International Journal for Quality in Health Care 2012; Volume 24, Number 2: pp. 114– 120
Advance Access Publication: 24 January 2012
10.1093/intqhc/mzr088
Evaluation of a pilot surgical adverse event
detection system for Italian hospitals
CATERINA CAMINITI 1, FRANCESCA DIODATI 1, DONATELLA BACCHIERI 1, PAOLO CARBOGNANI 1,
PAOLO DEL RIO 1, ELISA IEZZI 1, DANTE PALLI 1, ISABELLA RABOINI 1, ERICAVECCHIONE 1
AND LUCA CISBANI 2
Address reprint requests to: Caterina Caminiti, Research and Innovation Unit, Parma Teaching Hospital, Italy. Tel: þ39-0521-1702138;
Fax: þ39-0521-1702277; E-mail: ccaminiti@ao.pr.it
Accepted for publication 28 December 2011
Abstract
Objective. To devise an adverse event (AE) detection system and assess its validity and utility.
Design. Observational, retrospective study.
Setting. Six public hospitals in Northern Italy including a Teaching Hospital.
Participants. Eligible cases were all patients with at least one admission to a surgical ward, over a 3-month period.
Interventions. Computerized screening of administrative data and review of flagged charts by an independent panel.
Main Outcome Measures. Number of records needed to identify an AE using this detection system.
Results. Out of the 3310 eligible cases, 436 (13%) were extracted by computerized screening. In addition, out of the 2874
unflagged cases, 77 randomly extracted records (3%) were added to the sample, to measure unidentified cases. Nursing staff
judged 108 of 504 (21%) charts positive for one or more criteria; surgeons confirmed the occurrence of AEs in 80 of 108
(74%) of these. Compared with random chart review, the number of cases needed to detect an AE, with the computerized
screening suggested by this study, was reduced by two-thirds, although sensitivity was low (41%).
Conclusions. This approach has the potential to allow the timely identification of AEs, enabling to quickly devise interventions. This detection system could be of true benefit for hospitals that intend assessing their AEs.
Keywords: adverse events, patient safety, quality improvement, quality management, safety indicators, surgery, teamwork,
audit, external quality assessment, medical errors
Introduction
Adverse events (AEs) represent a serious problem; they
interest nearly 1 out of 10 hospitalized patients and a substantial part of these is preventable [1]. It is increasingly
accepted that most medical errors are not due to ignorance
or negligence of a single individual, the so-called ‘person
approach’, but that the causes of errors must be searched for
within the system, towards which improvement interventions
should be directed (the ‘systems approach’) [2]. This latter
approach should be systematic, and should include the analysis of healthcare problems, selection of evidence-based
interventions involving all stakeholders, context analysis for
the identification of barriers and facilitators for change and
program testing and performance measuring [3, 4].
Different detection strategies have been reported in the literature, aimed at identifying AEs in hospitals. Thomas and
Petersen [5] examined as many as eight AE measurement
methods, specifying that these were only the most commonly
used out of a wide range of strategies applied in different settings. Each method had advantages and limits, suggesting
that the choice of which method to use should be determined by various factors, such as aims, context and resource
availability. Clinical documentation review seems to be the
most widely used method for AE identification in hospitals.
Despite its known limitations, chart review is very easy to
implement, it allows planning of data collection and, thus, is
generally favored by staff [6].
In most experiences, clinical records to be reviewed are
randomly selected [7 – 12], or all records from a given period
International Journal for Quality in Health Care vol. 24 no. 2
# The Author 2012. Published by Oxford University Press in association with the International Society for Quality in Health Care; all rights
reserved
114
Downloaded from http://intqhc.oxfordjournals.org/ at Biblioteca della Facolta' di economia, Universita' di Pavia on February 28, 2014
1
Research and Innovation Unit, Parma Teaching Hospital, Parma, Italy, and 2Emilia Romagna Regional Health Trust, Regional Health Care
and Social Agency, Clinical Governance, Bologna, Italy
Detecting adverse events †
are considered [13, 14]. In both strategies, without an initial
screening of cases with potential AEs, the number of
records to be examined in order to detect AEs must be very
high, making investigations extremely time consuming and
cumbersome. More recently, the use of indicators applied to
administrative data, to screen cases with potential AEs for
chart review, has been proposed [15].
