are aggregated into a group of physical functions and their average duration is summarized as an O index, there is a certain business. The place can make a general evaluation such as strong or weak physical care, or strong or weak dementia care. In other words, it is effective as a clue to know the specialty field of the business establishment.

In addition, when looking at how home care and home-visit services are related to service types, if multiple items representing these services are aggregated and their average duration is summarized as an O index, the situation for each service type. Can be grasped.

Looking at the business establishments, if there are multiple business establishments of one long-term care provider and multiple business establishments by different long-term care providers in the local government, multiple business establishments are aggregated for each of these long-term care providers. If the average duration of is used as the O index, a comprehensive evaluation for each long-term care provider becomes possible.

Furthermore, by displaying the index value or the value obtained by calculation, which is the result of data processing such as aggregation of each of these elements, in a graph by a widely used chart representation method, each evaluation described above can be visually displayed. It is possible to make a judgment and easily grasp it. In other words, it is possible to realize the generation of information that can be effective knowledge for the community-based comprehensive care business, and it is possible to express it with charts such as histograms, bar graphs, and line graphs, which are widely used chart representation methods. Hereinafter, these specific means will be described.

FIG. 16 shows information that can be useful for the community-based comprehensive care business using the outcome index (O index) 102O among the indexes held in the database 10 (shown in FIG. 2) of the community-based comprehensive care business system. It shows the flow of the system for generation and generation of charts. In this embodiment, it is assumed that the processing is carried out for general purposes, and information related to the outcome index 102O, such as regional information to which the business address belongs and group information at the mental and physical condition stage, is supplementary information. It was set to 102a so that it could be used as needed.

The outcome index chart representation unit 1601 is realized by three-step processing by the outcome index element processing unit 1602, the element two-dimensional development unit 1603, and the chart generation unit 1604.

FIG. 17 shows the internal processing of the outcome index element processing unit 1602 shown in FIG. As shown in the figure, the outcome index 102O has three elements, that is, the service type, the business establishment number, and the mental and physical condition stage, in addition to the average duration which is the index value. In addition, the value of the denominator of division when the average duration is calculated is held as the average value calculation parameter, and can be used for processing the outcome index.

Processing each of the three elements of the outcome index 102O can be considered as a process of setting conditions to narrow down information, aggregating a plurality of data, and decomposing the data into finer units. At this time, supplementary information 102a, which is a general-purpose data source, can be used. In addition, there are innumerable processing methods, and the processing method is not limited to the selection, aggregation, and decomposition described above, but here, a case where aggregation is performed as a processing method will be described.

The processing of each of the three elements described above is performed by the service type processing unit 1701, the business establishment number processing unit 1702, and the mental and physical condition processing unit 1703.

In FIG. 17, as an example, the service type processed version outcome index 1704 in which the service type numbers of the outcome index 102O are combined by the tens digit by the service type processing unit 1701, and the outcome index 102O by the business office number processing unit 1702. The business office number processed version outcome index 1705, which combines the business establishment numbers in the first place, and the mental and physical condition processed version outcome index 1706, which combines the mental and physical condition stages of the outcome index 102O into mental and physical condition units by the mental and physical condition processing unit 1703. , Is shown.

Looking at the above-mentioned service type processed version outcome index 1704, the first line is the "degree of long-term care required 1" at the business establishment with the business establishment number No_01 when the service type numbers are combined (aggregated) in the tens place. It shows that the average duration (index value) for "deterioration" is 19.3.

Looking at the business number processed version outcome index 1705, the first line is the "needs care required" for service type 15 when the last digit of the business number is 0 for a group of business establishments. It shows that the average duration (index value) of "degree 1 deterioration" is 18.4.

Furthermore, looking at the mental and physical condition processed version outcome index 1706, the first line shows the business establishment number of the business establishment number No. 01 in the service type 15 when the mental and physical condition stages are combined (aggregated) by "deterioration of the degree of long-term care". It shows that the average duration (index value) in is 21.2.

That is, the three elements of the outcome index are aggregated within a predetermined range, and the average value of the aggregated index values is used as the aggregated index value.

In FIG. 17 described above, in the outcome index element processing unit 1602, the service type processing unit 1701, the business establishment number processing unit 1702, and the mental and physical condition processing unit 1703 perform processing for each of the three elements, and the result is the service type processing version. It is created as an outcome index 1704, a business establishment number processed version outcome index 1705, and a mental and physical condition processed version outcome index 1706, respectively. It is also possible to apply the processing unit 1703 in order. Since the processing elements of these service type processing unit 1701, business establishment number processing unit 1702, and mental and physical condition processing unit 1703 are different, they can be processed independently. Then, the result of these processing processes is obtained as an all-element processed version outcome index 1801. The order in which the processes are applied can be changed.

FIG. 19 shows the internal processing of the element two-dimensional expansion unit 1603 shown in FIG. As an example, the mental and physical condition processed version outcome index 1706 obtained in FIG. 17 is developed. The element two-dimensional expansion unit 1603 has an element arrangement designation unit 1901, a calculation element aggregation unit 1902, and the result of these processes is obtained as a two-dimensional expansion version outcome index 1903.

The element arrangement designation unit 1901 determines an element to be fixed, an element to be arranged on the vertical axis, and an element to be arranged on the horizontal axis in order to expand the three elements of the outcome index in two dimensions.

In the example of FIG. 19, based on the mental and physical condition processed version outcome index 1706, the elements to be fixed are set as the designated index table 1904 and the service type = 15. Further, the elements arranged on the vertical axis were defined as the mental and physical states processed as the vertical axis designated element 1905. As the element to be arranged on the horizontal axis, the establishment number was arranged as the horizontal axis designation element 1906. An average duration (index value) is set in the cell where these vertical axis elements and horizontal axis elements intersect.

Next, the calculation element totaling unit 1902 calculates calculated values such as standard deviation, average value, and median that can be calculated by arranging them two-dimensionally. The calculation results are arranged on the vertical axis calculation element 1907 and the horizontal axis calculation element 1908. In this way, the two-dimensional table of the arrangement determined by the element arrangement designation unit 1901 and the value calculated by the calculation element aggregation unit 1902 is the two-dimensional expansion version outcome index 1903, and the element two-dimensional expansion unit 1603. Is generated by.

FIG. 20 shows a chart utilization location of the value of the two-dimensional expansion version outcome index 1903 generated by the element two-dimensional expansion unit 1603 of FIG. By expanding the outcome index in two dimensions, the numerical values of the elements arranged on the vertical axis or the horizontal axis become the values to be compared. Here, when evaluating the average duration (index value) included in the outcome index itself, the vertical axis element outcome index 2001 and the horizontal axis element outcome index 2002, which are surrounded by thick lines, are used. When evaluating the calculated value calculated from the outcome index, the vertical axis element calculated value index 2003 and the horizontal axis element calculated value index 2004 are utilized. That is, the standard deviation, the average value, the median value, etc. calculated from each index value arranged in two dimensions are the vertical axis element calculation value index 2003, and the number of upper index values, the number of lower index values, etc. are the horizontal axis element calculation. Value index 2004.

The values of the vertical axis element outcome index 2001, the horizontal axis element outcome index 2002, the vertical axis element calculated value index 2003, and the horizontal axis element calculated value index 2004 are utilized for chart generation by the chart generator 1604 shown in FIG. NS.

FIG. 21 shows a list 2101 explaining the effective chart and the outcome index element processing method, and is used in the community-based comprehensive care business generated by the chart generator 1604 using the above-mentioned two-dimensional expanded version of the outcome index 1903. The outline of the charts that are considered to be effective and the processing method of the three types of outcome indicators are shown. That is, as described with reference to FIG. 19, the three types of elements are divided into fixed elements, elements arranged on the vertical axis, and elements arranged on the horizontal axis, and used as a source for generating a chart. -Various charts shown in FIG. 26 can be created. The outline of the effective charts shown in this list 2101 is a part of innumerable valid charts, and the present invention is not limited to the contents described.

22 to 26 show No. 2101 shown in Table 2101 of the outcome index element processing method. 1-No. The chart of 5 is generated by tentative numerical values. As shown in the list 2101, FIG. 22 represents the average duration of the specific mental and physical states of the specific service as a histogram (frequency distribution table). In FIG. 23, the standard deviations of the mental and physical states calculated from the values of each business establishment are divided into groups of mental and physical states and represented by a bar graph. FIG. 24 is represented by a bar graph in which the number of indicators having a long / short average duration of each business establishment is ranked. Figure 25 summarizes the mental and physical state stages into groups such as the physical system and the mental system to express the average gift period. FIG. 26 shows the average duration by service.

P index: [By service type x By business establishment x By mental and physical condition stage x By service item x Average amount of service provided]
・ By service type × By business establishment × By status of need for nursing care By stage Service item × Average service provision amount ・ By service type × By business establishment × Dementia degree of independence By stage Service item × Average service provision amount ・ By service type × By establishment × By disability independence stage by service item × By average service provision amount / service type × By establishment × By certification survey item By stage Service item × Average service provision amount / service type × By establishment × Business User attributes, etc. by location (index for analyzing the cause of differences between establishments)
Here, as described above, the P index is obtained by calculating the average amount of services provided by the user within the average duration of each mental and physical condition stage calculated by the O index. When the improvement of the mental and physical condition is considered, as the O index, in addition to the duration until the stage of the mental and physical condition deteriorates and the duration until the improvement as described above, the deterioration gradient and the improvement gradient based on these durations are used. Sometimes.

In such a case, in obtaining the above-mentioned P index, the O index is divided into a deterioration group of users showing a deterioration tendency of the mental and physical condition and an improvement group showing an improvement tendency of the mental and physical condition, and the deterioration group and improvement. Find the P index for each group.

The average amount of service provided corresponds to the average value during the period such as the total number of units, the number of times, and the number of days.

In addition, as an example of service items, services provided to each user such as additional items by service type that contribute to maintenance and improvement of mental and physical condition and combinations of basic services related to business affiliation and service amount are used.

Furthermore, the above-mentioned [by service type x business establishment x business establishment-specific user attributes, etc.] is based on the business establishment side, such as the user's lifestyle, life philosophy, home environment, work environment, neighborhood environment, and economic conditions. This is data that aggregates the attributes of users who are difficult to control for each business establishment. These are not included in the long-term care certification data.

These may be useful for cause analysis between business establishments that provide the same service but have different effects.

S index: [By service type x by business establishment x by stage of mental and physical condition x by business establishment efforts x average implementation amount]
・ Service type × Business establishment × Nursing care required status classification Business establishment effort × Average implementation amount ・ Service type × Business establishment × Dementia independence stage Business establishment effort × Average implementation amount ・ Service type × By establishment × By disability independence stage By establishment effort × Average implementation amount / service type × By establishment × Certification survey item By stage By establishment effort × Average implementation amount / service type × By establishment × Business Affiliation, etc.
Here, as described above, the S index is calculated as the average implementation amount of what kind of measures and efforts the business operator, which is the aggregation unit, has implemented within the average duration for each mental and physical condition calculated by the O index. .. When not only the deterioration of the mental and physical condition but also the improvement is considered, as the O index, as described above, the duration until the stage of the mental and physical condition deteriorates and the duration until the improvement, and the deterioration gradient and the improvement gradient based on these durations. Is used.

In such a case, in obtaining the above-mentioned S index, the O index is divided into a deterioration group of users showing a deterioration tendency of the mental and physical condition and an improvement group showing an improvement tendency of the mental and physical condition, and the deterioration group and improvement are performed. Find the S index for each group.

Of the five S indicators mentioned above, the top four are the average values of each service establishment within the average duration of each service establishment within the average duration of the mental and physical condition calculated by the O index. And calculate the average implementation amount by totaling it at the business establishment.

On the other hand, the fifth S index is data that is partly included in the nursing care business register, such as the size of the business establishment and the type of corporation, and is based on the attributes of the business establishment that are difficult to control by the efforts of the business establishment. be. It is also an index for analyzing the causes of differences between business establishments.

As a result, it may be useful for cause analysis between business establishments that have the same business establishment efforts (S index) but different effects (O index).

In addition, the average implementation amount of business establishment efforts corresponds to the average value during the relevant period, such as the training participation rate and the number of implementations of various initiatives.

Examples of business establishment efforts include "initiatives to increase the number of qualified personnel", "initiatives to improve the staff system", and "initiatives to work closely with related parties inside and outside the business establishment" to contribute to maintaining and improving the physical and mental condition. , "Efforts for skill improvement training, etc.", "Efforts related to guidance for users and their families", "Efforts to enhance service items", and "Efforts to improve the user's home (whereabouts)" are envisioned. It should be noted that these efforts are closely related to the calculation requirements of each service addition item, etc. in the P index.

Next, the aggregation conditions related to long-term care prevention and life support will be explained in comparison with the above-mentioned deterrence of the mental and physical condition of long-term care.

[Aggregation target area]
Each care prevention / life support project: Examples of the above projects include facility infrastructure development projects such as local community salons, life support coordinators and volunteer training projects, user-oriented projects such as care prevention classes, and various information provision projects. It is necessary to quantitatively carry out the PDCA cycle such as issues, effective measures, and monitoring for each.

[Target person]
Non-certified persons (healthy persons, frail persons, non-certified persons using the new comprehensive business, etc.)
[Aggregation unit]
In the case of small areas / government ordinances within the care insurer such as the daily living area, administrative districts, daily living areas, junior high school districts, elementary school districts, residents' associations, etc. Areas, junior high school districts, elementary school districts, residents'associations, etc. ・ In the case of wide-area associations, branches, constituent municipalities, daily living areas, junior high school districts, elementary school districts, residents' associations, etc. [Data source]
・ Data on subregional efforts (data common to subregions unique to insurers, data owned by the Regional Comprehensive Support Center, etc.)
・ User effort data (insurer-specific user-specific daily living area needs survey data, new comprehensive business checklist, other questionnaire data, etc.)
・ Data such as subregional attributes (insurer-specific user-specific daily living area needs survey data, new comprehensive business checklist, other questionnaire data, etc.)
・ Data such as user attributes by subregion (user-specific daily living area needs survey data unique to insurers, new comprehensive business checklist, other questionnaire data, etc.)
Data such as subregional attributes are data such as population, population density, elderly ratio, major industrial population ratio, household composition, and geographical environment of subregions, and are basic attributes of subregions that are difficult to control by subregional efforts. And so on. Data such as user attributes by subregion is data that aggregates the basic attributes of users, etc., which are difficult to control by user efforts, for each subregion, such as the user's home environment, work environment, neighborhood environment, and economic conditions. Is. For each attribute data, the code that leads to good or bad is defined in consideration of the magnitude relationship.

As a result, subregional efforts and subregional user efforts may be useful for cause analysis between subregions that have the same but different effects.

Specific examples of OPS indicators in long-term care prevention / life support based on the above-mentioned aggregation conditions will be described.

O index; [By business x By subregion x By mental and physical condition stage x Average independence duration (month)]
・ By business × By subregion × Category requiring long-term care × Average independence (non-certified) Duration (month)
・ By business × By subregion × Dementia independence degree × Average independence duration (month)
・ By business × by subregion × degree of disability independence × average independence duration (month)
・ By business × By subregion × By accredited survey item × Average independence duration (month)
・ By business × By subregion × Daily living area Needs survey By group Score by stage × Average duration (month)
Here, the calculation start month of the independence duration is the birth month of 65 years old regardless of the elderly.

In addition, in the case of the long-term care status category, the duration until the new certification month matches the non-certification period. For other certified data-based mental and physical condition items, there is a possibility that they will be independent at the time of new certification, and the duration will be until the first certification month when they move to the first stage, which is the second worst after the independence stage. In addition, even during the non-certification period, each of the above mental and physical condition items shall be regarded as "independent".

The daily living area needs survey group score stage is the data obtained by counting up the number of applicable items of each group's survey items in the basic checklist, etc., according to the staged threshold value, and is the average for each stage. The calculation logic of the duration is the same as that of deterring the aggravation of the mental and physical condition.

Of the five O indicators mentioned above, the top four adjust the average duration by gender, but due to the nature of the indicators, adjustment by age group is not necessary. On the other hand, for the fifth O index, the average duration is adjusted by gender and age group. As the age classification, the early-stage elderly classification and the late-stage elderly classification are adopted. In addition, the judgment timing of the age classification shall be judged from the age in the certification month when each stage of each mental and physical condition was first reached.

