CN115458138A - DIP pre-grouping recommendation method, device, equipment and storage medium - Google Patents
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Abstract
The invention discloses a DIP pre-grouping recommendation method, device, equipment and storage medium. The method comprises the following steps: acquiring admission documents corresponding to patients; extracting category text information from the admission document; analyzing the category text information by using a preset knowledge graph module to obtain standard text information corresponding to the admission documents; and determining DIP pre-grouping recommendation information corresponding to the patient according to the standard text information. By the invention, before the diagnosis and treatment of the patient, a doctor can know the corresponding DIP group entry cost of the patient in advance, and the treatment scheme is adjusted according to the DIP group entry cost, so that the requirements of the hospital and the patient are met.
Description
Technical Field
The invention relates to the technical field of information processing, in particular to a DIP (local point method total budget and pay by disease score) pre-grouping recommendation method, a device, equipment and a storage medium.
Background
With the innovation of the employee's medical insurance system, the payment of DIP scores is continuously advanced. The payment of DIP value is a management mode of medical insurance settlement, and the DIP value determines the rationality of medical insurance settlement cost.
However, currently, DIP scores are determined after a patient is diagnosed, so that doctors cannot make a diagnosis plan meeting the requirements of hospitals and patients in advance. Furthermore, the diagnosis and treatment modes of the same disease can be various, and because a doctor cannot know the DIP value of the patient, the doctor can make subjective judgment on which diagnosis and treatment modes are specifically used in the treatment process, and the actual condition of the patient cannot be balanced. For example, thyroid nodule surgery includes conventional open surgery and laser minimally invasive surgery, and the DIP groups and costs for the two surgeries are certainly different, and if one surgery is selected at will, the payment for the achieved DIP score is not facilitated.
Disclosure of Invention
The invention mainly aims to provide a DIP pre-grouping recommendation method, a device, equipment and a storage medium, and aims to solve the problem that a doctor cannot make a treatment scheme meeting the requirements of a hospital and a patient in advance due to the fact that DIP values are determined after the patient is diagnosed.
In order to realize the technical problem, the invention is realized by the following technical scheme:
the embodiment of the invention provides a DIP pre-grouping recommendation method, which comprises the following steps: acquiring admission documents corresponding to patients; extracting category text information from the admission documents; analyzing the category text information by using a preset knowledge graph module to obtain standard text information corresponding to the admission documents; and determining DIP pre-grouping recommendation information corresponding to the patient according to the standard text information.
Wherein, in the canonical text information, the method includes: a diagnostic code and a surgical code for the patient; the DIP pre-grouping recommendation information comprises: a DIP pre-grouping cost corresponding to the patient; determining DIP pre-grouping recommendation information corresponding to the patient according to the standard text information, wherein the determining comprises the following steps: acquiring a DIP score table corresponding to a hospital where the patient is located; querying the DIP score table for a DIP entry component value that matches the patient's diagnostic code and surgical code; and determining the corresponding DIP pre-grouping cost of the patient according to the DIP grouping value.
Wherein, in the admission document, extracting category text information comprises: performing text recognition processing on the admission document to obtain the integral text information of the admission document; and extracting category text information from the overall text information by using a preset standard characteristic value in a standard document sample.
The analyzing the category text information by using a preset knowledge graph module to obtain the standard text information corresponding to the admission document comprises the following steps: carrying out field identification and field extraction on category text information corresponding to part of standard characteristic values by using a preset knowledge graph module, and correcting the extracted field information; and taking the corrected field information and the category text information which is not subjected to field extraction as the standard text information corresponding to the admission document.
The knowledge graph module is used for identifying the non-standard words in the field information and correcting the non-standard words in the field information into standard words.
The embodiment of the invention also provides a DIP pre-grouping recommendation device, which comprises: the acquisition module is used for acquiring admission documents corresponding to the patients; the extraction module is used for extracting category text information from the admission document; the analysis module is used for analyzing the category text information by utilizing a preset knowledge graph module to obtain the standard text information corresponding to the admission document; and the determining module is used for determining the DIP pre-grouping recommendation information corresponding to the patient according to the standard text information.
Wherein, in the specification text information, the following are included: a diagnostic code and a surgical code for the patient; the DIP pre-grouping recommendation information comprises: a DIP pre-grouping cost corresponding to the patient; the determining module is configured to: acquiring a DIP score table corresponding to a hospital where the patient is located; querying the DIP score table for a DIP entry component value that matches the patient's diagnostic code and surgical code; and determining the pre-grouping cost corresponding to the patient according to the DIP entry group value.