This study intended to take advantage of both computerized screening methods and medical record review, with
three objectives:
The findings will determine whether this system could be
introduced into routine practice for timely detection of AEs,
allowing for the identification of corrective measures.
Methods
Definitions
The following definitions are provided in order to enable a comparison of the results with other international research data.
In this study, positively screened cases, or flagged cases,
are cases selected by computerized screening because they
exhibited one or more indicators. Unflagged cases are cases
that were not selected by computerized screening.
An AE was defined as an injury that was caused by
medical management (rather than the underlying disease)
and that prolonged hospitalization, produced a disability at
the time of discharge, or both. Causation is the degree to
which the reviewer is confident that the event was caused by
healthcare management, i.e. that an AE had occurred.
Preventable AEs were those that would not have occurred if
the patient had received ordinary standards of care, appropriate for the time of the study [6, 9]. For each AE found in
this study, the degree of confidence in preventability was
assigned on a six-point scale, and events were judged preventable if they received a confidence score of 4 or higher, as
indicated by the Harvard Study [9].
The reference standard indicates the clinical chart review
method used to confirm the occurrence of an AE. Findings
on the comparison between computerized screening and the
reference standard are described in terms of: sensitivity,
the ability to flag correctly the cases with an AE; specificity,
the ability to not flag correctly the cases without an AE;
positive predictive value (PPV), the proportion of flagged
cases that were also confirmed as having an AE; negative
predictive value (NPV), the proportion of unflagged cases
that were also confirmed as not having an AE.
System development
The study was conducted in six public hospitals within the
Northern Italian Provinces of Parma and Piacenza, which
volunteered to participate: the Parma Teaching Hospital (i.e.
a hospital with full-time core residency training programs in
medicine and surgery, 1334 beds), two Community Hospitals
in the Parma Province (293 and 118 beds) and three
Community Hospitals in the Piacenza Province (130, 178
and 524 beds). The geographical proximity of the two
Provinces facilitated the conduction of the study. Data collection took place at the coordinating center, at the Parma
Teaching Hospital. Since a large proportion of AEs occurs in
surgical wards—up to 60% according to different studies [1]
the investigation was limited to general surgical units.
The first phase of the study concerned the development
of a computerized method for the screening of discharged
summaries containing possible AEs. For this purpose, the
working group, made up of three surgeons, four nurses and
one epidemiologist, analyzed the literature to identify indicators which could be used to detect AEs in the hospital.
Three sets of indicators were found: those used in the
Harvard Study [9], the limited adverse occurrence screening
by Wolff [13] and the Patient Safety Indicators (PSIs) developed by AHRQ [16, 17]. Although the first two sets of indicators were designed to be applied to clinical records, the
latter is a screening tool for administrative data, the content
of the three sets often overlapped. After eliminating redundancies, a list of 29 possible indicators was completed, and
each item was reviewed and discussed by the working group,
resulting in the selection of seven indicators relevant to surgical patients and judged most suitable according to the discharge summary coding rules used at the participating
hospitals. The selected indicators are the following [13, 16]:
(i) Death—in-hospital death of surgical patients aged
,75 years.
(ii) Transfer to intensive care—surgical cases transferred from a general ward to the intensive care
unit during the same hospitalization.
(iii) Return to operating theater—surgical cases returning to the operating room within 7 days during the
same hospitalization.
(iv) Unplanned readmission—unplanned readmissions
for surgical cases within 28 days of discharge, also
to a different hospital.
(v) Length of stay (LOS)—surgical cases with LOS .
21 days.
(vi) Postoperative PE or DVT—cases of deep vein
thrombosis or pulmonary embolism in surgical
discharges.
(vii) Postoperative respiratory failure—cases of acute
respiratory failure in elective surgical discharges.