As for the continuation check method in the average duration of the small area where each user lives, the same method as the establishment continuation check method described with reference to FIG. 12 shall be applied. )
P index: [By business x by subregion x by mental and physical condition stage by user's efforts x average implementation amount]
・ By business × by subregion × by user's efforts for “non-certified” status of care required × average implementation amount ・ By business × by subregion × degree of dementia independence “independence” by user's efforts × average implementation amount ・By business x subregion x disability independence "independence" by user effort x average implementation amount / business x subregion x certification survey item "independence" by user effort x average implementation amount / business x small Area x Daily Living Area Needs Survey Group Scores By Stage x Average Implementation / Business x Subregion x Subregion User Attributes, etc. (Indicator for Cause Analysis of Differences between Subregions)
The P index is the user's efforts (nursing care) within the average independence duration of the mental and physical condition items of the certified data system calculated by the O index, the daily living area needs survey, the new comprehensive business checklist, and other mental and physical condition stages. Calculate and calculate the average implementation amount of prevention, use of life support services, participation in hobbies, work, volunteers, etc.).

The average implementation amount corresponds to the average value during the period such as the number of times and days of use of various services and the participation rate in various activities.

Examples of user efforts include participation in exercise programs such as muscle training (prevention of locomotive syndrome), participation in communication with friends at contact salons (prevention of depression and dementia), and types of hobbies that make life worthwhile. It is expected that there will be some (depression and dementia prevention), and participation in life support activities through volunteer points. By aggregating the implementation status of these efforts by subregion, it is possible to quantitatively evaluate them.

The above user efforts collect information as small area-specific questionnaires or user questionnaire items unique to local governments, and incorporate them into the community-based comprehensive care analysis database platform, such as nursing care certification data and daily living area needs survey data. The index is aggregated by collating with the data of each user.

Of the six P-indexes mentioned above, the sixth P-index is a subregion that is not included in the data requiring long-term care such as the user's lifestyle, life philosophy, home environment, work environment, neighborhood environment, and economic situation. This is data that aggregates the basic attributes of users that are difficult to control on the side for each subregion. These may be useful for cause analysis between subregions with the same user efforts but different effects.

S index: [By business x by subregion x by mental and physical condition stage by subregional efforts x average implementation status]
・ By business × by subregion × by subregional status classification “non-certified” by subregional efforts × average implementation amount ・ By business × by subregion × degree of dementia independence by subregional efforts of “independence” × average implementation amount ・By business x by subregion x degree of disability independence by subregional efforts of "independence" x average implementation amount / by business x by subregion x certification survey item "independence" by subregional efforts x average implementation amount / by business x small Regional x Daily Living Area Needs Survey Group Score By Subregion Efforts by Stage x Average Implementation / Project x Subregion x Subregion Attributes, etc. (Indicator for Cause Analysis of Differences between Subregions)
Here, among the above 6 S indexes, the top 5 S indexes are the average independence duration of the mental and physical condition items of the certified data system calculated by the O index, the daily living area needs survey, the new comprehensive business checklist, etc. Within the duration of each subregion's mental and physical condition, the efforts of each subregion (prevention of care, improvement of life support service system, activities to promote participation in hobbies, work, volunteers, etc.) are carried out within the duration of the individual user. Calculate as an average value, aggregate it in subregions, and calculate the average implementation amount.

The sixth S index is data such as population and household composition of subregions, and is the basic attributes of subregions that are difficult to control by subregional efforts. These may be useful for cause analysis between subregions that have the same subregional efforts (S index) but different effects (O index).

The average implementation amount corresponds to the average value during the relevant period, such as the number of infrastructure allocations for various subregional efforts and the maintenance rate of related staff.

Examples of subregional efforts include the development and provision of exercise programs (prevention of locomotive syndrome) such as muscle training, and the construction and placement of places for communication with friends (prevention of depression and dementia) at contact salons, etc.・ Participation call activities, activities to support the acquisition of motivation for hobbies and work, life support activities using volunteer points, and activities to train life support supporters are envisioned.

The above subregional efforts collect information as questionnaire items or existing management items unique to local governments, and incorporate them into the community-based comprehensive care analysis database platform to collect data on certification of long-term care and daily living area needs survey data. Index aggregation is performed by matching with the data of each user.

Next, the case where the linked OPS index is used for disease prevention and disease aggravation prevention other than long-term care will be described.

[Aggregation target area]
・ Aggregation by medical insurer: The following 5 types are roughly classified. Union Health Insurance, Mutual Aid Health Insurance, Japan Health Insurance Association, National Health Insurance, Medical Union for the Elderly Elderly. There is one type of long-term care insurer (municipalities, wide-area union units).

・ Disease type: Disease types are neoplasm (cancer), stroke, acute myocardial infarction, diabetes, mental illness (depression, dementia, etc.), head fracture and thigh fracture due to fall, respiratory / digestive organs.・ Chronic diseases of the internal organs such as circulatory organs and urinary organs.

[Target person]
Healthy people, low-risk people, medium-risk people, and high-risk people for each disease of all generations (elderly, working, minor generation).

As a method of defining the above risk, test items and the like are picked up for each disease, and even for items having continuous numerical values such as sample test values of medical examination items, those numerical values are determined by setting a predetermined threshold value. By allocating to the risk stages of, it is possible to convert to discrete code values, and it can be treated in the same way as the classification of long-term care required status in the case of long-term care.

[Aggregation unit]
<Work area medical insurer (Union Health Insurance, Mutual Aid Health Insurance)>
With insurers at the top, business establishments, in-house companies, business headquarters, business divisions, departments, sections <Regional medical insurers (Japan Health Insurance Association, National Health Insurance, Medical Union for the Elderly)>
With prefectures at the top, secondary medical areas, municipalities (administrative districts in the case of government ordinances, branch offices in the case of core / general cities / wide-area unions, lower levels, daily living areas, junior high school districts, elementary school districts, residents' associations, etc. [Data source]
・ Medical examination data (common throughout Japan, blood tests, diagnostic findings, interviews, etc.)
・ Medical receipt data (common throughout Japan)
・ Effort data by composition hierarchy of medical insurers (by aggregation unit) (common questionnaire data unique to insurers, data held by management departments, etc.)
・ User activity data (questionnaire data such as exercise habits, dietary habits, drinking / tobacco, various stresses, biosensor data, etc.)
・ Data such as attributes of medical insurers by rank (by aggregation unit) (common questionnaire data unique to insurers, data owned by management departments, etc.)
・ Data such as user attributes by rank of medical insurer (by aggregation unit) (common questionnaire data unique to insurers, data owned by management department, etc.)
The items subject to medical examination may differ depending on the type of insurer in the medical examination data. It can be said that it is mental and physical condition related data corresponding to the long-term care certification data in the case of long-term care.

Medical receipt data includes out-of-hospital, in-hospital (including DPC), dentistry, pharmacy, home-visit nursing, home medical care, etc. It can be said that the data is related to the service content (treatment content) provided by the medical institution, etc., which corresponds to the long-term care receipt data in the case of long-term care.

Effort data by composition level (by aggregation unit) of medical insurers corresponds to the creation of a mechanism to support and promote the efforts of users by disease and risk stage for the insured to which they belong.

Biosensor data for user efforts includes daily continuous measurement of activity, autonomic nervous activity, sleep, etc. by wearable biosensors, in addition to daily measurement data from home health equipment such as body weight, blood pressure, and body fat percentage. Data is also the target.

Data such as attributes of medical insurers by rank (by aggregation unit) is controlled by each level of business establishment / workplace in the case of occupational insurance, and by each level of subregion in the case of regional insurance. Difficult basic attributes, etc. This may be useful for cause analysis from the viewpoint of hierarchical attributes between hierarchical groups that have the same efforts but different effects.

Data such as user attributes by rank of medical insurer (by aggregation unit) is not included in medical examination data such as user's lifestyle, life philosophy, family environment, work environment, neighborhood environment and economic situation. This is data that aggregates basic attributes, etc. that are difficult to control by user efforts for each of the above layers. This may be useful for cause analysis from the viewpoint of user attributes between hierarchical groups that have the same efforts but different effects.

In the above, only the insurer's stratified group (business establishments and workplaces for occupational medical insurance, and each stratum small area within the prefecture for regional medical insurance) is targeted as the aggregation unit, and medical institutions are not listed as the aggregation unit. .. The reason is that, as a general rule, medical institutions have free access, and unlike nursing care, there is a high possibility that all insured persons of multiple insurers (five major insurers) are going to the hospital or hospitalized. This is because the data analysis of each insurer who holds the data only deals with some users of a predetermined medical institution, and the evaluation is insufficient. If medical institutions are evaluated accurately, it is necessary to integrate (unify) all the data of the five major insurers and then aggregate and analyze them by medical institution. However, it can be said that it is quite difficult to realize it under the current system. However, if it can be realized, the logic of collecting various indicators at that time is the same as that of one insurer.

The following is a detailed discussion of the above issues for each insurer for reference.

・ In the case of Japan Health Insurance Association and medical care for the elderly, the insurer is a prefecture, so the number is limited because it is a medical institution within the prefecture, and each hospital is used by a considerable number of insured persons or their dependents. Can be regarded as It is similar to the correspondence between certified persons and beneficiaries within each insurer of long-term care insurance.

・ In the case of Japan Health Insurance Association and Mutual Aid Health Insurance, the insureds live all over the country, so the number of hospitals used by each insured or dependent is enormous, and per insurer at each hospital. The number of insured persons in the number of patients in the hospital is likely to be quite small, and the sample may be too small to evaluate each hospital.

・ If the insurer of the National Health Insurance is a municipality, the medical institution used by the insured or dependents is not necessarily within the insurer, but at least up to the medical institutions within the secondary medical area including the insured. It is acceptable to see. Although the number of target medical institutions is not as large as that of union health insurance and mutual aid health insurance, the ratio of the number of insured persons of the national insurance of each medical institution to the number of patients of the medical institution is small, and it is necessary to evaluate each medical institution. The number of samples may be insufficient.

From the above, it should be noted that whether or not a medical institution can be accurately evaluated as in the case of long-term care depends on the type of insurer. In other words, regarding union health insurance, mutual aid health insurance, and national health insurance, the basic policy is not to provide guidance to medical institutions, but to provide individual guidance to insured persons or dependents. In other words, while the skills and treatment results of each medical institution are constant, whether the user's daily efforts (exercise, diet, drinking, tobacco, stress countermeasures, medication, etc.) and the mechanism to promote them are firmly established. It can be said that it holds the key.

O index (when improvement is not considered, only "average duration until deterioration" is used as O index, but when improvement is considered, "average duration until deterioration" and "average continuation until improvement" are used as O index. The following example, which uses "time" and "improvement rate", considers improvement).

O index (when improvement is not considered, only "average duration until deterioration" is used as O index, but when improvement is considered, "average duration until deterioration" and "average continuation until improvement" are used as O index. The following example using "time" and "improvement rate" is when improvement is considered. In addition, there are "average deterioration gradient" and "average improvement gradient" as another O index when improvement is considered. These are also shown).

In the following description, the symbol × does not mean multiplication, but represents a combination of items before and after it.

<Work area medical insurance (Union Health Insurance, Mutual Aid Health Insurance)>
[By illness x by occupational hierarchy group x by illness risk stage x average duration until deterioration (month)
By disease x occupational hierarchy group x disease risk stage x average duration until improvement (month)
By disease x occupational hierarchy group x disease risk stage x improvement rate (%)
Disease x Occupational Hierarchy Group x Disease Risk Stage x Average Deterioration Gradient Disease x Occupational Hierarchy Group x Disease Risk Stage x Average Improvement Gradient]
・ Neoplasm (cancer) × occupational hierarchy group × disease risk stage × average duration until deterioration (month)
・ Neoplasm (cancer) x occupational hierarchy group x disease risk stage x average duration until improvement (month)
・ Neoplasm (cancer) x occupational hierarchy group x disease risk stage x improvement rate (%)
・ Neoplasm (cancer) × by occupational hierarchy group × by disease risk stage × average deterioration gradient ・ Neoplasm (cancer) × by occupational hierarchy group × by disease risk stage × average improvement gradient ・ stroke × by occupational hierarchy group × Disease risk stage x average duration until deterioration (month)
・ Stroke x occupational hierarchy group x disease risk stage x average duration until improvement (month)
・ Stroke x occupational hierarchy group x disease risk stage x improvement rate (%)
・ Stroke × By occupational hierarchy group × By disease risk stage × Average deterioration gradient ・ Stroke × By occupational hierarchy group × By disease risk stage × Average improvement gradient ・ Acute myocardial infarction × By occupational hierarchy group × By disease risk stage × To worsening Average duration (month)
・ Acute myocardial infarction × By occupational hierarchy group × By disease risk stage × Average duration until improvement (month)
・ Acute myocardial infarction × occupational hierarchy group × disease risk stage × improvement rate (%)
・ Acute myocardial infarction × By occupational hierarchy group × By disease risk stage × Average deterioration gradient ・ Acute myocardial infarction × By occupational hierarchy group × By disease risk stage × Average improvement gradient ・ Diabetes × By occupational hierarchy group × By disease risk stage × Deterioration Average duration up to (month)
・ Diabetes x occupational hierarchy group x disease risk stage x average duration until improvement (month)
・ Diabetes x occupational hierarchy group x disease risk stage x improvement rate (%)
・ Diabetes × By occupational hierarchy group × By disease risk stage × Average deterioration gradient ・ Diabetes × By occupational hierarchy group × By disease risk stage × Average improvement gradient ・ By visceral chronic disease × By occupational hierarchy group × By disease risk stage × Deterioration Average duration (month)
・ Chronic visceral disease x occupational hierarchy group x disease risk stage x average duration until improvement (month)
・ Chronic visceral disease x occupational hierarchy group x disease risk stage x improvement rate (%)
・ Chronic visceral disease × By occupational hierarchy group × By disease risk stage × Average deterioration gradient ・ By visceral chronic disease × By occupational hierarchy group × By disease risk stage × Average improvement gradient)
・ Mental illness (depression, dementia) x occupational hierarchy group x illness risk stage x average duration until deterioration (month)
・ Mental illness (depression, dementia) x occupational hierarchy group x illness risk stage x average duration until improvement (month)
・ Mental illness (depression, dementia) x occupational hierarchy group x illness risk stage x improvement rate (%)
・ Mental illness (depression, dementia) × by occupational hierarchy group × by illness risk stage × average deterioration gradient ・ Mental illness (depression, dementia) × by occupational hierarchy group × by illness risk stage × average improvement gradient <Regional insurance (community insurance (depression, dementia) Association Kenpo, National Health Insurance, Medical Association for the Elderly Elderly) ＞
[By disease x Regional stratification group x Disease risk stage x Average duration until deterioration (month)
By disease x regional hierarchy group x disease risk stage x average duration until improvement (month)
By disease x regional hierarchy group x disease risk stage x improvement rate (%)
Disease x Regional Hierarchy Group x Disease Risk Stage x Average Deterioration Gradient Disease x Regional Hierarchy Group x Disease Risk Stage x Average Improvement Gradient]
・ Neoplasm (cancer) x regional stratification group x disease risk stage x average duration until exacerbation (month)
・ Neoplasm (cancer) × Regional stratification group × Disease risk stage × Average duration until improvement (month)
・ Neoplasm (cancer) x regional stratification group x disease risk stage x improvement rate (%)
・ Neoplasm (cancer) × Regional stratification group × Disease risk stage × Average deterioration gradient ・ Neoplasm (cancer) × Regional stratification group × Disease risk stage × Average improvement gradient ・ Stroke × Regional stratification group × Disease risk stage x average duration until deterioration (month)
・ Stroke × Regional stratification group × Disease risk stage × Average duration until improvement (month)
・ Stroke x regional stratification group x disease risk stage x improvement rate (%)
・ Stroke × Regional stratification group × Disease risk stage × Average deterioration gradient ・ Stroke × Regional stratification group × Disease risk stage × Average improvement gradient ・ Acute myocardial infarction × Regional stratification group × Disease risk stage × Deterioration Average duration (month)
・ Acute myocardial infarction × Regional stratification group × Disease risk stage × Average duration until improvement (month)
・ Acute myocardial infarction × Regional stratification group × Disease risk stage × Improvement rate (%)
・ Acute myocardial infarction × Regional stratification group × Disease risk stage × Average deterioration gradient ・ Acute myocardial infarction × Regional stratification group × Disease risk stage × Average improvement gradient ・ Diabetes × Regional stratification group × Disease risk stage × Deterioration Average duration up to (month)
・ Diabetes × Regional stratification group × Disease risk stage × Average duration until improvement (month)
・ Diabetes x regional stratification group x disease risk stage x improvement rate (%)
・ Diabetes × Regional stratification group × Disease risk stage × Average deterioration gradient ・ Diabetes × Regional stratification group × Disease risk stage × Average improvement gradient ・ Visceral chronic disease × Regional stratification group × Disease risk stage × Deterioration Average duration (month)
・ Chronic visceral disease × Regional stratification group × Disease risk stage × Average duration until improvement (month)
・ Chronic visceral disease x regional stratification group x disease risk stage x improvement rate (%)
・ Chronic visceral disease × Regional stratification group × Disease risk stage × Average deterioration gradient ・ Visceral chronic disease × Regional stratification group × Disease risk stage × Average improvement gradient ・ Mental illness (depression, dementia) × Regional stratification group × Disease risk stage × Average duration until deterioration (month)
・ Mental illness (depression, dementia) × Regional stratification group × Disease risk stage × Average duration until improvement (month)
・ Mental illness (depression, dementia) x regional stratification group x illness risk stage x improvement rate (%)
・ Mental illness (depression, dementia) × Regional stratification group × Disease risk stage × Average deterioration gradient ・ Mental illness (depression, dementia) × Regional stratification group × Disease risk stage × Average improvement gradient Of these, the average duration until deterioration and the average deterioration gradient are indexed for the deterioration group. In addition, the average duration until improvement, the improvement rate, and the average improvement gradient shall be indexed for the improvement group.