Wherein the extraction module is configured to: performing text recognition processing on the admission document to obtain the integral text information of the admission document; extracting category text information from the whole text information by using a standard characteristic value in a preset standard document sample; the analysis module is configured to: carrying out field identification and field extraction on category text information corresponding to part of standard characteristic values by using a preset knowledge graph module, and correcting the extracted field information; and taking the corrected field information and the category text information which is not subjected to field extraction as the standard text information corresponding to the admission document.
The embodiment of the invention also provides DIP pre-grouping recommendation equipment, which comprises a processor and a memory; the processor is configured to execute a DIP pre-grouping recommendation program stored in the memory to implement any of the DIP pre-grouping recommendation methods described above.
An embodiment of the present invention further provides a computer-readable storage medium storing one or more programs, which are executable by one or more processors to implement any of the DIP pre-grouping recommendation methods described above.
The invention has the following beneficial effects:
in the invention, a admission document corresponding to a patient is obtained; extracting category text information from the admission document; analyzing the category text information by using a preset knowledge graph module to obtain standard text information corresponding to the admission documents; and determining DIP pre-grouping recommendation information corresponding to the patient according to the standard text information. By the mode, before a patient diagnoses and treats, a doctor can know the corresponding DIP group entering cost of the patient in advance, and the treatment scheme is adjusted according to the DIP group entering cost, so that the requirements of a hospital and the patient are met.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a flow diagram of a DIP pre-grouping recommendation method according to an embodiment of the present invention;
fig. 2 is a block diagram of a DIP pre-grouping recommendation apparatus according to an embodiment of the present invention;
fig. 3 is a block diagram of a DIP pre-grouping recommendation device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments.
According to an embodiment of the invention, a DIP pre-grouping recommendation method is provided. Fig. 1 is a flowchart illustrating a DIP pre-grouping recommendation method according to an embodiment of the present invention.
And step S110, acquiring admission documents corresponding to the patients.
And the admission document is used for recording the patient information and the clinic information corresponding to the patient. The admission document can be an image file or an electronic text.
The contents of admission documents, including but not limited to: patient water flow number, patient name, patient admission time, and admission record text. The contents of the admission record text include but are not limited to: disease diagnosis and symptom description information.
Specifically, an EMR (Electronic Medical Record) of the hospital may be connected, and the admission text of the patient pushed by the EMR system is received after the admission document of the patient is entered in the EMR system.
And step S120, extracting category text information from the admission document.
The category text information refers to text items extracted from the case image.
Categories of category text information include, but are not limited to: patient water flow number, patient name, patient admission time, admission record text.
If the admission document is an electronic text, category text information can be directly extracted from the admission document.
If the admission document is an image file, text recognition processing can be performed on the admission document to obtain the whole text information of the admission document; and extracting category text information from the overall text information by using a preset standard characteristic value in a standard document sample.
The standard document sample is layout information of admission documents. Wherein, the standard document sample comprises: a plurality of standard feature values. Each standard feature value corresponds to a category text message. Each standard feature value is used to indicate the location of the corresponding category text information in the case document.
The text recognition process may be an OCR image recognition process.
And S130, analyzing the category text information by using a preset knowledge graph module to obtain the standard text information corresponding to the admission documents.
Carrying out field identification and field extraction on category text information corresponding to part of standard characteristic values by using a preset knowledge graph module, and correcting the extracted field information; and taking the corrected field information and the category text information which is not subjected to field extraction as the standard text information corresponding to the admission document. The category text information corresponding to the part of standard characteristic values is as follows: admission record text.
Further, the knowledge map module identifies disease diagnosis and disease description information in the admission record text, determines field information such as a disease code, a disease name, a diagnosis and treatment scheme, a surgery name, a surgery code and the like according to the disease diagnosis and disease description information, and corrects the field information. For example: and correcting the field information in the disease name and the diagnosis and treatment scheme. Further, the disease name, the diagnosis and treatment plan and/or the operation name may be extracted from the disease diagnosis and disease description information, and other information may be obtained by querying a preset information correspondence table, that is: the information corresponding table records the corresponding relation of the disease code, the disease name, the diagnosis and treatment scheme, the operation name and the operation code.
Further, the specification text information may be in the form of a table. Table fields include, but are not limited to: patient serial number, patient name, patient admission time, disease code, disease name, diagnosis and treatment scheme, operation name and operation code.
