Essay: Reviewing medical records to improve patient safety in hospitals

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Reviewing medical records to improve patient safety in hospitals, are the methods evidence based?
Introduction: Infliction of unintended medical harm received increased attention. Increasing numbers of hospitals evaluate medical records of patients who died during their stay. This is labour-intensive and expensive. Information about adequacy of screening tools used for detecting adverse events (AEs) is therefore important.
Methods: We searched the literature for evidence concerning these screening methods based on the World Health Organisation (WHO) criteria for evaluating screening programs. Results are reported separately for the trigger tool of the Institute for Healthcare Improvement (IHI) and the one developed in the Harvard Medical Practice Study (HMPS).
Results: 4353 studies were identified. After selection 57 studies with relevant information about these tests remained. The reduction in prevalence of adverse events (AE) was between 0.5%-3.5% per year. For the IHI method the specificity ranged between 93%-100%, and the sensitivity between 34%-95%. Average positive predictive value (PPV) of the trigger systems was 38%. The Kappa for agreement between doctors on the presence of an AE ranged between fair and substantial in the HMPS studies and between fair and almost perfect with IHI triggers. Average costs of an AE were €8739.
Conclusion: Retrospective medical record review has not been extensively evaluated especially not according to the WHO criteria for screening programs. There is a lack of information on improvement in AE occurrence, cost-effectiveness and the predictive value of the triggers. We recommend policy makers to scientifically evaluate the chosen methods for their utility and cost effectiveness with improvement of care as the end goal.
Unintentional medical harm inflicted on patients in hospitals received increased attention during the past years. Several studies have shown significant rates of adverse events (AEs) that harm patients during their hospital stay.1-3 Therefore, interest for implementing safety- and quality programs has grown. Avoiding medical injury during hospital admission and improving patient safety therefore has high priority in hospitals, for healthcare inspection and for the government.4 According to the report ‘to Err is Human’ of the Institute of Medicine, at least 44,000 people (and perhaps even 96,000) die each year in hospitals in the US as a result of medical errors that could have possibly been prevented. Fifteen years after the initial report, a recent update stresses the importance of keeping focus on improving patient safety.3 In the Netherlands, a report by NIVEL (Dutch institute for research in healthcare) in which hospital medical records were evaluated on care related harm, increased the need for transparency and responsibility about this subject. They estimated that in Dutch hospitals every year about 1700 patients (4.1% of the total number of deaths in hospitals) die because of unintentional, but avoidable, harm. Six years after this initial report, which was published in 2007, follow up showed improvement but, 2.6% of the total number of deaths in hospital still occurred because of unintentional but preventable harm. In other countries an incidence between 2.5% and 11.5% was found.5-9 Several reasons are thought to contribute to the risk of these AEs. First, the increasing number of elderly patients with severe comorbidity and growing medical technical possibilities to carry out more complex treatments and interventions, result in more risks. Second, due to this expansion in treatment options the estimation of the possible health benefits is more complex. This could lead to treating more patients in which the predicted outcome is unclear, especially in the elderly and newborns with substantial comorbidities. Furthermore, social changes as part-time working and stricter adherence to prescribed working hours have their influence on the expertise and experience of medical professionals and the continuity of care. Teamwork and integrated care are therefore necessary, but they depend on good communication within and between teams which is frequently suboptimal. Also economic limitations and cuts put health-care under pressure.10-14 All of these factors are thought to influence patient safety.
Reducing unintentional but preventable harm is important for hospitals and the methods used for detection are diverse. In 1991 the Harvard Medical Practice Study provided us with a powerful tool to identify cases with possible AE.15 Another method which is often used in hospitals worldwide to select cases with possible AEs is the global trigger tool (GTT), developed by the Institute for Healthcare Improvement (IHI).16 This method is found to be labour-intensive and expensive when used for reviewing all deaths in the hospital. Information about sensitivity, specificity, positive and negative predictive values of this screening tool for detecting AEs are therefore important. With this information we can potentially determine the costs for every detected AE. However, detecting AEs is of course not the ultimate goal. Rather, the procedure aims to avoid harm to patients and even to save lives. So, adequately detecting AEs is only a small part of the whole process, which also involves feedback to the medical departments, adjustments in the delivery of care and hence improved outcome for patients. From this viewpoint, searching for AEs is actually a screening method in which the AE is the disease for which early intervention should improve the outcome.