Here, the average duration in the normal state (no risk) is the number of months elapsed from the date of birth (age itself). In addition, the average duration of the other low-risk or higher stages corresponds to the period from the date when the risk stage first entered to the date when the risk stage changed to another risk stage.

Adjust the average duration by gender and age group. As for the age classification, the elderly are classified into the early-stage elderly category and the late-stage elderly category. For those under 65 years old, for example, the age group is defined by the age group for each teenager. In addition, the determination timing of the age category shall be determined from the age in the certification month when each stage of mental and physical condition was first reached for each disease.

In the above, the lowest stage of the risk stage is defined as the normal stage, but a method of defining the normal stage itself as a plurality of stages, for example, a tentative name, "mental and physical strength stage" is also conceivable. In other words, the idea of so-called "health promotion" is to promote the active training of the mind and body that are less susceptible to illness by further finely grading the normal stage from the viewpoint of the disease risk stage. be.

The following shows an example in which a specific mental and physical strength stage can be set.

・ Athletic ability (flexibility, jumping ability, endurance, instantaneousness, etc.)
-Mental stress tolerance (work stress tolerance; workplace stress tolerance, family stress tolerance, etc.), etc. Various measures and methods for increasing this mental and physical strength level can also be derived within the scope of this proposal.

P index (As described above, users whose O index is in the direction of deterioration and users who are in the direction of improvement are divided into a deterioration group and an improvement group, and the P index is obtained for each group).
<Work area medical insurance (Union Health Insurance, Mutual Aid Health Insurance)>
[By disease x by occupational hierarchy group x by disease risk stage x by user efforts x average implementation status]
・ Neoplasm (cancer) × by occupational hierarchy group × by disease risk stage by user effort × average implementation status ・ Stroke × by occupational hierarchy group × by disease risk stage by user effort × average implementation status ・ Acute myocardial infarction × By occupational hierarchy group × By disease risk stage User efforts × Average implementation status ・ Diabetes × By occupational hierarchy group × By disease risk stage × By user efforts × Average implementation status ・ Visceral chronic disease × By occupational hierarchy group × Disease risk stage By user's efforts x Average implementation status / mental illness (depression, dementia) x By occupational hierarchy group x By disease risk stage By user's efforts x Average implementation status / by illness x By occupational hierarchy group x By group Attributes, etc. (index for cause analysis of differences between hierarchical groups)
<Community medical insurance (Japan Health Insurance Association, National Health Insurance, Medical Association for the Elderly Elderly)>
[By disease x by regional hierarchy group x by disease risk stage x by user efforts x average implementation status]
・ Neoplasm (cancer) × by regional stratification group × by disease risk stage by user effort × average implementation status ・ Stroke × by region stratification group × by disease risk stage by user effort × average implementation status ・ Acute myocardial infarction × By Regional Hierarchy Group x By User Efforts by Disease Risk Stage x Average Implementation Status / Diabetes x By Regional Hierarchical Group x By Disease Risk Stage By User Effort x Average Implementation Status / Visceral Chronic Disease x By Regional Hierarchical Group x Disease Risk Stage By user's efforts × Average implementation status / mental illness (depression, dementia) × By regional hierarchy group × By disease risk stage By user's efforts × Average implementation status / disease × By regional hierarchy group × Users by the same group Attributes, etc. (index for cause analysis of differences between hierarchical groups)
The P index is the user's efforts (health promotion, disease prevention, exercise habits, eating habits, abstinence / alcohol saving, smoking cessation / smoking habits, various stress tolerance enhancement, etc. within the average duration for each disease risk stage calculated by the O index. Calculate and calculate the average amount of addiction (hobbies, etc.).

The average implementation amount corresponds to the average value during the period such as the number of times and days of use of various user efforts and the participation rate in various activities.

The above user efforts collect information as subregional questionnaires or user questionnaire items unique to local governments, and incorporate them into the community-based comprehensive care analysis database platform for individual users such as medical examination data and medical receipt data. The index is aggregated by collating with the data of.

The 7th P index mentioned above is a usage that is difficult for the user to control, which is partly included in the medical examination data such as the user's lifestyle, life philosophy, home environment, work environment, neighborhood environment, and economic situation. It is the data that aggregates the basic attributes of the insurer for each hierarchical group of the insurer. These may be useful for cause analysis between hierarchical groups with the same user efforts but different effects.

S index (As described above, users whose O index is in the direction of deterioration and users who are in the direction of improvement are divided into a deterioration group and an improvement group, and the S index is obtained for each group).
<Work area medical insurance (Union Health Insurance, Mutual Aid Health Insurance)>
[By disease x by occupational hierarchy group x by disease risk stage by hierarchy group efforts x average implementation status]
・ Neoplasm (cancer) × by occupational hierarchy group × by disease risk stage, by hierarchical group effort × average implementation status ・ Stroke × by occupational hierarchy group × by disease risk stage, by hierarchical group initiative × average implementation status ・ Acute myocardial infarction × By Occupational Hierarchy Group x Disease Risk Stage By Hierarchical Group Initiative x Average Implementation Status / Diabetes x By Occupational Hierarchy Group x By Disease Risk Stage By Hierarchical Group Initiative x Average Implementation Status / Visceral Chronic Disease x By Occupational Hierarchy Group x Disease Risk Stage By Strategic Group Initiatives x Average Implementation Status / Mental Illness (Depression, Dementia) x Occupational Hierarchical Group x Disease Risk Stage Hierarchical Group Initiatives x Average Implementation Status / Disease x Occupational Hierarchical Group x Hierarchical Group by Group Attributes, etc. (index for cause analysis of differences between hierarchical groups)
<Community medical insurance (Japan Health Insurance Association, National Health Insurance, Medical Association for the Elderly Elderly)>
[By disease x by regional hierarchy group x by disease risk stage by hierarchy group efforts x average implementation status]
・ Neoplasm (cancer) × Regional stratified group × Disease risk staged stratified group efforts × Average implementation status ・ Stroke × Regional stratified group × Disease risk staged stratified group initiatives × Average implementation status ・ Acute myocardial infarction × By Regional Hierarchical Group x Disease Risk Stage Hierarchical Group Initiatives x Average Implementation Status / Diabetes x Regional Hierarchical Group x Disease Risk Stage Hierarchical Group Initiatives x Average Implementation Status / Visceral Chronic Disease x Regional Hierarchical Group x Disease Risk Stage By Group Efforts x Average Implementation Status / Mental Illness (Depression, Dementia) x By Regional Hierarchical Group x By Disease Risk Stage Hierarchical Group Efforts x Average Implementation Status / Disease x By Regional Hierarchical Group x Hierarchical Group by Same Group Attributes, etc. (*) Index for analyzing the cause of differences between hierarchical groups The S index is a hierarchical group effort (health promotion, disease prevention, exercise habits, eating habits, etc.) within the average duration of each disease risk stage calculated by the O index. Calculate and calculate the average amount of sickness / sickness, sickness / sickness, sickness, sickness, sickness, sickness, sickness, sickness, sickness, sickness, sickness, and sickness.

The average amount of implementation corresponds to the average value during the period such as the number and frequency of implementation of various hierarchical group efforts, the recognition rate, participation rate, and implementation rate of various activities.

The above-mentioned hierarchical group approach collects information as insurer-specific related party questionnaire items or existing management items, incorporates them into the health management analysis database platform (tentative name), and users of medical examination data, medical receipt data, etc. Index aggregation is performed by matching with individual data.

In this way, the OPS index can be used for disease prevention and disease aggravation prevention other than long-term care, and objective and quantitative data analysis enables accurate problem extraction, and further measures. The effectiveness will be shown concretely, and it will be possible to accurately implement community-based comprehensive care projects using highly accurate indicators.

In the above, examples of the O index, the P index, and the S index have been shown separately for the long-term care system and the medical system. However, in reality, the mental and physical condition of the long-term care system (classification of long-term care status, degree of independence of dementia, degree of independence of disability, certification survey items such as ADL, etc.) and the mental and physical condition of medical system (blood test items, etc.) are parallel. In many cases, it progresses in a targeted manner. In addition, depending on the disease, the onset or progression of the disease may be the cause of long-term care.

FIG. 14 shows the relationship between the main illness and the mental and physical condition of the long-term care system. Column 1 shows the name of the disease. No. 1 to 5 indicate 5 diseases designated by the Ministry of Health, Labor and Welfare in the medical plan. Columns 2 to 5 show risk factors for the disease, and columns 6 show how the disease develops. Depending on the degree of risk factors, for example, they are classified into health, low risk, medium risk, high risk, etc. Especially in the high risk group, the probability of developing the disease is high, and the patient has to be hospitalized. The risk judgment based on the blood test value is applicable when the degree of risk is reflected in the blood test value. In addition, there are two ways of developing the disease, sudden and continuous (progressive). The former corresponds to the case of stroke, acute myocardial infarction and thigh fracture, and the latter corresponds to the case of cancer, dementia and locomotive syndrome. Equivalent to. Column 7 shows the relationship between the illness and the cause of long-term care, and is applicable when the deterioration of the mental and physical condition of the long-term care system occurs or progresses at the same time due to the onset or progression of the illness. Rows 8 to 10 show the physical and mental states of the long-term care system. Further, columns 11 to 14 show the classification of medical and long-term care measures.

The details of each disease will be described below.

No. For cancer (neoplasm) of 1, the disease risk factors are lifestyle (diet, exercise, smoking, etc.) and heredity, and the degree of risk is reflected in blood test values (tumor markers, etc.). The onset of the disease progresses continuously in a progressive manner, and may be cured after hospitalization treatment (surgery, radiation therapy, etc.) and return to daily life, or the prognosis may be poor and death may occur in a short period of time. It is considered that the patient will not be in nursing care. Therefore, as a measure classification, measures for disease prevention and disease aggravation prevention other than long-term care are required.

No. Regarding the stroke of 2, the disease risk factor is lifestyle (diet, exercise, etc.), and the degree of risk is reflected in the blood test value. The onset of the disease is suddenly caused by cerebral thrombosis, cerebral embolism, etc., and even after inpatient treatment (catheter treatment, etc.), paralysis / contracture of limbs and discontinuous deterioration of physical functions such as ADL occur. In addition, since it causes cerebrovascular dementia, in that case, discontinuous deterioration of cognitive function including IADL also occurs. As a measure classification, all measures other than long-term care prevention are required.

No. Regarding the acute myocardial infarction of 3, the disease risk factors are lifestyle (diet, exercise, smoking, etc.) and hypertension, and the degree of risk is reflected in the blood test value (neutral fat, cholesterol, etc.). The onset of the disease may occur suddenly due to thrombosis or embolization of the coronary artery, and may heal after hospitalization treatment (balloon catheter treatment, thrombolytic treatment, etc.) and return to daily life, or may lead to death in a short period of time with a poor prognosis. , Like the No. 1 cancer, it is considered that it will not lead to medium- to long-term care. Therefore, as a measure classification, measures for disease prevention and disease aggravation prevention other than long-term care are required.

No. Regarding diabetes in 4, the disease risk factor is lifestyle (diet, exercise, etc.), and the degree of risk is reflected in the blood test value (HbA1c value, blood glucose level, etc.). The onset of the disease progresses continuously with progression, and in the case of medium risk or less, it is possible to lead an independent daily life by improving lifestyle and insulin treatment, and it is necessary to reach a state of long-term care for the medium to long term. Conceivable. However, it becomes a high risk, and when blindness, dialysis, amputation of the lower limbs, etc. occur, discontinuous deterioration of physical function develops, leading to a medium- to long-term care state. Therefore, the policy classification is centered on measures to prevent diseases other than long-term care and prevent the aggravation of illness, but for high-risk persons, measures to prevent the aggravation of long-term care are also required.

No. Regarding 5 dementia (Alzheimer's type, Lewy body type, etc.), the risk factors for illness are lifestyle-related (diet, exercise, socializing, etc.) and heredity, especially friends due to good or bad communication with family and going out. The amount of interaction with acquaintances is attracting attention as a risk factor. Furthermore, there are cases where the onset of depression due to stress (family, community, etc.) leads to the onset of dementia. It is considered that the onset of the disease progresses continuously with aging and surely leads to a medium- to long-term care state. Therefore, as a measure classification, all measures related to illness and long-term care are required.

No. Regarding the thigh fracture of No. 6, the disease risk factors are the risk of falling such as a step in the house and the deterioration of physical function due to locomotive syndrome and the like. The onset of the disease suddenly occurs due to a fall in the house or the like, and discontinuous deterioration of physical functions such as ADL occurs even after inpatient treatment (surgical out, rehabilitation, etc.). No. Unlike the stroke of 2, paralysis and contracture are usually not accompanied. As a measure classification, it is necessary to prevent long-term care such as deterrence of physical function decline and avoidance of fall risk, and deterrence of aggravation of long-term care after the onset.

No. Regarding 7 locomotive syndrome (decrease in physical function due to aging), disease risk factors include lifestyle (exercise, etc.) and living environment (environment in which one has to walk, one in which one has to work on a daily basis, and slopes). Whether or not there is a lot of environment etc.) It is considered that the onset of the disease progresses continuously with aging and leads to a medium- to long-term care state. As a measure classification, it is necessary to prevent long-term care such as deterrence of physical function decline and avoidance of fall risk, and deterrence of aggravation of long-term care after the onset.

No. For 8 other chronic diseases (cranial nerve system, respiratory system, digestive system, urinary system), the disease risk factor is lifestyle (diet, exercise, etc.), and the blood test values are characteristic test items for each disease. The degree of risk is reflected in. The onset of the disease is progressive and continuous, but in the case of moderate risk or higher, it is possible to live a daily life without hospitalization while receiving home medical care and long-term care. However, it is at high risk, and the possibility of accelerating death is increased due to the development of discontinuous deterioration of physical function due to acute exacerbation. Therefore, as a measure classification, measures to prevent diseases other than long-term care and prevent the aggravation of illness are the main measures, but for medium-risk and above, measures to prevent the aggravation of long-term care centered on home medical care and long-term care cooperation are required. ..

In the above, the relationship between the mental and physical conditions of the long-term care system by illness has been specifically explained. From the above, No. 2 strokes, No. Diabetes of 4, No. Dementia No. 5 (Alzheimer's type, Lewy body type, etc.), No. No. 6 thigh fracture, No. Locomotive Syndrome No. 7 and No. For 8 other chronic diseases (in the case of home medical patients), the onset and progression of the disease are likely to be the cause of long-term care. It is necessary to evaluate and analyze the P index and S index at the same time. That is, both the O index, P index, and S index of the long-term care system of the above embodiment and the same index of the medical system are linked for the same person, and their correlation analysis and the like are performed from a bird's-eye view of both long-term care and medical care. I need to go. Even in such a case, the method of totaling the long-term care index and the medical index is basically the same as in the previous examples.

Next, measures aimed at extending the length of stay at home through home medical care / long-term care cooperation will be described. As a drastic deterrent measure against the increase in medical expenses, the Ministry of Health, Labor and Welfare has significantly reduced the number of hospitalized patients with chronic diseases, mainly medical care beds, by 2025 or later, and has hundreds of thousands of patients. Has announced a plan to shift to home medical care. Along with this, there is an urgent need to install home medical equipment, home-visit medical care, and the development of bases and human resources to provide home-visit nursing services, and it is also necessary to provide long-term care services in cooperation with these. In addition, along with this, the Ministry of Health, Labor and Welfare issued the "Guide for Home Medical Care / Nursing Care Cooperation Promotion Project" in 2015, in which the following eight policy proposals that contribute to the promotion of home medical care / long-term care cooperation are proposed. It is carried out.