Further, a knowledge graph module is preset. The knowledge graph module is used for identifying the non-standard words in the field information and extracting the non-standard words in the field information into standard words. The knowledge-graph module may be obtained by pre-training. Further, the knowledge-graph module can be trained to obtain based on hospital data in combination with a Pubmed database.
In particular, there are a large number of synonyms or supernyms for medical terms. Further, the same symptom has a wide variety of textual expressions, such as: "extra-systole", "premature beat" and "premature beat" are synonymous. The same symptom is often modified by different words to express slightly different semantic meanings, such as: "acute back pain" and "chronic back pain" can be the lower words of "back pain". Currently, ICD (International classification of diseases) codes are largely adopted in medical diagnosis, but the ICD coding structure does not contain complete upper and lower relations. Taking the example of the Chinese ICD code [1] which refers to acute rheumatic heart disease, the supernumerary words of the Chinese ICD code refer to rheumatic heart disease and acute rheumatic heart disease, and the two diseases share the common supernumerary word of rheumatic heart disease and the supernumerary word of rheumatic heart disease. How to deal with the problems needs to unify the synonyms, the upper and lower terms through a knowledge graph module.
And step S140, determining DIP pre-grouping recommendation information corresponding to the patient according to the standard text information.
In the DIP pre-grouping recommendation information (which may be a table), at least one of the following is included: the system comprises a pre-grouping cost corresponding to a patient, a patient serial number, a patient name, patient admission time, a disease code, a disease name, a diagnosis and treatment scheme, an operation name and an operation code.
Acquiring a DIP score table corresponding to a hospital where the patient is located; querying the DIP score table for a DIP entry component value that matches the patient's diagnostic code and surgical code; and determining the corresponding DIP pre-grouping cost of the patient according to the DIP grouping value.
The DIP score table is used for recording the corresponding relation of the diagnosis code, the operation code and the DIP score.
DIP pre-grouping cost = DIP entry component value × average cost × hospital area coefficient × disease category coefficient. Wherein the average cost represents the cost of a unit score; the hospital area coefficient can be obtained by inquiring a preset hospital area coefficient table. The hospital area coefficient table is used for recording the area coefficient of the hospital where the patient is located. The disease type coefficient can be obtained by inquiring a preset disease type coefficient table. The disease category system table is used for recording disease category coefficients corresponding to disease names (or disease codes).
After the DIP pre-grouping expense is determined, at least one of the obtained patient serial number, the patient name, the patient admission time, the disease code, the disease name, the diagnosis and treatment scheme, the operation name and the operation code is combined with the DIP pre-grouping expense to form DIP pre-grouping recommendation information. And sending the DIP pre-grouping recommendation information to a preset display terminal for reference of a doctor. Therefore, the doctor can clearly know the DIP grouping cost of the patient under different diagnosis and treatment schemes, and can determine the satisfactory scheme of both academies and patients by integrating the patient demand opinions.
In this embodiment, a admission document corresponding to the patient is acquired; extracting category text information from the admission documents; analyzing the category text information by using a preset knowledge graph module to obtain standard text information corresponding to the admission documents; and determining DIP pre-grouping recommendation information corresponding to the patient according to the standard text information. By the mode, before a patient diagnoses and treats, a doctor can know the corresponding DIP group entry cost of the patient in advance, and adjust a treatment scheme according to the DIP group entry cost, so that the requirements of a hospital and the patient are met.
According to the invention, the acquisition of the hospitalization record diseases and symptoms of the patient and the recommendation of different diagnosis and treatment schemes based on the diseases and symptoms are realized by using the knowledge map, and then the DIP pre-grouping information is acquired according to the disease codes and the operation codes of different treatment schemes, so that the urgent need that a doctor wants to acquire the DIP grouping of the patient before the diagnosis and treatment actions occur is solved, the payment of the DIP value of the hospital is accelerated, and the three-win of the patient, the hospital and the medical insurance bureau is realized.
The embodiment of the invention also provides a DIP pre-grouping recommendation device. Fig. 2 is a block diagram of a DIP pre-grouping recommendation apparatus according to an embodiment of the present invention.
The DIP pre-grouping recommendation device comprises:
an obtaining module 210, configured to obtain admission documents corresponding to the patient.
And the extraction module 220 is used for extracting category text information from the admission document.
And the analysis module 230 is configured to analyze the category text information by using a preset knowledge graph module to obtain the specification text information corresponding to the admission document.
A determining module 240, configured to determine, according to the normative text information, DIP pre-grouping recommendation information corresponding to the patient.