The chosen indicators were used for the development of a
computer program based on SAS software, by the Agenzia
Sanitaria e Sociale Regionale of the Emilia Romagna Region,
designed to screen discharge summaries to identify possible
AEs using ICD-9-CM diagnosis codes. A hospital discharge
case was flagged if at least one of the indicators scored positive based on the algorithms defined by their respective
authors [13, 16]. A file containing all flagged cases, and a
115
Downloaded from http://intqhc.oxfordjournals.org/ at Biblioteca della Facolta' di economia, Universita' di Pavia on February 28, 2014
(1) devise an AE detection system;
(2) assess its validity;
(3) assess its utility (to what extent computerized screening improves efficiency).
Patient Safety, Quality Measurement
Caminiti et al.
Table 1 k coefficient values for the measurement of
agreement between reviewers
K
Classification
95% CI
....................................................................................
Nursing interrater reliability
Nursing intrarater reliability
Medical interrater reliability
Medical intrarater reliability
0.59
0.75
0.50
0.86
Moderate
High
Moderate
Almost perfect
0.53 – 0.65
0.69 – 0.81
0.41 – 0.59
0.77 – 0.95
adopted in many subsequent studies of this kind, is appealing
as the selection by qualified nurses allows to save more costly
physician time, still enabling surgeons to look into the clinical
details for reasons and prevention strategies.
To avoid corporate bias, reviewers did not examine charts
of patients hospitalized at the institution where they operated,
and the names of the surgeons were deleted from the chart
photocopies used during the review.
Discharge summaries of all patients with at least one
admission in a surgical ward of the participating institutions,
during the index period (3 months), were included in the
investigation (eligible cases); no exclusion criteria were
applied, to ensure sample representativeness.
The review of flagged charts required a total of 215 h/
nurse and 96 h/physician; during each meeting, abstracted
records relative to patients discharged in the previous
2 weeks were reviewed.
To test reliability, calculated for the presence of AEs, 10%
of records was randomly selected and resubmitted 3 months
after the first review, both to the same reviewer (intrarater
reliability) and also to a different reviewer (interrater reliability). Reviewers were blinded to the outcome of the first
assessment. Overall, k coefficient values for the measurement of agreement between reviewers was good (Tables 1),
better than those recorded in studies using a similar methodology [10].
Statistical analysis
System evaluation
Findings of screening were validated using clinical records as
the reference standard. Charts abstracted by the centralized
software were reviewed in a two-stage process similar to the
one used by the Harvard Study: charts relative to flagged
cases were first reviewed by nurses of the Research Team,
who identified those containing potential safety-related
events. The selected records were then submitted to the
physician reviewers of the Research Team, who were asked to
confirm the presence of AEs, as well as to rate the degree of
causation and preventability of each event. To prevent diagnostic review bias, nursing and medical reviewers were
blinded to the output of computerized screening, i.e. they
did not know whether the charts they had reviewed had been
flagged as containing potential AEs or not. The two-stage
process, initially used in the Harvard Study [9] and then
116
Sample size
Because the paper is descriptive in nature, the sample size
was calculated based on practical considerations, since the literature provides no indications on the efficiency of a system
of this kind. The sample size was estimated considering an
expected accuracy of 0.75 and a minimal acceptable lower
confidence limit of 0.65 [18]; the necessary number of cases
(with confirmed AE) was 262. Assuming a prevalence value
for the study population between 5 and 10%, a total sample
of ≏4000 records, corresponding to 3 months of collection,
were necessary.
In addition to flagged records, to measure the proportion
of false negatives and estimate the total prevalence of AEs
( pretest probability), 3% of unflagged cases was abstracted at
the same time, stratifying by hospital.
Downloaded from http://intqhc.oxfordjournals.org/ at Biblioteca della Facolta' di economia, Universita' di Pavia on February 28, 2014
random sample of cases not identified by computerized
screening (see the ‘Sample size’ section) was sent to the
coordinating center for chart retrieval and review.
The second phase of the study was the identification of a
tool to be used for the two-stage (nursing and medical)
manual chart review for the confirmation of AEs, following
the methodology outlined by the Harvard Study [9]. The
working group decided to employ the instruments developed
by Michel et al. [6], which were kindly sent to us by the
author, translated into Italian and adapted to the Italian
healthcare system. The tool consisted in two questionnaires:
the detection sheet, to be used by nurses to judge the presence of possible AEs, consisting in 17 criteria, and the confirmation sheet, used by clinicians to confirm AEs and rate
the degree of causation and preventability of each event.