(A) Understanding local medical / nursing resources (b) Identifying issues related to home medical / nursing cooperation and examining countermeasures (c) Promoting the construction of a seamless home medical / home care provision system (d) Medical care・ Support for information sharing of care-related persons (e) Consultation support for home medical care / nursing cooperation (f) Training for medical / nursing-related persons (g) Dissemination and enlightenment to local residents (h) Related cities for home medical care / nursing cooperation Cooperation between wards, towns and villages By finding and implementing effective measures in each of the above (a) to (c), the effect of home medical care / long-term care cooperation will be enhanced, and finally medical care by extending the period of stay at home (shortening the hospitalization period).・ It will lead to a significant reduction in nursing care benefit costs.

FIG. 15A is an image diagram showing a difference in the length of stay at home (difference in hospitalization period) due to a difference in efforts for home medical care / long-term care cooperation.

The vertical axis of FIG. 15A is the medical / long-term care benefit cost (total of medical benefit cost and long-term care benefit cost), and the horizontal axis is the medium- to long-term period (unit: month). The starting point on the horizontal axis is a preset time, for example, the birth month of the user at the age of 65, and the period of stay at home from the timing of the onset of aggravation for each disease to hospitalization is calculated as the O index. In addition, the shaded area in the figure represents the medical benefit cost for hospitalization, the horizontal line part represents the medical benefit cost for home medical care, and the vertical line part represents the long-term care benefit cost incurred with home medical care, as can be seen from the figure. In addition, it can be seen that the hospitalization medical benefit cost is extremely high compared to the total of the home medical care benefit cost and the long-term care benefit cost. In other words, the longer the length of stay at home (the shorter the length of hospital stay), the more likely it is that medical expenses can be significantly reduced.

If the measures related to home medical care / long-term care cooperation are enhanced by subregion and the efforts of the medical side, the long-term care side, and the user side are activated, the period of stay at home will be longer, and the patient will not be hospitalized and will stay at home. It will be possible to take care of it. The graph expressing the situation is 151.

On the other hand, if the measures related to home medical care / long-term care cooperation are insufficient for each subregion and the medical care side, long-term care side, and user side are not taking measures or activating them, home medical care cannot be realized. As the disease becomes more severe, he will be hospitalized and will be taken care of at the hospital. The graph expressing the situation is 153.

In addition, the graph expressing the state in between these is 152. As can be seen from FIG. 15A, the lifetime medical / long-term care benefit cost for the user is the smallest in the case of 151, the largest in the case of 153, and the middle in the case of 152 among the three patterns.

In this case, the length of stay at home becomes an O index due to the aggravation of the user's illness, and the content of cooperation between home medical care and long-term care services of the small area or long-term care establishment / home medical institution related to the user during the period of stay at home is P. As an index, the measures of small areas or nursing care establishments / home medical institutions during the period of stay at home (the above measures (a) to (ku)) are S indexes, and a consolidated OP / S index based on a combination of these. Will implement home medical care / long-term care cooperation, which is one of the comprehensive community care businesses.

Actually, as shown in FIG. 15B, hospitalization due to acute exacerbation occurs during home medical care, and after discharge, hospitalization due to acute exacerbation occurs again after shifting to home medical care, so-called home medical care and acute exacerbation. In some cases, hospitalization is repeated. Even if home medical care and hospitalization due to acute exacerbation are repeated in this way, the case where home medical care / nursing care is completed to the end shown at the top of FIG. 15B is evaluated as the best maintenance state. On the other hand, even if the shift to home medical care is shown at the bottom of FIG. 15B, if the patient is readmitted to the hospital immediately without long-lasting and shifts to the care as it is, it is evaluated as the worst maintenance state. The middle part of FIG. 15B shows an average maintenance state between them.

In any case, by grasping the period of stay at home, which is an O index, what kind of home medical care and long-term care services were provided and what kind of measures were implemented within that period, and it became a comprehensive community care business. It can be reflected.

Next, a specific example of the OPS index in the above-mentioned home medical care / long-term care cooperation business will be described.

First, we will explain the aggregation conditions for this business.

[Aggregation target area]
Each home medical care / long-term care cooperation project: It is necessary to quantitatively carry out the PDCA cycle such as issues, effective measures, and monitoring for each of the above (a) to (c).

[Target person]
Certified person (certified person who has each illness and needs support / nursing care to receive home medical care)
[Aggregation unit]
In the case of small areas / government ordinances within the care insurer such as the daily living area, administrative districts, daily living areas, junior high school districts, elementary school districts, residents' associations, etc. Areas, junior high school districts, elementary school districts, residents'associations, etc. ・ In the case of wide-area associations, branch offices, constituent municipalities, daily living areas, junior high school districts, elementary school districts, residents' associations, etc. Short-term admission, etc.)
Home medical institutions (visit clinics, home-visit nursing, home-visit rehabilitation, etc.)
Medical care bed hospital (hospitalization)
[Data source]
・ Long-term care certification data (common throughout Japan)
・ Nursing care receipt data (common throughout Japan)
・ Medical examination data (common throughout Japan, blood tests, diagnostic findings, interviews, etc.)
・ Medical receipt data (common throughout Japan)
・ Data on subregional efforts, etc. (Efforts related to all measures (A) to (K) above)
・ Data on long-term care establishment efforts, etc. (Of the measures (a) to (c) above, in particular, efforts related to home medical care and long-term care cooperation of long-term care establishments related to (a), (c), (d), and (f))
・ Data on home medical institution efforts, etc. (Of the measures (a) to (ku) above, in particular, efforts related to home medical care / nursing care cooperation of home medical institutions related to (a), (c), (d), and (f))
・ Data on medical care bed hospital efforts, etc. (Of the measures (a) to (ku) above, in particular, efforts related to home medical care / long-term care cooperation of home medical institutions related to (a), (c), (d), and (f))
・ Data on user efforts, etc. (Of the measures (a) to (k) above, in particular, efforts related to home medical care / nursing care cooperation of home medical institutions related to (e) and (g))
・ Data such as subregion attributes (questionnaire data unique to insurers, etc.)
・ Data such as user attributes by subregion (questionnaire data unique to insurers, etc.)
・ Attribute data of nursing care establishments, home medical institutions, etc. (questionnaire data unique to insurers, etc.)
・ Data such as user attributes of nursing care establishments and home medical institutions (questionnaire data unique to insurers, etc.)
Data such as subregional attributes are data such as population, population density, elderly ratio, major industrial population ratio, household composition, and geographical environment of subregions, and are basic attributes of subregions that are difficult to control by subregional efforts. And so on. Data such as user attributes by subregion is data that aggregates the basic attributes of users, etc., which are difficult to control by user efforts, for each subregion, such as the user's home environment, work environment, neighborhood environment, and economic conditions. Is. For each attribute data, the code that leads to good or bad is defined in consideration of the magnitude relationship.

As a result, subregional efforts and subregional user efforts may be useful for cause analysis between subregions that have the same but different effects.

In addition, the long-term care business affiliation data, home medical institution attribute data, etc. are basic attributes, etc. that are difficult to control by the efforts of the business establishment or institution, depending on the scale of each business establishment or institution, the type of corporation, and the like.

User attribute data by nursing care establishment, user attribute data by home medical institution, etc. are difficult to control by user efforts such as user's lifestyle, life philosophy, home environment, work environment, neighborhood environment and economic situation. This is data that aggregates the basic attributes of various users for each nursing care facility, home medical institution, etc.

As the data such as each of these attributes, the code that leads to good or bad is defined in consideration of the magnitude relationship.

As a result, there is a possibility that the services provided and the efforts of nursing care establishments / home medical institutions, etc. may be useful for cause analysis between establishments / institutions with the same but different effects.

As mentioned above, in the case of home medical care / long-term care cooperation, it can be seen that both analysis by subregion and analysis by long-term care establishment / home medical institution are required.

Specific examples of OPS indicators in the home medical care / long-term care cooperation business based on the above-mentioned aggregation conditions will be described. In the following, dementia and depression are included in cranial nerve diseases, and diabetes is included in gastrointestinal diseases.

O index <subregion analysis>
[By illness x by subregion x average length of stay at home (month)]
・ By cranial nerve system disease × by subregion × average length of stay at home (month)
・ By respiratory disease x subregion x average length of stay at home (month)
・ By digestive system disease × by subregion × average length of stay at home (month)
・ By urinary system disease × by subregion × average length of stay at home (month)
<Analysis of long-term care establishments and home medical institutions>
[By illness x by long-term care establishment / by home medical institution x average length of stay at home (month)]
・ By cranial nerve system disease × By long-term care establishment ・ By home medical institution × Average length of stay at home (month)
・ By respiratory illness × By long-term care establishment ・ By home medical institution × Average length of stay at home (month)
・ By digestive system disease × By nursing care establishment ・ By home medical institution × Average length of stay at home (month)
・ By urinary system disease × By nursing care establishment ・ By home medical institution × Average length of stay at home (month)
Here, the calculation start month of the average staying at home is the month when the aggravation of each disease occurs. Usually, due to the acute exacerbation of each disease, after inpatient treatment at an acute care hospital, the discharge month is assumed to be the start month following the discharge. In addition, the average length of stay at home shall be the period during which home medical care was provided from the start month to hospitalization, and if hospitalization is repeated, the period of home medical care provided during that period shall be added up.

For the above indicators, adjust the average duration by gender and age group. As the age classification, the early-stage elderly classification and the late-stage elderly classification are adopted. The timing of determining the age category shall be determined from the age in the month when home medical care is started.

The continuous check method for the average length of stay at home in the small area where each user lives and the continuous check method for nursing care establishments and home medical institutions are the same as the establishment continuation check method described in FIG. Method shall be applied.

P index <subregion analysis>
[By disease x by subregion x by user's efforts x average efforts (month)]
・ Cranial nerve system disease x subregion x user effort x average effort status (month)
・ Respiratory disease x Subregion x User effort x Average effort status (Monday)
・ By gastrointestinal disease × by subregion × by user's efforts × average efforts (month)
・ By urinary system disease × by subregion × by user's efforts × average efforts (month)
<Analysis of long-term care establishments and home medical institutions>
[By illness x by nursing care establishment / by home medical institution x by collaborative activity x average provision status (month)]
・ By cranial nerve system disease × By long-term care establishment ・ By home medical institution × By collaborative activity × Average provision status (month)
・ By respiratory disease × By nursing care establishment ・ By home medical institution × By collaborative activity × Average provision status (month)
・ By gastrointestinal disease × By nursing care establishment ・ By home medical institution × By collaborative activity × Average provision status (month)
・ By urinary system disease × By nursing care establishment ・ By home medical institution × By collaborative activity × Average provision status (Monday)
The P index is calculated by calculating the average amount of implementation related to user efforts that contribute to the extension of the home stay period and collaborative activities between nursing care establishments and home medical institutions within the home stay period calculated by the O index.

The average implementation amount corresponds to the average value during the period such as the number of times and days of use of various services and the participation rate in various activities.

Examples of user efforts by subregion include (e) consultation support regarding home medical care / long-term care cooperation and (g) dissemination and enlightenment to local residents among the above eight measures. Utilization rate and participation rate of insurers in dissemination and enlightenment activities are assumed.

In addition, the following are assumed as collaborative activities by nursing care establishments and home medical institutions.

・ The ratio of long-term care service staff meetings by user, the content of the meetings, and the attendance rate by job type (information gathered from long-term care receipt data, user questionnaires, and business establishment questionnaires)
・ Emergency response cooperation status between care workers and clinics during acute exacerbations by user (information gathered from nursing care receipt data, medical receipt data, etc.)
・ System usage status by business and disease in the multidisciplinary information sharing system by user (Daily log data of the system is aggregated as monthly data and collected as cooperation activity information)
The above user efforts are to collect information as questionnaires by small area peculiar to local governments, user questionnaire items, questionnaire items for nursing care establishments / home medical institutions, log aggregation data of multidisciplinary cooperation information sharing system, etc. Is incorporated into the community-based comprehensive care analysis database platform, and each user is matched with the data of each individual user to aggregate the indicators.

S index <subregion analysis>
[By disease x by subregion x by subregion efforts x average efforts (month)]
・ Cranial nerve system disease x subregion x subregion effort x average effort status (month)
・ Respiratory disease x subregion x subregion effort x average effort status (month)
・ By gastrointestinal disease x subregion x subregion effort x average effort status (month)
・ By urinary system disease × by subregion × by subregion effort × average effort status (month)
<Analysis of long-term care establishments and home medical institutions>
[By illness x by nursing care establishment / by home medical institution x by establishment, etc. x average effort status (month)]
・ By cranial nerve system disease × By long-term care establishment ・ By home medical institution × By establishment, etc. × Average status of efforts (Monday)
・ Respiratory diseases x Nursing care establishments / Home medical institutions x Business establishments x Average efforts (Monday)
・ By digestive system disease × By nursing care establishment ・ By home medical institution × By establishment, etc. × Average status of efforts (Monday)
・ By urinary system disease × by nursing care establishment ・ By home medical institution × by establishment, etc. × average effort status (month)
Here, the S index is the efforts of each subregion (8 measures (A) to (K) above) and the efforts of nursing care establishments and home medical institutions (mainly the above) within the period of stay at home calculated by the O index. Calculated as the average value of (a), (c), (d), and (f) of the eight measures within the relevant duration, and aggregated by subregion or by nursing care establishment / home medical institution. Calculate and obtain the average implementation amount.

The efforts of the above subregions, long-term care establishments, home medical institutions, etc. are to collect information as questionnaire items or existing management items unique to local governments, and incorporate them into the community-based comprehensive care analysis database platform for various individual users. Index aggregation is performed by matching with the unit data.

In the above, when the aggregation unit is analyzed as a small area, the O index is defined by [disease x small area x average length of stay at home (month)], and the aggregation unit is analyzed as a long-term care establishment / home medical institution. , [By illness x by long-term care establishment / by home medical institution x average length of stay at home (month)], the O index was defined. That is, the average value of the user's staying at home (month) by aggregation unit is used as the O index.

However, the O index is not limited to these, and those defined as follows may be defined as other O indexes. That is, when the aggregation unit is analyzed as a small area, [by disease x small area x average stay at home rate (%)], and when the aggregation unit is analyzed as a long-term care establishment / home medical institution, [by disease x It is also possible to adopt [By long-term care establishment / by home medical institution x average stay at home rate (%)].

Here, the average stay-at-home rate (%) = the average value (%) of the user's {total stay-at-home period (months) ÷ total analysis target period (months) of the user} by aggregation unit, and analysis The target period is the period from the month when home medical care is first started to the latest month of the analysis target period. Actually, the analysis target period consists of the sum of the total home medical care period (stay at home period) and the total hospitalization period (including the hospitalization period due to acute exacerbation).

In many cases, there are no hospitalization / discharge events and the period of stay at home continues for several months from the month when the user's home medical care is first started, so it should be excluded from the analysis. For this reason, the standard number of months (threshold value) is set from the average length of stay at home (month) for all insurers, and the standard number of months (threshold value) has passed since the month when the user first started home medical care. If not, a method of excluding the user from the analysis target can be considered.

In addition, without distinguishing by disease, as an O index, in the case of subregion analysis, [by subregion x average stay at home rate (%)], and in the case of nursing care establishment / home medical institution analysis, [by nursing care establishment]・ It is also possible to adopt [By home medical institution x average stay at home rate (%)].

The embodiment of the invention relating to home medical care / long-term care cooperation has been described above. As described above, in this embodiment, by analyzing and evaluating from two viewpoints, one by small area and the other by nursing care establishment / home medical institution, it is possible to comprehensively identify effective measures that contribute to the extension of the period of stay at home. It will be possible, and eventually the hospitalization period will be shortened, which will lead to the integration of medical care and long-term care, and the reduction of benefit costs.

The above-mentioned embodiment explained a low-level community-based comprehensive care business system implemented and operated by a local government or the like as an insurer. Here, the insurer is the local government for nursing care insurers, the occupational medical insurance for union health insurance and mutual aid health insurance, etc. Is.

It is possible to apply the method of the comprehensive community care business system at the insurer level to the system of the comprehensive community care business at the higher prefecture or country level. Hereinafter, embodiments of this higher-level regional comprehensive business system will be described.