Wherein, in the canonical text information, the method includes: a diagnostic code and a surgical code for the patient; the DIP pre-grouping recommendation information comprises: a DIP pre-grouping cost corresponding to the patient; the determining module 240 is configured to: acquiring a DIP score table corresponding to a hospital where the patient is located; querying the DIP score table for a DIP entry component value that matches the patient's diagnostic code and surgical code; and determining the pre-grouping cost corresponding to the patient according to the DIP grouping value.
Wherein the extracting module 220 is configured to: performing text recognition processing on the admission document to obtain the integral text information of the admission document; extracting category text information from the whole text information by using a standard characteristic value in a preset standard document sample; the analysis module is configured to: carrying out field identification and field extraction on category text information corresponding to part of standard characteristic values by using a preset knowledge graph module, and correcting the extracted field information; and taking the corrected field information and the category text information which is not subjected to field extraction as the standard text information corresponding to the admission document.
The knowledge graph module is used for identifying the non-standard words in the field information and correcting the non-standard words in the field information into standard words.
The functions of the apparatus according to the embodiments of the present invention have been described in the above method embodiments, so that reference may be made to the related descriptions in the foregoing embodiments for details which are not described in the present embodiment, and further details are not described herein.
The embodiment provides a DIP pre-grouping recommendation device. Fig. 3 is a block diagram of a DIP pre-grouping recommendation apparatus according to an embodiment of the present invention.
In this embodiment, the DIP pre-grouping recommendation device includes, but is not limited to: a processor 310, a memory 320.
The processor 310 is configured to execute a DIP pre-grouping recommendation program stored in the memory 820 to implement the DIP pre-grouping recommendation method.
Specifically, the processor 310 is configured to execute the DIP pre-grouping recommendation program stored in the memory 320 to implement the following steps: acquiring admission documents corresponding to patients; extracting category text information from the admission document; analyzing the category text information by using a preset knowledge graph module to obtain standard text information corresponding to the admission documents; and determining DIP pre-grouping recommendation information corresponding to the patient according to the standard text information.
Wherein, in the canonical text information, the method includes: a diagnostic code and a surgical code for the patient; the DIP pre-grouping recommendation information comprises: a DIP pre-grouping cost corresponding to the patient; determining DIP pre-grouping recommendation information corresponding to the patient according to the standard text information, wherein the determining comprises the following steps: acquiring a DIP score table corresponding to a hospital where the patient is located; querying the DIP score table for a DIP entry component value that matches the patient's diagnostic code and surgical code; and determining the corresponding DIP pre-grouping cost of the patient according to the DIP grouping value.
Wherein, in the admission document, extracting category text information comprises: performing text recognition processing on the admission document to obtain the integral text information of the admission document; and extracting category text information from the whole text information by using a preset standard characteristic value in a standard document sample.
The analyzing the category text information by using a preset knowledge graph module to obtain the standard text information corresponding to the admission document comprises the following steps: carrying out field identification and field extraction on category text information corresponding to part of standard characteristic values by using a preset knowledge graph module, and correcting the extracted field information; and taking the corrected field information and the category text information which is not subjected to field extraction as the standard text information corresponding to the admission documents.
The knowledge graph module is used for identifying the non-standard words in the field information and correcting the non-standard words in the field information into standard words.
The embodiment of the invention also provides a computer readable storage medium. The computer-readable storage medium herein stores one or more programs. Among other things, computer-readable storage media may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, a hard disk, or a solid state disk; the memory may also comprise a combination of memories of the kind described above.
One or more programs in a computer readable storage medium are executable by one or more processors to implement the DIP pre-grouping recommendation method described above.
Specifically, the processor is configured to execute a DIP pre-grouping recommendation program stored in the memory to implement the steps of: acquiring admission documents corresponding to patients; extracting category text information from the admission documents; analyzing the category text information by using a preset knowledge graph module to obtain standard text information corresponding to the admission documents; and determining DIP pre-grouping recommendation information corresponding to the patient according to the standard text information.
Wherein, in the canonical text information, the method includes: a diagnostic code and a surgical code for the patient; the DIP pre-grouping recommendation information comprises: a DIP pre-grouping cost corresponding to the patient; determining DIP pre-grouping recommendation information corresponding to the patient according to the standard text information, wherein the DIP pre-grouping recommendation information comprises the following steps: acquiring a DIP score table corresponding to a hospital where the patient is located; querying the DIP score table for a DIP entry component value that matches the patient's diagnostic code and surgical code; and determining the corresponding DIP pre-grouping expense of the patient according to the DIP grouping value.