After defining the instruments for the study, the project’s
Research Team was created, consisting in the nurses and clinicians who would be responsible for the manual chart review.
The Research Team was made up of 10 nurses and 7 surgeons, including the healthcare professionals of the working
group, representative of all participating institutions. The
number of professionals was determined based on an estimate of the number of charts to be reviewed, and by the estimate of the time required for the chart review. The nurses
were employees of their institution’s administration offices,
and had experience of clinical record coding. The physicians
were highly motivated surgeons mainly operating in clinical
practice. A 3-day training program was provided to all
members of the Research Team, between December 2007
and January 2008, with the aim of ensuring uniformity in
judgment and minimize interpretation biases.
During the course, reviewers were introduced to the
project and to the tools adapted from Michel et al. [6].
Training included simulations of the chart review process,
both on an individual basis, with each person reviewing the
same chart before and after receiving instructions, and in
groups consisting in one nurse and one clinician, where each
group examined the same 10 charts followed by discussion
of results. For training, charts were selected relative to events
difficult to evaluate.
Detecting adverse events †
Patient Safety, Quality Measurement
Table 2 Characteristics of eligible cases compared with flagged, unflagged and confirmed cases
Total of
Flagged
eligible cases cases
3310 100% 436 13%
Unflagged
cases
2874 87%
Confirmed
by nurses
108 3%
Confirmed by
physicians
80
2%
.............................................................................................................................................................................
Local health area
Parma Teaching Hospital
Piacenza Community Hospitals
Parma Community Hospitals
Age
P25
P50 (median)
P75
LOS
P25
P50 (median)
P75
78% 271 62% 2299 80% 16
9% 65 15% 217
8% 5
14% 100 23% 358 12% 87
100%
100%
100%
1509 46% 250 57% 1584 55% 80
17%
6%
77%
100%
71%
27
7
14
16
30
7
2
5
25% 19
6% 5
12% 6
14% 9
28% 26
6% 3
2% 2
4% 10
100%
24%
6%
8%
12%
32%
4%
2%
12%
100%
703
449
413
380
356
179
165
665
21% 143 33%
14% 48 11%
12% 37
8%
11% 38
9%
11% 88 20%
5% 16
4%
5% 29
7%
20% 37
8%
100%
100%
578
397
370
343
277
162
137
610
57
71
81
56
70
78
57
72
82
61
71
78
57
70
78
2
5
11
4
9
18
2
5
10
8
13
24
8
15
23
Data analysis
Continuous data were reported as median and 25 – 75th IQ
range values. Dichotomous variables were compared by
two-sided x 2 test, for the evaluation of patient characteristics.
All performance parameters were determined inferentially,
deriving from the unflagged sample (3%) the information
relative to the total of unflagged cases. Point estimates and
95% confidence intervals for the measures of diagnostic
performance were determined.
SAS software release 8.2 was used for the statistical
analysis.
Results
Figure 1 depicts the study’s flow diagram. Discharge summaries of all patients hospitalized in surgical wards between
February and April 2008 were screened by the computer
program every 2 weeks.
Out of the 3310 eligible cases, 436 (13%) were abstracted.
Out of 2874 unflagged cases, 77 randomly abstracted
records (3%) were added to the sample to measure diagnostic performance. Missing data concerned 8 of 436 (2%)
flagged charts and 1 of 77 (1%) unflagged charts; they could
not be retrieved either because patients had been readmitted
20%
14%
13%
12%
10%
6%
5%
21%
100%
15% 14
4% 5
81% 61
100%
74% 57
and were currently hospitalized or because they still had not
been signed and archived. Nursing staff identified 108 of 504
charts (21%) as positive for one or more criteria and submitted these to medical review. Surgeons confirmed the occurrence of AEs in 80 charts, corresponding to 74% (80 of
108) of nurse-flagged cases and 16% (80 of 504) of all
reviewed records.
Demographic and clinical features of all eligible cases, of
flagged and unflagged cases, as well as of cases confirmed
by nurse and physician review are reported in Table 2.