<< Embodiment of high-level community-based comprehensive care system >>
In the above-mentioned lower-level community-based comprehensive system, each insurer of long-term care and medical care grasped the value of the OPS index for each business unit for each aggregation unit such as a subregion or a business establishment. On the other hand, in the embodiment of the high-level regional comprehensive system, the aggregation unit is each insurer, and the disparity and the magnitude of the divergence of the OPS indicators of each insurer are calculated, and the region of each insurer is obtained. To be able to grasp the status of comprehensive care business at the level of prefectures and countries.

The system configuration in this case will be described with reference to FIGS. 16 and 17. Since the basic system configuration has many parts in common with those shown in FIGS. 1 and 2, the common parts will be described below with the same reference numerals.

This higher-level community-based comprehensive care business system also has a database 10 and a solution function 15 for the target business area 16 as shown in the schematic diagram of FIG.

However, since the aggregation unit is each insurer and it is a regional comprehensive business system at the prefectural and national levels higher than those, various insurers A, B, C, ... The following various data are provided.

-Basic data 11, resident address data 111, resident common number (tentative name) 112, regional characteristic data 113, regional efforts 114.

-As long-term care insurance data 12, long-term care certification data 121, long-term care receipt data 122, and long-term care establishment ledger 123.

-As data 13 related to medical insurance, elderly specific medical examination data 131, National Health Insurance / late-stage elderly medical receipt data 132, medical institution ledger 133.

-Basic checklist (results of daily living area needs survey) 141, measure plans and achievements for small areas, business establishments, and users 142 as specific data 14 for measures related to comprehensive community care unique to local governments for residents.

The database 10 centralizes and stores the necessary parts of these data 11, 12, 13, and 14. That is, in this database 10, the related parts of these data 11, 12, 13, and 14 are linked and unified.

In order to achieve the measures implemented by each business area 161, 162, 163, 164, 165, 166 of the community-based comprehensive care business, the indicators set for each business area 161, 162, 163, 164, 165, 166 are It is registered in the index menu 151 of the solution function 15.

The solution function 15 is realized by a computer and is executed in each business area 161, 162, 163, 164, 165, 166 using each data 11, 12, 13, 14 centralized in the database 10. The achievement status and implementation status of measures are analyzed by aggregating the values of the indicators set for each measure. For this purpose, in addition to the index menu 151 described above, an aggregation unit menu 152 and various analysis functions 153 are provided.

That is, in the upper community comprehensive care business system shown in FIG. 16, various data including individual level data provided by each insurer A, B, C, ... Is under the jurisdiction of the upper prefecture or country level. In the solution function 15, the index obtained from these data is aggregated and analyzed for each insurer, which is the aggregation unit, by the same method as the above-mentioned comprehensive community care business system at the lower level. It is a system. Here, as described above, the insurers that are the aggregation unit are long-term care insurers (local governments), occupational medical insurers (union health insurance, mutual aid health insurance), and regional medical insurers (Japan Health Insurance Association, National Health Insurance, Medical Union for the Elderly). ).

FIG. 17 shows the configuration of a computer system that creates a database 10 and executes a community-based comprehensive care business. Also in FIG. 17, the computer system has a client 31 and a server 32 connected to the client 31, and the storage / reading of data is controlled by the server 32.

The client 31 is a computer provided to each insurer A, B, C, ..., And the data acquisition unit 311 has basic data 11, long-term care insurance data 12, medical insurance data 13, measures, etc. regarding residents. The unique data 14 is taken in and provided to the server 32.

The server 32 stores the above-mentioned data 11, 12, 13, and 14 in the original data storage unit 101 of the database 10 as unified data by the database creation unit 321.

In addition, the server 32 has an index calculation unit 322, an aggregation unit 323, and an analysis unit 324 in order to realize the solution function of the community-based comprehensive care business. The index calculation unit 322 calculates the value of the index registered for each business area in the index menu 151 of FIG.

The tabulation department 323 is a tabulation unit (long-term care insurer (local government), occupational medical insurer (union health insurance, mutual aid health insurance), regional medical insurer (association health insurance, national health insurance, late-stage elderly) registered in the tabulation unit menu 152 in FIG. The value of the index is totaled for each medical association), etc.). The analysis unit 324 executes each of the analysis functions 153 shown in FIG.

In this higher-level community-based comprehensive care business system, as in the lower-level community-based comprehensive care system, the mental and physical condition of long-term care is suppressed, care prevention / life support, disease prevention / disease severity prevention, home medical care / nursing care cooperation. For each business, O / P / S indicators are obtained, and each insurer is evaluated using the consolidated O / P / S indicators. Therefore, a specific example of the OPS index in each of the above-mentioned projects will be described.

First, we will explain the aggregation conditions for deterring the aggravation of the mental and physical condition of long-term care.

[Aggregation target area]
Each long-term care insurance service type: Single service type for outpatient / residential / facility type, combined service type for visit / short-term admission type [Target person]
Certified person requiring long-term care (support 1 and 2, long-term care 1 to 5)
[Aggregation unit]
Long-term care insurer (local government)
[Data source]
・ Long-term care certification data (common throughout Japan)
・ Nursing care receipt data (common throughout Japan)
・ Data on business establishment efforts (questionnaire data common to business establishments by each service type unique to insurers)
・ Data such as business affiliation ・ Data such as user attributes by business establishment (questionnaire data common to business establishments by each service type unique to insurers)
Here, the business affiliation data is data such as the scale of the business establishment, the type of corporation, etc., and a part of the data is also included in the nursing care business establishment ledger. In other words, it is the basic attributes of business establishments that are difficult to control by the efforts of business establishments.

Data such as user attributes by business establishment is not included in the data requiring nursing care such as lifestyle, life philosophy, home environment, work environment, neighborhood environment and economic situation of the user, and it is difficult to control by user efforts. This is data that aggregates the basic attributes of various users for each business establishment.

As the data such as each of these attributes, the code that leads to good or bad is defined in consideration of the magnitude relationship.

As a result, it may be useful for cause analysis between long-term care insurers who have the same services and business establishment efforts but have different effects.

Specific examples of the OPS index in suppressing the aggravation of the mental and physical condition of long-term care based on the above-mentioned aggregation conditions and the like will be described below.

O index (when improvement is not considered, only "average duration until deterioration" is used as O index, but when improvement is considered, "average duration until deterioration" and "average continuation until improvement" are used as O index. The following example using "time" and "improvement rate" is when improvement is considered. Further, as another O index when improvement is considered, the "average deterioration gradient" and "average improvement" explained in FIG. 8C are used. There is a "gradient", which is also shown).

In the following description, the symbol × does not mean multiplication, but represents a combination of items before and after it.

[By service type x By long-term care insurer x By mental and physical condition item stage x Average duration until deterioration (month)
By service type × By long-term care insurer × By mental and physical condition item stage × Average duration until improvement (month)
By service type × By long-term care insurer × By mental and physical condition item stage × Improvement rate (%)
Service type x Long-term care insurer x Mental and physical condition item stage x Average deterioration gradient Service type x Long-term care insurer x Mental and physical condition item stage x Average improvement gradient]
・ By service type × By long-term care insurer × By long-term care status classification Stage × Average duration until deterioration (month)
・ By service type × By long-term care insurer × By long-term care status classification Stage × Average duration until improvement (month)
・ By service type × By long-term care insurer × By long-term care status classification Stage × Improvement rate (%)
・ By service type × By long-term care insurer × By long-term care status category × Average deterioration gradient ・ By service type × By long-term care insurer × By long-term care status category × Average improvement gradient ・ By service type × By long-term care insurer × Dementia independence level by stage x average duration until deterioration (month)
・ By service type × By long-term care insurer × By dementia independence stage × Average duration until improvement (month)
・ By service type × By long-term care insurer × By dementia independence stage × Improvement rate (%)
・ By service type × By long-term care insurer × By dementia independence degree stage × Average deterioration gradient ・ By service type × By long-term care insurer × By dementia independence degree stage × Average improvement gradient ・ By service type × By long-term care insurer × Dementia independence stage x average duration until deterioration (month)
・ By service type × By long-term care insurer × By disability independence stage × Average duration until improvement (month)
・ By service type × By long-term care insurer × By disability independence stage × Improvement rate (%)
・ By service type × By long-term care insurer × By disability independence stage × Average deterioration gradient ・ By service type × By long-term care insurer × By disability independence stage × Average improvement gradient ・ By service type × By long-term care insurer × Certification survey Item stage x Average duration until deterioration (month)
・ By service type × By long-term care insurer × By certification survey item stage × Average duration until improvement (month)
・ By service type × By long-term care insurer × By certification survey item stage × Improvement rate (%)
・ By service type × By long-term care insurer × By accredited survey item Stage × Average deterioration gradient ・ By service type × By long-term care insurer × By accredited survey item × Average improvement gradient The period and average deterioration gradient are indexed for the deterioration group. In addition, the average duration until improvement, the improvement rate, and the average improvement gradient shall be indexed for the improvement group.

The average duration, which is these index values, is adjusted by gender and age group. As the age classification, the early-stage elderly classification and the late-stage elderly classification will be adopted. In addition, the determination timing of the age category shall be determined from the age in the certification month when each stage of each mental and physical condition was first reached.

As described in the implementation of the lower-level community-based comprehensive care system in the first half of this specification, since the O index is defined for each stage of the mental and physical condition items in the above embodiment, it is assumed that the number of O indexes will be enormous. Will be done.

If the number of indicators is larger than necessary, information overload will occur. Therefore, if the number of indicators is large, the amount will be aggregated to an appropriate amount by the same method as described above. That is, since the ratio of the total number of people in each stage of the mental and physical condition items is different for each stage, it is not possible to compare between the total units (between insurers, etc.) by their simple average. Therefore, the weighted average based on the aggregated number of people ratio at each stage of the same mental and physical condition item is calculated by the following formula as an O index that aggregates all stages of the mental and physical condition item.

Here, the index values for each stage of the mental and physical state items to be aggregated are V1, V2, V3 ... Vn, and the total number of people (number of records) for each stage index is N1, N2, N3 ... Nn. Let U be the index value.

U = (V1 x N1 + V2 x N2 + V3 x N3 + ... + Vn x Nn)
÷ (N1 + N2 + N3 + ... + Nn)
Regarding the degree of aggregation, it is possible to use an O index that aggregates not only each stage of the same mental and physical condition items but also related mental and physical condition items such as the first group to the fifth group of the accredited survey items. ..

As a result, when the number of indicators is large, it is possible to aggregate the contents of the indicators and the number of indicators that are understandable to the insurer at the site and lead to concrete practice for business improvement.

The above aggregation example can be similarly applied to the P index and the S index. In addition, all business areas of the community-based comprehensive care business (long-term care severity deterrence business (mental and physical condition improvement / maintenance continuation business), new comprehensive business (long-term care prevention / life support), home medical care / nursing care cooperation, acute exacerbation hospitalization / discharge medical care / nursing care It can be applied to cooperation, disease prevention, and prevention of severity).

P index: [By service type x By long-term care insurer x By mental and physical condition stage x By service item x Average amount of service provided]
・ By service type × By long-term care insurer × By care status category By stage Service item × Average service provision amount ・ By service type × By long-term care insurer × Dementia independence degree By stage Service item × Average service provision amount ・ Service By type × By long-term care insurer × By disability independence stage By service item × By average service provision amount / service type × By long-term care insurer × By certification survey item By stage Service item × Average service amount / service type × Nursing care User attributes by insurer x establishment (index for analyzing the cause of differences by long-term care insurer)
Here, as described above, the P index is obtained by calculating the average amount of services provided by the user within the average duration of each mental and physical condition stage calculated by the O index. When the improvement of the mental and physical condition is considered, as the O index, in addition to the duration until the stage of the mental and physical condition deteriorates and the duration until the improvement as described above, the deterioration gradient and the improvement gradient based on these durations are used. Sometimes.

In such a case, in obtaining the above-mentioned P index, the O index is divided into a deterioration group of users showing a deterioration tendency of the mental and physical condition and an improvement group showing an improvement tendency of the mental and physical condition, and the deterioration group and improvement. Find the P index for each group.

The average amount of service provided corresponds to the average value during the period such as the total number of units, the number of times, and the number of days.

In addition, as an example of service items, services provided to each user such as additional items by service type that contribute to maintenance and improvement of mental and physical condition and combinations of basic services related to business affiliation and service amount are used.

Furthermore, the above-mentioned [by service type x long-term care insurer x user attributes by business establishment, etc.] is the long-term care certification data such as the user's lifestyle, life philosophy, home environment, work environment, neighborhood environment, and economic situation. This is data that aggregates the attributes of users that are difficult to control on the business establishment side, which are not included in the above, for each business establishment. These may be useful for cause analysis between long-term care insurers who have the same service but different effects.

S index: [By service type x By long-term care insurer x By mental and physical condition stage by business establishment efforts x Average amount of implementation]
・ By service type × By long-term care insurer × By stage of need for long-term care × By business establishment effort × Average implementation amount ・ By service type × By long-term care insurer × Dementia Independence degree By business establishment effort × Average implementation amount ・ Service By type x By long-term care insurer x By disability independence stage by business establishment effort x Average amount of implementation / service type x By long-term care insurer x By certification survey item By stage of establishment effort x Average amount of implementation / service type x Nursing care By insurer x business affiliation, etc. (*) Index for analyzing the cause of differences between business establishments Here, as described above, the S index is an aggregate unit within the average duration for each mental and physical condition stage calculated by the O index. Calculate the average amount of measures and initiatives implemented by a long-term care insurer. When not only the deterioration of the mental and physical condition but also the improvement is considered, as the O index, as described above, the duration until the stage of the mental and physical condition deteriorates and the duration until the improvement, and the deterioration gradient and the improvement gradient based on these durations. Is used.

In such a case, in obtaining the above-mentioned S index, the O index is divided into a deterioration group of users showing a deterioration tendency of the mental and physical condition and an improvement group showing an improvement tendency of the mental and physical condition, and the deterioration group and improvement are performed. Find the S index for each group.

Of the above-mentioned five S indexes, the top four are calculated as the average value of each service establishment within the average duration of each service establishment within the average duration of the mental and physical condition calculated by the O index. Then, the long-term care insurer aggregates it and calculates the average implementation amount.

On the other hand, the fifth S index is data that is partly included in the nursing care business register, such as the size of the business establishment and the type of corporation, and is based on the attributes of the business establishment that are difficult to control by the efforts of the business establishment. be.

As a result, it may be useful for cause analysis between long-term care insurers who have the same business establishment efforts (S index) but different effects (O index).

In addition, the average implementation amount of business establishment efforts corresponds to the average value during the relevant period, such as the training participation rate and the number of implementations of various initiatives.

Examples of business establishment efforts include "initiatives to increase the number of qualified personnel", "initiatives to improve the staff system", and "initiatives to work closely with related parties inside and outside the business establishment" to contribute to maintaining and improving the physical and mental condition. , "Efforts for skill improvement training, etc.", "Efforts related to guidance for users and their families", "Efforts to enhance service items", and "Efforts to improve the user's home (whereabouts)" are envisioned. It should be noted that these efforts are closely related to the calculation requirements of each service addition item, etc. in the P index.

Next, the aggregation conditions related to long-term care prevention and life support will be explained in comparison with the above-mentioned deterrence of the mental and physical condition of long-term care.

[Aggregation target area]
Each care prevention / life support project: Examples of the above projects include facility infrastructure development projects such as local community salons, life support coordinators and volunteer training projects, user-oriented projects such as care prevention classes, and various information provision projects. It is necessary to quantitatively carry out the PDCA cycle such as issues, effective measures, and monitoring for each.

[Target person]
Non-certified persons (healthy persons, frail persons, non-certified persons using the new comprehensive business, etc.)
[Aggregation unit]
Long-term care insurer (local government)
[Data source]
・ Data on subregional efforts (data common to subregions unique to insurers, data owned by the Regional Comprehensive Support Center, etc.)
・ User effort data (insurer-specific user-specific daily living area needs survey data, new comprehensive business checklist, other questionnaire data, etc.)
・ Data such as subregional attributes (insurer-specific user-specific daily living area needs survey data, new comprehensive business checklist, other questionnaire data, etc.)
・ Data such as user attributes by subregion (user-specific daily living area needs survey data unique to insurers, new comprehensive business checklist, other questionnaire data, etc.)
Data such as subregional attributes are data such as population, population density, elderly ratio, major industrial population ratio, household composition, and geographical environment of subregions, and are basic attributes of subregions that are difficult to control by subregional efforts. And so on. Data such as user attributes by subregion is data that aggregates the basic attributes of users, etc., which are difficult to control by user efforts, for each subregion, such as the user's home environment, work environment, neighborhood environment, and economic conditions. Is. For each attribute data, the code that leads to good or bad is defined in consideration of the magnitude relationship.