Wherein, in the admission document, extracting category text information comprises: performing text recognition processing on the admission document to obtain the integral text information of the admission document; and extracting category text information from the whole text information by using a preset standard characteristic value in a standard document sample.
The analyzing the category text information by using a preset knowledge graph module to obtain the standard text information corresponding to the admission document comprises the following steps: carrying out field identification and field extraction on category text information corresponding to part of standard characteristic values by using a preset knowledge graph module, and correcting the extracted field information; and taking the corrected field information and the category text information which is not subjected to field extraction as the standard text information corresponding to the admission document.
The knowledge graph module is used for identifying the non-standard words in the field information and correcting the non-standard words in the field information into standard words.
The above description is only an example of the present invention, and is not intended to limit the present invention, and it is obvious to those skilled in the art that various modifications and variations can be made in the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (10)
1. A DIP pre-grouping recommendation method is characterized by comprising the following steps:
acquiring admission documents corresponding to patients;
extracting category text information from the admission document;
analyzing the category text information by using a preset knowledge graph module to obtain standard text information corresponding to the admission documents;
and determining DIP pre-grouping recommendation information corresponding to the patient according to the standard text information.
2. The method of claim 1,
in the specification text information, the following are included: a diagnostic code and a surgical code for the patient;
the DIP pre-grouping recommendation information comprises: a DIP pre-grouping cost corresponding to the patient;
determining DIP pre-grouping recommendation information corresponding to the patient according to the standard text information, wherein the determining comprises the following steps:
acquiring a DIP score table corresponding to a hospital where the patient is located;
querying the DIP score table for a DIP entry component value that matches the patient's diagnostic code and surgical code;
and determining the corresponding DIP pre-grouping expense of the patient according to the DIP grouping value.
3. The method of claim 1, wherein extracting category text information in the admission document comprises:
performing text recognition processing on the admission document to obtain the integral text information of the admission document;
and extracting category text information from the overall text information by using a preset standard characteristic value in a standard document sample.
4. The method according to claim 3, wherein the analyzing the category text information by using a preset knowledge graph module to obtain the specification text information corresponding to the admission document comprises:
performing field identification and field extraction on category text information corresponding to part of the standard characteristic values by using a preset knowledge graph module, and correcting the extracted field information;
and taking the corrected field information and the category text information which is not subjected to field extraction as the standard text information corresponding to the admission document.
5. The method according to any one of claims 1-4, wherein the knowledge-graph module is configured to identify and correct non-canonical terms in the field information to canonical terms.
6. A DIP pre-grouping recommendation device, comprising:
the acquisition module is used for acquiring admission documents corresponding to the patients;
the extraction module is used for extracting category text information from the admission document;
the analysis module is used for analyzing the category text information by utilizing a preset knowledge graph module to obtain the standard text information corresponding to the admission document;
and the determining module is used for determining DIP pre-grouping recommendation information corresponding to the patient according to the standard text information.
7. The apparatus of claim 6,
in the specification text information, the following are included: a diagnostic code and a surgical code for the patient;
the DIP pre-grouping recommendation information comprises: a DIP pre-grouping cost corresponding to the patient;
the determining module is configured to:
acquiring a DIP score table corresponding to a hospital where the patient is located;
querying the DIP score table for a DIP entry component value that matches the patient's diagnostic code and surgical code;
and determining the pre-grouping cost corresponding to the patient according to the DIP grouping value.
8. The apparatus of claim 6,
the extraction module is configured to: performing text recognition processing on the admission document to obtain the whole text information of the admission document; extracting category text information from the overall text information by using a standard characteristic value in a preset standard document sample;
the analysis module is configured to: carrying out field identification and field extraction on category text information corresponding to part of standard characteristic values by using a preset knowledge graph module, and correcting the extracted field information; and taking the corrected field information and the category text information which is not subjected to field extraction as the standard text information corresponding to the admission document.
9. The DIP pre-grouping recommendation device is characterized by comprising a processor and a memory; the processor is configured to execute a DIP pre-grouping recommendation program stored in the memory to implement the DIP pre-grouping recommendation method of any of claims 1-7.
10. A computer readable storage medium, storing one or more programs, the one or more programs being executable by one or more processors for performing the DIP pre-grouping recommendation method of any of claims 1-7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116798581A (en) * | 2023-06-13 | 2023-09-22 | 北京智诚民康信息技术有限公司 | System of DRG/DIP grouping method for inpatients based on clinical decision support |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116798581A (en) * | 2023-06-13 | 2023-09-22 | 北京智诚民康信息技术有限公司 | System of DRG/DIP grouping method for inpatients based on clinical decision support |
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