Seventy-eight percent of patients under study were cases
from the Parma Teaching Hospital; 46% were male, with a
median age of 71 years, median LOS of 5 days. The most
common major diagnostic category (MDC) was digestive
system (MDC 6). Distribution of flagged cases suggested a
higher prevalence in the community hospitals, with respect
to the total number of eligible subjects in each institution.
Notably, 80% of cases confirmed by nurse review and 77%
of cases confirmed by physician review were concentrated in
the community hospitals.
The frequency of flagged cases for each individual indicator, the corresponding percentages of confirmed and preventable AEs after medical review, is given in Table 3. The
screening indicator ‘Return to operating theater’ was the
most frequent, but also the less predictive indicator (only
16% of flagged cases turned out to be confirmed AEs).
117
Downloaded from http://intqhc.oxfordjournals.org/ at Biblioteca della Facolta' di economia, Universita' di Pavia on February 28, 2014
Male sex
Most frequent major diagnostic categories
Digestive system (MDC 6)
Kidney and urinary tract (MDC 11)
Circulatory system (MDC 5)
Respiratory system (MDC 4)
Hepatobiliary system and pancreas (MDC 7)
Male reproductive system (MDC 12)
Skin, subcutaneous tissue and breast (MDC 9)
OTHER
2570
282
458
Caminiti et al.
Table 3 Distribution of flagged cases, confirmed and preventable AEs for each indicator
Flagged cases
Confirmed AEs
Preventable AEs
.............................................................................................................................................................................
Death
Postoperative respiratory failure
Transfer to intensive care
LOS . 21 days
Unplanned readmission
Return to operating theater
Postoperative PE or DVT
12
9
56
131
11
264
5
2%
2%
11%
27%
2%
54%
1%
7
6
28
39
2
42
3
58%
67%
50%
30%
18%
16%
60%
2
3
3
6
0
5
1
29%
50%
11%
15%
0%
12%
33%
The same case may be reported more than once being eligible for more than one indicator; similarly, the same AE may be linked to more
than one indicator.
The data of chart review on AE frequency, preventability
and outcome, relative to flagged and unflagged cases are
shown in Table 4. The frequency of AEs was 18% (77 of
428) for flagged cases and 4% (3 of 76) for unflagged cases.
Out of 88 AEs confirmed by surgeons in the flagged
case-mix, 17% was judged preventable (15 of 88) vs.
one-third in the unflagged case-mix (33%).
Concerning the outcome in flagged cases, 22% of AEs
caused disability at discharge, 49% prolonged LOS, 14%
influenced survival, and 8% caused death. AEs found in
unflagged cases consisted in hematoma, bleeding and
intense pain.
118
Validity
The diagnostic performance of computerized screening for
the detection of AEs is detailed in Table 5. Sensitivity was
41%, specificity was 89%, PPV was 18% and NPV was
96%. Overall, the screening accuracy (true positives þ true
negatives over the total of cases) was 86%.
Efficiency
Computerized screening reduced by two-thirds the number
of charts to be reviewed to detect an AE compared with
Downloaded from http://intqhc.oxfordjournals.org/ at Biblioteca della Facolta' di economia, Universita' di Pavia on February 28, 2014
Figure 1 Flow diagram. Numbers of medical records are shown.
Detecting adverse events †
Table 4 AEs, preventability and outcome
Reviewed charts
Flagged Unflagged
random
samplea
....................................................................................
a
3% of unflagged cases was randomly abstracted.
Table 5 Diagnostic performance—for
unflagged cases inferential data were used
estimates
of
Cases with AEs Cases without AEs
....................................................................................
Flagged
77
Unflagged 112 (3)
189 (80)
Sensitivity
Specificity
PPV
NPV
41%
89%
18%
96%
351
2725 (73)
3076 (424)
95% CI
34%
87%
14%
95%
428
2837 (76)
3265 (504)
48%
90%
22%
97%
The sample actually analyzed, equal to 3% of unflagged cases, is
given in parentheses.
random sampling. This value was derived comparing AE
prevalence in the total of eligible cases (6%) and the frequency of AEs in flagged cases (18%). Specifically, without
computerized screening, physicians would have had to review
17 medical records to detect one AE, while with the screening, physicians needed to review only 6 medical records to
find an AE.