As a result, subregional efforts and subregional user efforts may be useful for cause analysis between long-term care insurers who have the same but different effects.

Specific examples of OPS indicators in long-term care prevention / life support based on the above-mentioned aggregation conditions will be described.

O index; [By business x By long-term care insurer x By mental and physical condition stage x Average independence duration (month)]
・ By business × By long-term care insurer × Category requiring long-term care × Average independence (non-certified) Duration (month)
・ By business × By long-term care insurer × Dementia independence degree × Average independence duration (month)
・ By business × By long-term care insurer × Degree of independence of disability × Average duration of independence (month)
・ By business × By long-term care insurer × By accredited survey item × Average independence duration (month)
・ By business × By long-term care insurer × Daily living area needs survey By group Score by stage × Average duration (month)
Here, the calculation start month of the independence duration is the birth month of 65 years old regardless of the elderly.

In addition, in the case of the long-term care status category, the duration until the new certification month coincides with the non-certification period. For other certified data-based mental and physical condition items, there is a possibility that they will be independent at the time of new certification, and the duration will be until the first certification month when they move to the first stage, which is the second worst after the independence stage. In addition, even during the non-certification period, each of the above mental and physical condition items shall be regarded as "independent".

The daily living area needs survey group score stage is the data obtained by counting up the number of applicable items of each group's survey items in the basic checklist, etc., according to the staged threshold value, and is the average for each stage. The calculation logic of the duration is the same as that of deterring the aggravation of the mental and physical condition.

Of the five O-indexes mentioned above, the top four adjust the average duration by gender, but due to the nature of the index, adjustment by age group is unnecessary. On the other hand, for the fifth O index, the average duration is adjusted by gender and age group. As the age classification, the early-stage elderly classification and the late-stage elderly classification will be adopted. In addition, the judgment timing of the age classification shall be judged from the age in the certification month when each stage of each mental and physical condition was first reached.

As for the continuation check method in the average duration of the small area where each user lives, the same method as the establishment continuation check method described with reference to FIG. 12 shall be applied.

P index: [By business x By long-term care insurer x By mental and physical condition stage by user's efforts x Average implementation]
・ By business × By long-term care insurer × By user's efforts in the category of “non-certified” requiring long-term care × Average amount of implementation ・ By business × By long-term care insurer × Dementia Independence degree By user's efforts of “independence” × Average implementation Amount / business x long-term care insurer x disability independence "independence" user effort x average implementation amount / business x long-term care insurer x certification survey item "independence" user effort x average implementation amount By business x By long-term care insurer x Daily living area Needs survey By group Score by user's efforts x Average amount / business x By long-term care insurer x By subregion User attributes, etc. (Differences between long-term care insurers) Cause analysis index)
The P index is the user's efforts (nursing care) within the average independence duration of the mental and physical condition items of the certified data system calculated by the O index, the daily living area needs survey, the new comprehensive business checklist, and other mental and physical condition stages. Calculate and calculate the average implementation amount of prevention, use of life support services, participation in hobbies, work, volunteers, etc.).

The average implementation amount corresponds to the average value during the period such as the number of times and days of use of various services and the participation rate in various activities.

Examples of user efforts include participation in exercise programs such as muscle training (prevention of locomotive syndrome), participation in communication with friends at contact salons (prevention of depression and dementia), and types of hobbies that make life worthwhile. It is expected that there will be some (depression and dementia prevention), and participation in life support activities through volunteer points. By aggregating the implementation status of these efforts by long-term care insurer, it is possible to quantitatively evaluate them.

The above user efforts collect information as small area-specific questionnaires or user questionnaire items unique to local governments, and incorporate them into the community-based comprehensive care analysis database platform, such as nursing care certification data and daily living area needs survey data. The index is aggregated by collating with the data of each user.

Of the six P-indexes mentioned above, the sixth P-index is a subregion that is not included in the data requiring long-term care such as the user's lifestyle, life philosophy, home environment, work environment, neighborhood environment, and economic situation. This is data that aggregates the basic attributes of users that are difficult to control on the side for each subregion. These may be useful for analyzing the causes of long-term care insurers who have the same user efforts but different effects.

S index: [By business x By long-term care insurer x By mental and physical condition stage by subregional efforts x Average implementation status]
・ By business × By long-term care insurer × By sub-regional effort of “non-certified” status of long-term care x Average implementation amount ・ By business × By long-term care insurer × Dementia Independence degree “Independence” by sub-regional initiative × Average implementation Amount / business x long-term care insurer x disability independence "independence" by small area effort x average implementation amount / business x long-term care insurer x certification survey item "independence" by small area effort x average implementation amount By business × By long-term care insurer × Daily living area Needs survey By group Score by small area effort × Average amount / business × By long-term care insurer × Small area attributes, etc. (Cause analysis of differences between long-term care insurers) Index)
Here, among the above 6 S indexes, the top 5 S indexes are the average independence duration of the mental and physical condition items of the certified data system calculated by the O index, the daily living area needs survey, the new comprehensive business checklist, etc. Within the continuation period for each mental and physical condition of the user, the efforts of each small area (prevention of long-term care, improvement of life support service system, activities to promote participation in hobbies, work, volunteers, etc.) are carried out within the continuation period of the individual user. Calculate as an average value, aggregate it by long-term care insurer, and calculate the average implementation amount.

The sixth S index is data such as population and household composition of subregions, and is the basic attributes of subregions that are difficult to control by subregional efforts. As a result, it may be useful for cause analysis between long-term care insurers who have the same subregional efforts (S index) but different effects (O index).

The average implementation amount corresponds to the average value during the relevant period, such as the number of infrastructure allocations for various subregional efforts and the maintenance rate of related staff.

Examples of subregional efforts are the same as in the case of the comprehensive community care business at the lower level mentioned above, such as the development of exercise programs (locomotive syndrome prevention) such as muscle training, the establishment of a provision system, and friends at contact salons, etc. It is envisioned that there will be activities to build, arrange, and call for participation in communication (prevention of depression and dementia), activities to support the acquisition of motivation for hobbies and work, and activities to support life by volunteer points and training activities for life support supporters. ..

These subregional efforts collect information as questionnaire items or existing management items unique to local governments, incorporate them into the community-based comprehensive care analysis database platform, and incorporate them into community-based comprehensive care analysis database infrastructure, such as nursing care certification data and daily living area needs survey data. The index is aggregated by collating with the data of each user.

Next, the case where the linked OPS index is used for disease prevention and disease aggravation prevention other than long-term care will be described.

[Aggregation target area]
・ Aggregation by medical insurer: The following 5 types are roughly classified. Union Health Insurance, Mutual Aid Health Insurance, Japan Health Insurance Association, National Health Insurance, Medical Union for the Elderly Elderly. There is one type of long-term care insurer (municipalities, wide-area union units).

・ Disease type: Disease types are neoplasm (cancer), stroke, acute myocardial infarction, diabetes, mental illness (depression, dementia, etc.), head fracture and thigh fracture due to fall, respiratory / digestive organs.・ Chronic diseases of the internal organs such as circulatory organs and urinary organs.

[Target person]
Healthy people, low-risk people, medium-risk people, and high-risk people for each disease of all generations (elderly, working, minor generation).

As a method of defining the above risk, test items and the like are picked up for each disease, and even for items having continuous numerical values such as sample test values of medical examination items, those numerical values are determined by setting a predetermined threshold value. By allocating to the risk stages of, it is possible to convert to discrete code values, and it can be treated in the same way as the classification of long-term care required status in the case of long-term care.

[Aggregation unit]
Unlike the case of the community-based comprehensive care business at the lower level mentioned above, there are occupational medical insurers (union health insurance, mutual aid health insurance) and community medical insurers (Japan Health Insurance Association, National Health Insurance, Medical Union for the Elderly Elderly).

[Data source]
・ Medical examination data (common throughout Japan, blood tests, diagnostic findings, interviews, etc.)
・ Medical receipt data (common throughout Japan)
・ Effort data by composition hierarchy of medical insurers (by aggregation unit) (common questionnaire data unique to insurers, data held by management departments, etc.)
・ User activity data (questionnaire data such as exercise habits, dietary habits, drinking / tobacco, various stresses, biosensor data, etc.)
・ Data such as attributes of medical insurers by rank (by aggregation unit) (common questionnaire data unique to insurers, data owned by management departments, etc.)
・ Data such as user attributes by rank of medical insurer (by aggregation unit) (common questionnaire data unique to insurers, data owned by management department, etc.)
The items subject to medical examination may differ depending on the type of insurer in the medical examination data. It can be said that it is mental and physical condition related data corresponding to the long-term care certification data in the case of long-term care.

Medical receipt data includes out-of-hospital, in-hospital (including DPC), dentistry, pharmacy, home-visit nursing, home medical care, etc. It can be said that the data is related to the service content (treatment content) provided by the medical institution, etc., which corresponds to the long-term care receipt data in the case of long-term care.

Effort data by composition level (by aggregation unit) of medical insurers corresponds to the creation of a mechanism to support and promote the efforts of users by disease and risk stage for the insured to which they belong.

Biosensor data for user efforts includes daily continuous measurement of activity, autonomic nervous activity, sleep, etc. by wearable biosensors, in addition to daily measurement data from home health equipment such as body weight, blood pressure, and body fat percentage. Data is also the target.

Data such as attributes of medical insurers by rank (by aggregation unit) is controlled by each level of business establishment / workplace in the case of occupational insurance, and by each level of subregion in the case of regional insurance. Difficult basic attributes, etc. This may be useful for cause analysis from the viewpoint of hierarchical attributes between medical insurers who have the same efforts but different effects.

Data such as user attributes by rank of medical insurer (by aggregation unit) is not included in medical examination data such as user's lifestyle, life philosophy, family environment, work environment, neighborhood environment and economic situation. This is data that aggregates basic attributes, etc. that are difficult to control by user efforts for each of the above layers. This may be useful for analyzing the causes of medical insurers who have the same efforts but different effects from the viewpoint of user attributes.

O index (when improvement is not considered, only "average duration until deterioration" is used as O index, but when improvement is considered, "average duration until deterioration" and "average continuation until improvement" are used as O index. The following example using "time" and "improvement rate" is when improvement is considered. In addition, there are "average deterioration gradient" and "average improvement gradient" as another O index when improvement is considered. These are also shown).

In the following description, the symbol × does not mean multiplication, but represents a combination of items before and after it.

<Work area medical insurance (Union Health Insurance, Mutual Aid Health Insurance)>
<Work area medical insurer (Union Health Insurance, Mutual Aid Health Insurance)>
[By illness x Occupational medical insurer x By illness risk stage x Average duration until deterioration (month)
By illness × By occupational medical insurer × By illness risk stage × Average duration until improvement (month)
By illness x occupational medical insurer x illness risk stage x improvement rate (%)
Disease x Occupational Medical Insurer x Disease Risk Stage x Average Deterioration Gradient Disease x Occupational Medical Insurer x Disease Risk Stage x Average Improvement Gradient]
・ Neoplasm (cancer) x occupational medical insurer x disease risk stage x average duration until deterioration (month)
・ Neoplasm (cancer) x occupational medical insurer x disease risk stage x average duration until improvement (month)
・ Neoplasm (cancer) x occupational medical insurer x disease risk stage x improvement rate (%)
・ Neoplasm (cancer) × by occupational medical insurer × by disease risk stage × average deterioration gradient ・ Neoplasm (cancer) × by occupational medical insurer × by disease risk stage × average improvement gradient ・ stroke × occupational medical insurance By person x disease risk stage x average duration until deterioration (month)
・ Stroke × Occupational medical insurer × Disease risk stage × Average duration until improvement (month)
・ Stroke × Occupational medical insurer × Disease risk stage × Improvement rate (%)
・ Stroke × Occupational medical insurer × Disease risk stage × Average deterioration gradient ・ Stroke × Occupational medical insurer × Disease risk stage × Average improvement gradient ・ Acute myocardial infarction × Occupational medical insurer × Disease risk stage × Average duration until deterioration (month)
・ Acute myocardial infarction × Occupational medical insurer × Disease risk stage × Average duration until improvement (month)
・ Acute myocardial infarction × Occupational medical insurer × Disease risk stage × Improvement rate (%)
・ Acute myocardial infarction × By occupational medical insurer × By disease risk stage × Average deterioration gradient ・ Acute myocardial infarction × By occupational medical insurer × By disease risk stage × Average improvement gradient ・ Diabetes × By occupational medical insurer × Disease risk stage Another x average duration until deterioration (month)
・ Diabetes × Occupational medical insurer × Disease risk stage × Average duration until improvement (month)
・ Diabetes x occupational medical insurer x disease risk stage x improvement rate (%)
・ Diabetes × By occupational medical insurer × By disease risk stage × Average deterioration gradient ・ Diabetes × By occupational medical insurer × By disease risk stage × Average improvement gradient ・ Visceral chronic disease × By occupational medical insurer × By disease risk stage × Average duration until deterioration (month)
・ Chronic visceral disease × Occupational medical insurer × Disease risk stage × Average duration until improvement (month)
・ Chronic visceral disease x occupational medical insurer x disease risk stage x improvement rate (%)
・ Chronic visceral disease × By occupational medical insurer × By disease risk stage × Average deterioration gradient ・ By visceral chronic disease × By occupational medical insurer × By disease risk stage × Average improvement gradient)
・ Mental illness (depression, dementia) x occupational medical insurer x illness risk stage x average duration until deterioration (month)
・ Mental illness (depression, dementia) x occupational medical insurer x illness risk stage x average duration until improvement (month)
・ Mental illness (depression, dementia) x occupational medical insurer x illness risk stage x improvement rate (%)
・ Mental illness (depression, dementia) × Occupational medical insurer × Disease risk stage × Average deterioration gradient ・ Mental illness (depression, dementia) × Occupational medical insurer × Disease risk stage × Average improvement gradient <Region Medical insurers (Association Kenpo, National Health Insurance, Medical Union for the Elderly) ＞
[By disease x by community medical insurer x by disease risk stage x average duration until deterioration (month)
By illness x by community medical insurer x by illness risk stage x average duration until improvement (month)
By disease x by community medical insurer x by disease risk stage x improvement rate (%)
Disease x Community Health Insurer x Disease Risk Stage x Average Deterioration Gradient Disease x Community Health Insurer x Disease Risk Stage x Average Improvement Gradient]
・ Neoplasm (cancer) x regional medical insurer x disease risk stage x average duration until deterioration (month)
・ Neoplasm (cancer) × by community medical insurer × by disease risk stage × average duration until improvement (month)
・ Neoplasm (cancer) x regional medical insurer x disease risk stage x improvement rate (%)
・ Neobiology (cancer) × by regional medical insurer × disease risk stage × average deterioration gradient ・ Neobiology (cancer) × by regional medical insurer × disease risk stage × average improvement gradient ・ stroke × regional medical insurance By person x disease risk stage x average duration until deterioration (month)
・ Stroke × By community medical insurer × By disease risk stage × Average duration until improvement (month)
・ Stroke x Community medical insurer x Disease risk stage x Improvement rate (%)
・ Stroke × Regional medical insurer × Disease risk stage × Average deterioration gradient ・ Stroke × Regional medical insurer × Disease risk stage × Average improvement gradient ・ Acute myocardial infarction × Regional medical insurer × Disease risk stage × Average duration until deterioration (month)
・ Acute myocardial infarction × By community medical insurer × By disease risk stage × Average duration until improvement (month)
・ Acute myocardial infarction × By community medical insurer × By disease risk stage × Improvement rate (%)
・ Acute myocardial infarction × By regional medical insurer × By disease risk stage × Average deterioration gradient ・ Acute myocardial infarction × By regional medical insurer × By disease risk stage × Average improvement gradient ・ Diabetes × By regional medical insurer × Disease risk stage Another x average duration until deterioration (month)
・ Diabetes x community medical insurer x disease risk stage x average duration until improvement (month)
・ Diabetes x community medical insurer x disease risk stage x improvement rate (%)
・ Diabetes × by regional medical insurer × by disease risk stage × average deterioration gradient ・ Diabetes × by regional medical insurer × by disease risk stage × average improvement gradient ・ by visceral chronic disease × by regional medical insurer × by disease risk stage × Average duration until deterioration (month)
・ Chronic visceral disease x regional medical insurer x disease risk stage x average duration until improvement (month)
・ Chronic visceral disease x regional medical insurer x disease risk stage x improvement rate (%)
・ Chronic visceral disease x regional medical insurer x disease risk stage x average deterioration gradient-Visceral chronic disease x regional medical insurer x disease risk stage x average improvement gradient-mental illness (depression, dementia) x regional medical care By insurer x Disease risk stage x Average duration until deterioration (month)
・ Mental illness (depression, dementia) × by community medical insurer × by illness risk stage × average duration until improvement (month)
・ Mental illness (depression, dementia) x regional medical insurer x illness risk stage x improvement rate (%)
・ Mental illness (depression, dementia) × by regional medical insurer × illness risk stage × average deterioration gradient ・ Mental illness (depression, dementia) × by regional medical insurer × illness risk stage × average improvement gradient Of the O indicators, the average duration until deterioration and the average deterioration gradient are aggregated for the deterioration group. In addition, the average duration until improvement, the improvement rate, and the average improvement gradient shall be indexed for the improvement group.