Discussion
The detection system used in this study improved efficiency
in locating potential AE cases, enabling physicians to quickly
converge on the true AE cases to find causes and potential
strategies of correction. In fact, to detect an AE, the number
of charts to be reviewed was reduced by two-thirds.
Unfortunately, only 41% of AEs was captured ( probably the
most serious ones) implying the risk of underestimating
problems and the need to adopt interventions. It should be
emphasized, however, that work aimed at the refinement of
surgical AE screening indicators is ongoing [19], and the
computerized method will be gradually updated, thus
improving its performance.
Although this study was not designed to measure validity
and utility of each single indicator, our data suggested that
the indicator ‘LOS . 21 days’ worked best, as it captured the
highest number of confirmed and preventable AEs, requiring
the lowest number of charts. Moreover, this indicator exhibited a highly significant association with the other indicators,
i.e. frequently, cases flagged for this indicator were also
flagged for other indicators. A curious and unexpected
finding was the difference in the rate of confirmed cases
among the hospitals; specifically, the highest concentration of
AEs was found in one community hospital. This finding
suggests a potential use of the system as a tool for the prioritization of corrective interventions when applied to multiple
hospitals, for example, in a given geographical area.
To our knowledge, this is the first research investigating
the importance of AEs in an Italian setting. With respect to
other international research published in this field, our study
is mainly characterized by two aspects. It is the product of
the cooperation between representatives from the management, medical and nursing staff, who worked together from
the initial stages of the protocol development all through its
conduction. This is, we believe, a very important fact which
should favor the systematic use of this system in routine
practice. Also, unlike most studies on AE detection using
administrative data, which only refer to PSIs and generally
measure the validity of individual indicators, our research
tested the advantages of both computerized screening and
manual review, using a set of indicators derived from different experiences. However, our research reflects the findings
obtained by other PSI validation studies, which suggest a
generally high specificity and modest sensitivity. In particular,
two studies similar to ours have been published, by Romano
et al. [19], and by Kaafarani and Rosen [20], who have examined the use of these sorts of screening approaches using
surgical AHRQ PSIs. Unlike our research, however, both
papers consider selected PSIs individually, and do not
combine individual values. Sensitivities found in these studies
range from 19 to 63%, according to the PSI, against our
overall value of 41%. Specificities reported by Romano et al.
[19] range from 99.1 to 99.9%, higher than the overall value
obtained in our study.
This work has a few limitations, as evidenced in other
similar studies. First, its findings cannot be totally reproduced, as chart review implies some degree of subjective
judgment. Moreover, the sample is relatively small, and
limited to a restricted geographical area, therefore, direct
inferences cannot be made for other settings. Finally, the use
of administrative data for screening may lead to underestimation of a problem, because the ascertainment of AEs
depends on the quality and completeness of coding, and
only events for which there are corresponding ICD-9-CM
codes can be found [17].
Despite these limitations, this study suggests that this
detection system offers a true benefit for hospitals which
intend to assess their AEs.
119
Downloaded from http://intqhc.oxfordjournals.org/ at Biblioteca della Facolta' di economia, Universita' di Pavia on February 28, 2014
Fq (% no. of records)
No. of records
428 (100) 76 (100)
No. of patients with at least one AE
77 (18)
3 (4)
No. of AEs
88 (21)
3 (4)
No. of preventable AEs
15 (4)
1 (1)
Outcome
Fq (% no. of AEs)
The clinical event caused disability
19 (22)
0 (0)
The clinical event prolonged LOS
43 (49)
1 (33)
The clinical event influenced survival 12 (14)
0 (0)
The clinical event caused death
7 (8)
0 (0)
Patient Safety, Quality Measurement
Caminiti et al.
Acknowledgements
Funding
This work was supported by the Emilia-Romagna Regional
Health Trust.
References
1. de Vries EN, Ramrattan MA, Smorenburg SM et al. The incidence and nature of in-hospital adverse events: a systematic
review. Qual Saf Health Care 2008;17:216–23.
2. Dankelman J, Grimbergen CA. Systems approach to reduce
errors in surgery. Surg Endosc 2005;19:1017 –21.
3. van Bokhoven MA, Kok G, van der Weijden T. Designing a
quality improvement intervention: a systematic approach. Qual
Saf Health Care 2003;12:215–20.
4. Pronovost PJ, Berenholtz SM, Needham DM. Translating evidence into practice: a model for large scale knowledge translation. Br Med J 2008;337:a1714.
5. Thomas EJ, Petersen LA. Measuring errors and adverse events
in health care. J Gen Intern Med 2003;18:61–7.
6. Michel P, Quenon JL, de Sarasqueta AM et al. Comparison of
three methods for estimating rates of adverse events and rates
of preventable adverse events in acute care hospitals. Br Med J
2004;328:199 –202.
7. Thomas EJ, Studdert DM, Burstin HR et al. Incidence and
types of adverse events and negligent care in Utah and
Colorado. Med Care 2000;38:261– 71.
8. Vincent C, Neale G, Woloshynowych M. Adverse events in
British hospitals: preliminary retrospective record review. Br
Med J 2001;322:517 –9.
120
10. Baker GR, Norton PG, Flintoft V et al. The Canadian Adverse
Events Study: the incidence of adverse events among hospital
patients in Canada. CMAJ 2004;170:1678 –86.
11. Zegers M, de Bruijne MC, Wagner C et al. Design of a retrospective patient record study on the occurrence of adverse
events among patients in Dutch hospitals. BMC Health Serv Res
2007;7:27.
12. Soop M, Fryksmark U, Köster M et al. The incidence of
adverse events in Swedish hospitals: a retrospective medical
record review study. Int J Qual Health Care 2009;21:285 –91.
13. Wolff AM. Limited adverse occurrence screening: using medical
record review to reduce hospital adverse patient events. Med J
Aust 1996;164:458 –61.
14. Wolff AM, Bourke J, Campbell IA et al. Detecting and reducing
hospital adverse events: outcomes of the Wimmera clinical risk
management program. Med J Aust 2001;174:621 –5.
15. Zhan C, Miller MR. Administrative data based patient safety research: a critical review. Qual Saf Health Care 2003;12(Suppl
2):ii58 –63.
16. Agency for Healthcare Research and Quality. AHRQ Quality
Indicators—Patient Safety Indicators: Software Documentation.
Rockville, MD: Agency for Healthcare Research and Quality,
2002. http://www.qualityindicators.ahrq.gov/psi_overview.htm
(August 2010, date last accessed).
17. Romano PS, Geppert JJ, Davies S et al. A national profile of
patient safety in U.S. hospitals. Health Aff (Millwood)
2003;22:154 –66.
18. Flahault A, Cadilhac M, Thomas G. Sample size calculation
should be performed for design accuracy in diagnostic test
studies. J Clin Epidemiol 2005;58:859–62.
19. Romano PS, Mull HJ, Rivard PE et al. Validity of selected
AHRQ patient safety indicators based on VA National Surgical
Quality Improvement Program data. Health Serv Res
2009;44:182 –204.
20. Kaafarani HMA, Rosen AK. Using administrative data to
identify surgical adverse events: an introduction to the
Patient Safety Indicators. Am J Surg 2009;198(Suppl
5):S63 –8.
Downloaded from http://intqhc.oxfordjournals.org/ at Biblioteca della Facolta' di economia, Universita' di Pavia on February 28, 2014
We thank the medical and nursing reviewers, Alessandra
Bardiani, Barbara Benoldi, Stefania Bertocchi, Maria Cristina
Cornelli, Guglielmo Delfanti, Paolo De Tullio, Carlo Maggi,
Emilio Marchionni, Libera Notarangelo, Chiara Rocchetta,
Michela Squeri, Angela Villa, Pietro Vitolo and Mario
Zecchinato, for their invaluable contribution to the study. We
are grateful to the medical managements of the participating
institutions for their precious support. Special thanks go to Dr
Maria Elisabeth Street, for her linguistic revision of the paper.
9. Brennan TA, Leape LL, Laird NM et al. Harvard Medical
Practice Study I. Incidence of adverse events and negligence
in hospitalized patients: results of the Harvard
Medical Practice Study I. 1991. Qual Saf Health Care
2004;13:145–51.