Here, the average duration in the normal state (no risk) is the number of months elapsed from the date of birth (age itself). In addition, the average duration of the other low-risk or higher stages corresponds to the period from the date when the risk stage first entered to the date when the risk stage changed to another risk stage.

Adjust the average duration by gender and age group. As for the age classification, the elderly are classified into the early-stage elderly category and the late-stage elderly category. For those under 65 years old, for example, the age group is defined by the age group for each teenager. In addition, the determination timing of the age category shall be determined from the age in the certification month when each stage of mental and physical condition was first reached for each disease.

In the above, the lowest stage of the risk stage is defined as the normal stage, but a method of defining the normal stage itself as a plurality of stages, for example, a tentative name, "mental and physical strength stage" is also conceivable. In other words, the idea of so-called "health promotion" is to promote the active training of the mind and body that are less susceptible to illness by further finely grading the normal stage from the viewpoint of the disease risk stage. be.

The following shows an example in which a specific mental and physical strength stage can be set.

・ Athletic ability (flexibility, jumping ability, endurance, instantaneousness, etc.)
-Mental stress tolerance (work stress tolerance; workplace stress tolerance, family stress tolerance, etc.), etc. Various measures and methods for increasing this mental and physical strength level can also be derived within the scope of this proposal.

P index (As described above, users whose O index is in the direction of deterioration and users who are in the direction of improvement are divided into a deterioration group and an improvement group, and the P index is obtained for each group).
<Work area medical insurer (Union Health Insurance, Mutual Aid Health Insurance)>
[By illness x by occupational medical insurer x by illness risk stage by user's efforts x average implementation status]
・ Neoplasm (cancer) × by occupational medical insurer × by disease risk stage by user effort × average implementation status ・ Stroke × by occupational medical insurer × by disease risk stage by user effort × average implementation status ・ Acute myocardium Infarction x Occupational Medical Insurer x Disease Risk Stage User Initiative x Average Implementation Status / Diabetes x Occupational Medical Insurer x Disease Risk Stage User Initiative x Average Implementation Status / Visceral Chronic Disease x Occupational Medical Insurer By x Disease risk stage by user effort x Average implementation status / mental illness (depression, dementia) x Occupational medical insurer x Disease risk stage by user effort x Average implementation status / disease x Occupational medical insurer User attributes, etc. by x x group (index for analyzing the cause of differences between occupational insurers)
<Community medical insurer (Japan Health Insurance Association, National Health Insurance, Medical Union for the Elderly)>
[By disease x by community medical insurer x by disease risk stage x by user efforts x average implementation status]
・ Neoplasm (cancer) × by regional medical insurer × by user effort by disease risk stage × average implementation status ・ Stroke × by regional medical insurer × by user effort by disease risk stage × average implementation status ・ Acute myocardium Infarction x Regional Medical Insurer x Disease Risk Stage User Initiative x Average Implementation Status / Diabetes x Regional Medical Insurer x Disease Risk Stage User Initiative x Average Implementation Status / Visceral Chronic Disease x Regional Medical Insurance × By user effort by illness risk stage × Average implementation status / mental illness (depression, dementia) × By regional medical insurer × By illness risk stage user effort × By average implementation status / disease × By regional medical insurer × User attributes, etc. by group (index for analyzing the cause of differences between regional insurers)
The P index is the user's efforts (health promotion, disease prevention, exercise habits, eating habits, abstinence / alcohol saving, smoking cessation / smoking habits, various stress tolerance enhancement, etc. within the average duration for each disease risk stage calculated by the O index. Calculate and calculate the average amount of addiction (hobbies, etc.).

The average implementation amount corresponds to the average value during the period such as the number of times and days of use of various user efforts and the participation rate in various activities.

The above user efforts are to collect information as insurer-specific occupational or regional questionnaires or user questionnaire items, and incorporate them into the community-based comprehensive care analysis database platform to provide users of medical examination data and medical receipt data. Index aggregation is performed by matching with individual data.

The 7th P index mentioned above is a usage that is difficult for the user to control, which is partly included in the medical examination data such as the user's lifestyle, life philosophy, home environment, work environment, neighborhood environment, and economic situation. It is the data that aggregates the basic attributes of the insurer for each hierarchical group of the insurer. These may be useful for cause analysis between medical insurers who have the same user efforts but different effects.

S index (As described above, users whose O index is in the direction of deterioration and users who are in the direction of improvement are divided into a deterioration group and an improvement group, and the S index is obtained for each group).
<Work area medical insurer (Union Health Insurance, Mutual Aid Health Insurance)>
[By disease x Occupational medical insurer x Disease risk stage Hierarchical group efforts x Average implementation status]
・ Neobiology (cancer) × By occupational medical insurer × By disease risk stage Hierarchical group efforts × Average implementation status ・ Stroke × By occupational medical insurer × By disease risk stage Hierarchical group efforts × Average implementation status ・ Acute myocardium Infarction x Occupational Medical Insurer x Disease Risk Stage Hierarchical Group Initiative x Average Implementation Status / Diabetes x Occupational Medical Insurer x Disease Risk Stage Hierarchical Group Initiative x Average Implementation Status / Visceral Chronic Disease x Occupational Medical Insurer By x Disease Risk Stage Hierarchical Group Initiatives x Average Implementation Status / Mental Illness (Depression, Dementia) x Occupational Medical Insurers x Disease Risk Stage Hierarchical Group Initiatives x Average Implementation Status / Disease x Occupational Medical Insurers Different x Hierarchical group attributes by the same group (index for analyzing the cause of differences between regional medical insurers)
<Community medical insurer (Japan Health Insurance Association, National Health Insurance, Medical Union for the Elderly)>
[By disease x by community medical insurer x by disease risk stage, by hierarchical group efforts x average implementation status]
・ Neoplasm (cancer) × by regional medical insurer × by disease risk stage stratified group effort × average implementation status ・ Stroke × by regional medical insurer × by disease risk stage stratified group effort × average implementation status ・ Acute myocardium Infarction x Regional Medical Insurer x Disease Risk Stage Hierarchical Group Initiatives x Average Implementation Status / Diabetes x Regional Medical Insurer x Disease Risk Stage Hierarchical Group Initiatives x Average Implementation Status / Visceral Chronic Disease x Regional Medical Insurer By x Disease risk stage by hierarchical group effort x Average implementation status / mental disease (depression, dementia) x By regional medical insurer x By disease risk stage by hierarchical group effort x Average implementation status / by disease x Community medical insurer Different x Hierarchical group attributes by the same group (index for analyzing the cause of differences between regional medical insurers)
The S index is the insurer's efforts (health promotion, disease prevention, exercise habits, eating habits, abstinence / drinking and quitting / smoking habits, strengthening various stress tolerances, within the average duration for each disease risk stage calculated by the O index. Calculate and obtain the average amount of implementation (a device that promotes and supports hobbies that you can be absorbed in).

The average implementation amount corresponds to the average value during the period such as the number and frequency of implementation of each insurer's efforts, the recognition rate, participation rate, and implementation rate for various activities.

The above insurer's efforts collect information as insurer-specific related party questionnaire items or existing management items, incorporate them into the health management analysis database platform (tentative name), and users of medical examination data, medical receipt data, etc. Index aggregation is performed by matching with individual data.

In this way, the OPS index can be used for disease prevention and disease aggravation prevention other than long-term care, and objective and quantitative data analysis enables accurate problem extraction, and further measures. The effectiveness will be shown concretely, and it will be possible to accurately implement community-based comprehensive care projects using highly accurate indicators.

In the above, examples of the O index, the P index, and the S index have been shown separately for the long-term care system and the medical system. However, in reality, the mental and physical condition of the long-term care system (classification of long-term care status, degree of independence of dementia, degree of independence of disability, certification survey items such as ADL, etc.) and the mental and physical condition of medical system (blood test items, etc.) are parallel. In many cases, it progresses in a targeted manner. In addition, depending on the disease, the onset or progression of the disease may be the cause of long-term care.

The main relationship between the illness and the mental and physical condition of the long-term care system is as shown in FIG. 14 in the embodiment of the lower-level community-based comprehensive care business system described above, and duplicate explanations will be omitted.

Measures aimed at extending the length of stay at home through home medical care / long-term care cooperation are the same as those described in the above-mentioned embodiment of the lower-level community-based comprehensive care business system. In other words, the Ministry of Health, Labor and Welfare has significantly reduced the number of hospitalized patients with chronic diseases, mainly medical care beds, by 2025 or later as a drastic deterrent measure against the increase in medical expenses, and has a scale of several hundred thousand. Has announced plans to move patients to home care. Along with this, there is an urgent need to install home medical equipment, home-visit medical care, and the development of bases and human resources to provide home-visit nursing services, and it is also necessary to provide long-term care services in cooperation with these.

In addition, the Ministry of Health, Labor and Welfare has proposed the following eight measures that contribute to the promotion of home medical care / long-term care cooperation, as mentioned above, in the "Guide for Home Medical Care / Nursing Care Cooperation Promotion Project" issued in 2015. Is going.

(A) Understanding local medical / nursing resources (b) Identifying issues related to home medical / nursing cooperation and examining countermeasures (c) Promoting the construction of a seamless home medical / home care provision system (d) Medical care・ Support for information sharing of care-related persons (e) Consultation support for home medical care / nursing cooperation (f) Training for medical / nursing-related persons (g) Dissemination and enlightenment to local residents (h) Related cities for home medical care / nursing cooperation Cooperation between wards, towns and villages By finding and implementing effective measures in each of the above (a) to (c), the effect of home medical care / long-term care cooperation will be enhanced, and finally medical care by extending the period of stay at home (shortening the hospitalization period).・ It will lead to a significant reduction in nursing care benefit costs.

Differences in the length of stay at home (differences in hospitalization period) due to differences in home medical care / long-term care cooperation efforts are as explained in FIGS. 15A and 15B.

As can be seen from the figure, the inpatient medical benefit cost is very high compared to the total of the home medical benefit cost and the long-term care benefit cost. In other words, the longer the length of stay at home (the shorter the length of hospital stay), the more likely it is that medical expenses can be significantly reduced.

As shown in Graph 151 of FIG. 15A, when the measures related to home medical care / long-term care cooperation are enhanced for each subregion and the efforts of the medical side, the long-term care side, and the user side are activated, the period of stay at home is long. Therefore, it is possible to take care of the patient at home without having to be hospitalized.

On the other hand, as shown in Graph 153, when the measures related to home medical care / long-term care cooperation are insufficient for each subregion, and the medical care side, the long-term care side, and the user side are not taking measures or activating them. Without being able to realize home medical care, he was hospitalized with the onset of aggravation of the disease and was taken care of at the hospital as it was.

Graph 152 is a graph expressing a state in between of these. As can be seen from FIG. 15A, the lifetime medical / long-term care benefit cost for the user is the smallest in the case of graph 151, the largest in the case of graph 153, and the middle in the case of graph 152 among the three patterns. .. This is the same when the home medical care and hospitalization shown in FIG. 15B are repeated.

In this case, the length of stay at home becomes an O index due to the aggravation of the user's illness, and the content of cooperation between home medical care and long-term care services of the small area or long-term care establishment / home medical institution related to the user during the period of stay at home is P. As an index, the measures of small areas or nursing care establishments / home medical institutions during the period of stay at home (the above measures (a) to (ku)) are S indexes, and a consolidated OP / S index based on a combination of these. Will implement home medical care / long-term care cooperation, which is one of the comprehensive community care businesses.

A specific example of the OPS index in the above-mentioned home medical care / long-term care cooperation business will be described.

First, we will explain the aggregation conditions for this business.

[Aggregation target area]
Each home medical care / long-term care cooperation project: It is necessary to quantitatively carry out the PDCA cycle such as issues, effective measures, and monitoring for each of the above (a) to (c).

[Target person]
Certified person (certified person who has each illness and needs support / nursing care to receive home medical care)
[Aggregation unit]
Local governments There are multiple insurers involved, including long-term care insurers and medical insurers (National Health Insurance, late-stage elderly), when viewed on an individual basis. I think it would be appropriate to tabulate from personal indicators.

[Data source]
・ Long-term care certification data (common throughout Japan)
・ Nursing care receipt data (common throughout Japan)
・ Medical examination data (common throughout Japan, blood tests, diagnostic findings, interviews, etc.)
・ Medical receipt data (common throughout Japan)
・ Data on subregional efforts, etc. (Efforts related to all measures (A) to (K) above)
・ Data on long-term care establishment efforts, etc. (Of the measures (a) to (c) above, in particular, efforts related to home medical care and long-term care cooperation of long-term care establishments related to (a), (c), (d), and (f))
・ Data on home medical institution efforts, etc. (Of the measures (a) to (ku) above, in particular, efforts related to home medical care / nursing care cooperation of home medical institutions related to (a), (c), (d), and (f))
・ Data on medical care bed hospital efforts, etc. (Of the measures (a) to (ku) above, in particular, efforts related to home medical care / long-term care cooperation of home medical institutions related to (a), (c), (d), and (f))
・ Data on user efforts, etc. (Of the measures (a) to (k) above, in particular, efforts related to home medical care / nursing care cooperation of home medical institutions related to (e) and (g))
・ Data such as subregion attributes (questionnaire data unique to insurers, etc.)
・ Data such as user attributes by subregion (questionnaire data unique to insurers, etc.)
・ Attribute data of nursing care establishments, home medical institutions, etc. (questionnaire data unique to insurers, etc.)
・ Data such as user attributes of nursing care establishments and home medical institutions (questionnaire data unique to insurers, etc.)
Data such as subregional attributes are data such as population, population density, elderly ratio, major industrial population ratio, household composition, and geographical environment of subregions, and are basic attributes of subregions that are difficult to control by subregional efforts. And so on. Data such as user attributes by subregion is data that aggregates the basic attributes of users, etc., which are difficult to control by user efforts, for each subregion, such as the user's home environment, work environment, neighborhood environment, and economic conditions. Is. For each attribute data, the code that leads to good or bad is defined in consideration of the magnitude relationship.

As a result, subregional efforts and subregional user efforts may be useful for cause analysis between local governments that have the same but different effects.

In addition, the long-term care business affiliation data, home medical institution attribute data, etc. are basic attributes, etc. that are difficult to control by the efforts of the business establishment or institution, depending on the scale of each business establishment or institution, the type of corporation, and the like.

User attribute data by nursing care establishment, user attribute data by home medical institution, etc. are difficult to control by user efforts such as user's lifestyle, life philosophy, home environment, work environment, neighborhood environment and economic situation. This is data that aggregates the basic attributes of various users for each nursing care facility, home medical institution, etc.

As the data such as each of these attributes, the code that leads to good or bad is defined in consideration of the magnitude relationship.

As a result, it may be useful for cause analysis between local governments that have the same services, nursing care establishments, home medical institutions, etc. but have different effects.

As mentioned above, in the case of home medical care / long-term care cooperation, it can be seen that both analysis by subregion and analysis by long-term care establishment / home medical institution are required.

Specific examples of OPS indicators in the home medical care / long-term care cooperation business based on the above-mentioned aggregation conditions will be described. In the following, dementia and depression are included in cranial nerve diseases, and diabetes is included in gastrointestinal diseases.

O index [By disease x By municipality x Average length of stay at home (month)]
・ By cranial nerve system disease × by municipality × average length of stay at home (month)
・ Respiratory disease x municipality x average length of stay at home (month)
・ By digestive system disease × by municipality × average length of stay at home (month)
・ By urinary system disease × by municipality × average length of stay at home (month)
Here, the calculation start month of the average staying at home is the month when the aggravation of each disease occurs. Usually, due to the acute exacerbation of each disease, after inpatient treatment at an acute care hospital, the discharge month is assumed to be the start month following the discharge. In addition, the average length of stay at home shall be the period during which home medical care was provided from the start month to hospitalization, and if hospitalization is repeated, the period of home medical care provided during that period shall be added up.

For the above indicators, adjust the average duration by gender and age group. As the age classification, the early-stage elderly classification and the late-stage elderly classification will be adopted. The timing of determining the age category shall be determined from the age in the month when home medical care is started.

The continuous check method for the average length of stay at home in the small area where each user lives and the continuous check method for nursing care establishments and home medical institutions are the same as the establishment continuation check method described in FIG. Method shall be applied.

P index <user efforts>
[By disease x by local government x by user's efforts x average efforts (month)]
・ Cranial nerve system disease x local government x user effort x average effort status (month)
・ Respiratory disease x Municipality x User effort x Average effort status (Monday)
・ By gastrointestinal disease × by local government × by user's efforts × average efforts (month)
・ By urinary system disease × by local government × by user's efforts × average efforts (month)
<Home medical care / long-term care cooperation activities>
[By disease x by local government x by home medical care / long-term care cooperation activity x average provision status (month)]
・ By cranial nerve system disease × By local government × By home medical care / long-term care cooperation activity × Average provision status (Monday)
・ By respiratory disease × By local government × By home medical care / long-term care cooperation activity × Average provision status (Monday)
・ By digestive system disease × By local government × By home medical care / long-term care cooperation activity × Average provision status (Monday)
・ By urinary system disease × By local government × By home medical care / long-term care cooperation activity × Average provision status (Monday)
The P index is calculated by calculating the average amount of implementation related to user efforts that contribute to the extension of the home stay period and collaborative activities between nursing care establishments and home medical institutions in the average stay at home period calculated by the O index.

The average implementation amount corresponds to the average value during the period such as the number of times and days of use of various services and the participation rate in various activities.

Examples of user efforts by subregion include (e) consultation support regarding home medical care / long-term care cooperation and (g) dissemination and enlightenment to local residents among the above eight measures. Utilization rate and participation rate of insurers in dissemination and enlightenment activities are assumed.

In addition, the following are assumed as collaborative activities by nursing care establishments and home medical institutions.

・ The ratio of long-term care service staff meetings by user, the content of the meetings, and the attendance rate by job type (information gathered from long-term care receipt data, user questionnaires, and business establishment questionnaires)
・ Emergency response cooperation status between care workers and clinics during acute exacerbations by user (information gathered from nursing care receipt data, medical receipt data, etc.)
・ System usage status by business and disease in the multidisciplinary information sharing system by user (Daily log data of the system is aggregated as monthly data and collected as cooperation activity information)
The above user efforts are to collect information as questionnaires by small area peculiar to local governments, user questionnaire items, questionnaire items for nursing care establishments / home medical institutions, log aggregation data of multidisciplinary cooperation information sharing system, etc. Is incorporated into the community-based comprehensive care analysis database platform, and each user is matched with the data of each individual user to aggregate the indicators.

S index <Small area efforts>
[By disease x by local government x by subregional efforts x average efforts (month)]
・ Cranial nerve system disease x local government x subregional efforts x average efforts (month)
・ Respiratory disease x Municipality x Subregional efforts x Average efforts (Monday)
・ By gastrointestinal disease × by local government × by subregional efforts × average efforts (month)
・ By urinary system disease × by local government × by subregional efforts × average efforts (month)
<Home medical care / long-term care cooperation efforts>
[By illness x by local government x by home medical care / long-term care cooperation efforts x average efforts (month)]
・ By cranial nerve system disease × by local government × by home medical care / long-term care cooperation efforts × average efforts status (month)
・ Respiratory disease x Municipality x Home medical care / long-term care cooperation efforts x Average efforts (Monday)
・ By digestive system disease × By local government × By home medical care / long-term care cooperation efforts × Average efforts status (Monday)
・ By urinary system disease × By local government × By home medical care / long-term care cooperation efforts × Average efforts status (Monday)
Here, the S index is the efforts of each subregion (8 measures (A) to (K) above) and the efforts of nursing care establishments and home medical institutions (mainly the above) within the period of stay at home calculated by the O index. Calculated as the average value of (a), (c), (d), and (f) of the eight measures within the relevant duration, and aggregated by subregion or by nursing care establishment / home medical institution. Calculate and obtain the average implementation amount.

The efforts of the above subregions, long-term care establishments, home medical institutions, etc. are to collect information as questionnaire items or existing management items unique to local governments, and incorporate them into the community-based comprehensive care analysis database platform for various individual users. Index aggregation is performed by matching with the unit data.

Similar to the example of the community-based comprehensive care business system at the lower level explained in the first half of this specification, as another O index, in the case of small area analysis, [By disease × Small area × Average home stay rate ( %)], In the case of analysis of nursing care establishments / home medical institutions, it is also possible to adopt [by disease x by nursing care establishment / by home medical institution x average staying at home rate (%)].

In addition, without distinguishing by disease, as an O index, in the case of subregion analysis, [by subregion x average stay at home rate (%)], and in the case of nursing care establishment / home medical institution analysis, [by nursing care establishment]・ It is also possible to adopt [By home medical institution x average stay at home rate (%)].

The embodiment of the invention relating to home medical care / long-term care cooperation has been described above. As described above, in this embodiment, by analyzing and evaluating from two viewpoints, one by small area and the other by nursing care establishment / home medical institution, it is possible to comprehensively identify effective measures that contribute to the extension of the period of stay at home. It will be possible, and eventually the hospitalization period will be shortened, which will lead to the integration of medical care and long-term care, and the reduction of benefit costs.

Here, in promoting the community-based comprehensive care business, efforts for each individual, etc. are carried out at the local government or insurer level, but it is preferable to supervise these businesses at the higher prefecture or national level. For that purpose, higher-ranking organizations such as prefectures and countries need to make top-down comparisons between local governments and insurers. This mutual comparison (visualization) has the effect of inducing efforts to local governments and insurers.

The high-level community-based comprehensive care business system explained later in the specification of the present application uses this top-down method, with each insurer (in the case of home medical care, each local government) as the aggregation unit, and the consolidated OPS index. Since it is evaluated, it is possible to evaluate each insurer and each local government's efforts for community-based comprehensive care business quantitatively and correctly.

On the other hand, local governments and insurers themselves can understand their position in the whole country by the above-mentioned mutual comparison (visualization), but how to act to improve their position, high-level community comprehensive care business Specific measures cannot be seen only by evaluating the system.

In the lower-level community-based comprehensive care business system described in the first half of the specification of the present application, each local government and each insurer evaluates the businesses and subregions belonging to them as a total unit using a consolidated OPS index. Therefore, these businesses and subregions can be evaluated quantitatively and correctly. For this reason, it is possible to give specific and effective guidance to them and enhance their own evaluation nationwide.

From these facts, it is the best situation that both solutions of the comprehensive community care system at the local government and insurer level and the comprehensive community care system at the upper prefecture and national level are operated in parallel (applied in parallel). ..

Although some embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These novel embodiments can be implemented in various other embodiments, and various omissions, replacements, and changes can be made without departing from the gist of the invention. These embodiments and modifications thereof are included in the scope and gist of the invention, and are also included in the scope of the invention described in the claims and the equivalent scope thereof.

10 ... Database 11 ... Basic data 12 ... Nursing care insurance data 13 ... Medical insurance data 14 ... Measures and other specific data 16 ... Target business area 31 ... Client computer 311 ... Data acquisition department 32 ... Server computer 321 ... Database creation department 322 ... Index Calculation unit 3221 ... O index calculation unit 3222 ... P index calculation unit 3223 ... S index calculation unit

Claims (21)

Using a database that has basic data of residents, long-term care insurance data, medical insurance data, and unique data such as measures including measures implemented by local governments, the values of multiple indicators set in advance to achieve a predetermined measure can be set. It is a community-based comprehensive care business system that can be calculated for each predetermined aggregation unit.
As the index, a structure index including the implementation status of the measure of the implementer who implements the measure, a process index including the service provision status from the implementer to the user of the measure, and maintenance of the mental and physical condition of the user. Outcome indicators including time period are set,
From the aggregation point set in advance during a predetermined time period back check time, if the data representing the phase of the mental and physical states of the long-term care insurance data of the user has changed, the numerical values representing the duration until the change of the user With the outcome index calculation unit that calculates as the outcome index of the user,
The user uses the numerical value representing the performance of the service received from the implementer during the continuation period by using the insurance receipt data of the long-term care insurance data and the service provision data based on the medical receipt data of the medical insurance data. The process index calculation unit that calculates as the process index of the person,
A structure index calculation unit that calculates a numerical value representing the actual results of the measures taken by the implementer who provided the service to the user as a structure index from the data unique to the measures, etc.
Community comprehensive care business system with. Using a database that has basic data of residents, long-term care insurance data, medical insurance data, and unique data such as measures including measures implemented by local governments, the values of multiple indicators set in advance to achieve a predetermined measure can be set. It is a community-based comprehensive care business system that can be calculated for each predetermined aggregation unit.
As the index, a structure index including the implementation status of the measure of the implementer who implements the measure, a process index including the service provision status from the implementer to the user of the measure, and maintenance of the mental and physical condition of the user. Outcome indicators including time period are set,
From the aggregation point set in advance during a predetermined time period back check time, if the data representing the phase of the mental and physical states of the long-term care insurance data of the user has changed, the duration until the change of the user, this An outcome index calculation unit that aggregates the amount of change in the stage after the elapse of the continuation period and calculates a gradient indicating the degree of change in the continuation period as the outcome index of the user from the duration and the amount of change in the stage.
The user uses the numerical value representing the performance of the service received from the implementer during the continuation period by using the insurance receipt data of the long-term care insurance data and the service provision data based on the medical receipt data of the medical insurance data. The process index calculation unit that calculates as the process index of the person,
A structure index calculation unit that calculates a numerical value representing the actual results of the measures taken by the implementer who provided the service to the user as a structure index from the data unique to the measures, etc.
Community comprehensive care business system with. The outcome index calculation unit calculates the ratio of the users whose mental and physical condition has changed in the improvement direction to all the users who have changed in either the deterioration direction or the improvement direction as the improvement rate for each of the predetermined aggregation units. The community-based comprehensive care business system according to claim 1, which is characterized by having a function of performing. In the outcome index calculation unit, the process index calculation unit, and the structure index calculation unit, the outcome index is that the user whose mental and physical condition stage is changing in the worsening direction and the user whose mental and physical condition stage is changing in the improvement direction. If there is a problem, the community-based comprehensive care business system according to claim 1 or 2, which is divided into a deterioration group and an improvement group, and the index value of the aggregation unit is calculated for each group. The outcome index calculation unit has a plurality of items of the mental and physical condition, and when the outcome index value of each stage is calculated for each of the plurality of items of the mental and physical condition, the user belonging to the aggregation unit for each item of the mental and physical condition. Using the index value at each stage of the above and the number of records at each stage of this index value, a weighted average value obtained by summarizing all stages is obtained, and this weighted average value is used as an outcome index of the mental and physical condition item of the aggregation unit. The community-based comprehensive care business system according to any one of claims 1 to 4, characterized in that. When the process index calculation unit or the structure index calculation unit calculates the process index or the structure index based on the outcome index calculated for each stage of the plurality of items of the mental and physical condition by the outcome index calculation unit, the mental and physical index calculation unit or the structure index calculation unit may calculate the mental and physical condition. For each state item, the weighted average value obtained by aggregating all stages using the index value in each of the above stages and the number of records in each stage of these index values is obtained, and this weighted average value is used as the item of the mental and physical condition of the aggregation unit. The community-based comprehensive care business system according to any one of claims 1 to 4, characterized in that it is a process index or a structure index. Any of claims 1 to 6, wherein the outcome index calculation unit excludes the analysis if there is a change in the implementer who provides the service to the user during the continuation period. The community-based comprehensive care business system described in item 1. When the user is provided with services by a plurality of the implementers during the continuation period, the outcome index calculation unit is the implementer having the highest proportion of the users involved in the plurality of implementers. The community-based comprehensive care business system according to any one of claims 1 to 7, wherein if the ratio is equal to or higher than a preset threshold value, the user and the implementer are targeted for final analysis. .. The claim index calculation unit calculates, as the continuation period, the period from the preset starting point to the time when the stage of the mental and physical state changes to the next deterioration stage of the stage representing independence. The community-based comprehensive care business system described in item 1. When a change in the stage of the mental and physical condition occurs, the outcome index calculation unit sets a check period of a predetermined length before and after the time when the change occurs, and the presence or absence of the hospitalization event during the check period. The community-based comprehensive care business system according to claim 1, wherein is detected from the data of the medical receipt or the long-term care receipt of the user. The outcome index calculation unit is characterized in that when hospitalization occurs as a change in the stage of the mental and physical condition, the period of stay at home from the onset of aggravation of illness to the hospitalization is calculated as an outcome index. The community-based comprehensive care business system according to claim 1. When hospitalization and discharge occur as changes in the stage of the mental and physical condition, the outcome index calculation unit analyzes the total length of stay at home and the length of stay at home and the length of hospitalization until the hospitalization. The community-based comprehensive care business system according to claim 1, wherein the stay-at-home rate obtained from the target period is calculated as an outcome index. The community-based comprehensive care business system according to claim 1, wherein the outcome index calculation unit has a function of totaling the average benefit cost within the duration of the same stage of the physical and mental condition of an individual for each business establishment. The community-based comprehensive care business according to claim 1, wherein the outcome index calculation unit has a function of totaling the duration per unit amount of an individual obtained by dividing the duration by the average benefit cost for each business establishment. system. The area according to claim 1, wherein the outcome index calculation unit has a function of setting quantitative conditions that raise doubts in the accreditation survey and excluding those who meet these conditions from the aggregation target. Comprehensive care business system. The community-based comprehensive care business system according to any one of claims 1 to 15, wherein the mental and physical condition stage includes a disease risk stage. A totaling unit that calculates the average value by totaling the values of each individual index calculated by the outcome index calculation unit, the process index calculation unit, and the structure index calculation unit for each aggregation unit to which the user belongs. The community-based comprehensive care business system according to any one of claims 1 to 16, which has a separate aggregation unit and uses the calculated average value as an outcome index, a process index, and a structure index of the aggregation unit. The community-based comprehensive care business system according to any one of claims 1 to 17, wherein the outcome index has a correction unit that corrects the outcome index according to gender and age. The aggregation unit is a long-term care service establishment, a medical institution, a small area within the long-term care insurer, a stratified group with the occupational medical insurer at the top, a stratified group with the regional medical insurer at the top, the long-term care insurer, and the occupational area. The community-based comprehensive long-term care business system according to any one of claims 1 to 18, characterized in that the person is a medical insurer, a community medical insurer, or a local government. In addition to the index value, the outcome index has three elements as attributes: the type of service received by the user, the business establishment that provides the service to the user, and the mental and physical condition item stage of the user. Ori,
An outcome index element processing unit that performs processing including processing in which the three elements of the outcome index are aggregated within a predetermined range and the average value of the aggregated index values is used as the index value after aggregation.
In order to develop the three elements of the outcome index processed by the outcome index element processing unit in two dimensions, the element to be fixed is determined as a designated index element, and the element arranged on the vertical axis is used as the vertical axis index element. The vertical axis calculation element and the horizontal axis calculation element obtained by determining and deciding the element to be arranged on the horizontal axis as the horizontal axis index element and calculating each index value of these vertical axis index element and the horizontal axis index element are determined. The element 2D expansion part that generates the placed 2D expansion version outcome index, and
The vertical axis element outcome index consisting of each index value of the vertical axis index element and the horizontal axis element outcome consisting of each index value of the horizontal axis index element of the two-dimensional expansion version outcome index generated by this element two-dimensional expansion unit. An index, a chart creation unit that creates a chart using the vertical axis element calculated value index calculated from each index value arranged in these two dimensions, and a horizontal axis element calculated value index, and
The community-based comprehensive care business system according to claim 1, further comprising. The outcome index calculation unit extracts the maximum time, the minimum time, and the difference time for each item of the dendrogram model diagram, and the time extraction unit for each item of the dendrogram model diagram and the time extraction for each item of the dendrogram model diagram. Based on the maximum time, minimum time, and difference time for each item extracted by the department, it also has a severity prevention priority item selection function realized by the severity prevention item selection department that selects items that greatly contribute to prevention of severity. The community-based comprehensive care business system according to claim 1.

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