Therefore we wondered whether these screening methods are evidence based according to the WHO criteria (box 1) for evaluating screening programs.17 Although these criteria where initially developed for the evaluation of screening programs concerning for example the early detection of breast malignancies or colon cancer we think they are applicable to other screening programs as well. Therefore, we searched the literature for evidence concerning the use of these trigger tools and chart review methods to identify AEs and subsequently improving patient outcome, using these WHO criteria.
Box 1: 7 criteria for evaluating screening programs:
Data collection
For every criterion (Box 1) we performed a literature search in Pubmed, Embase and the Cochrane library. An overview of the search terms we used per WHO criterion is provided in table 1. We selected articles published between January 2000 and February 2016. Reviews were excluded as well as posters, comments, studies that did not concern a hospital setting and studies about adverse drug events.
Abstracts were reviewed by DK. Full articles were assessed by DK and RR. The following data were extracted if provided by the authors: Absolute number of cases reviewed, number of (preventable) AEs, sensitivity, specificity, positive and negative predictive value (PPV and NPV), reproducibility of the methods used, severity of AEs as well as costs. These variables were expressed as a mean with confidence intervals (CIs) when possible. We report all results for each of the WHO steps separately. Costs were not corrected for inflation. Different currencies were transformed to euros to make comparison easier. The exchange rate from June 2016 was used.
The number of studies generated by every part of our search is shown in table 2. Our search provided a total of 4353 citations, of which 4334 were discarded after title and/or abstract screening, 19 studies remained for further evaluation. After reading the references in these studies we found 38 additional studies. Some studies were relevant for more than 1 step. Below, we report the results step by step.
Step 1: Our search revealed 3 suitable studies concerning an improvement in end results (table 3). In 2001, Wolff et al published about the AE rate over a certain time period concerning inpatients and patients at the emergency department.18 The absolute risk reduction for inpatients to suffer an AE was 0.61% (in a period of 8 years, on average 0.08% per year). For patients in the emergency department this was 2.78% (in a period of 2 years, on average 1.4% per year). In data from the Baylor Health Care System, Kennerly et al (2013) showed a 7% reduction in AEs in a period of 2 years (on average 3.5% per year).19 In a more recent study Suarez et al found during a 6-year study period a decrease in absolute risk for suffering an AE of 2.5% (on average 0,4% per year).20
Step 2: Our search revealed 32 suitable studies concerning the effectiveness of the components of the multiphasic screening process. Studies were available on the trigger tool and the AE assessment strategy. We found no studies specifically addressing the result of feedback to the medical departments and the change in the rate of AEs related to this.
2.1 Trigger tool
Briefly, we distinguished two different trigger tools that were often used: the trigger tool originating from the Harvard medical practice study15,21 (HMPS) with 18 triggers and the GTT22 (IHI) with 54 triggers. Only a few studies23-25 use a different set of triggers that were not used in any other study we found. When looking at those using the HMPS triggers (figure 1), the positive predictive value (PPV), for identifying cases with AEs varied between 17 and 68% with an average of 33% (95% CI 22-44).5,7,26-35
The agreement between the nurses on the presence of a trigger, showed a kappa value (K) between 0.53 and 0.76 (moderate to substantial agreement)36 and an average K of 0.65 (95%CI 0.50-0.80).29,34
The IHI method (figure 1) showed a PPV between 16 and 99%, with an average of 43% (95% CI 15-71).35,37-42 There was only one study that calculated the NPV and this was 99%.37 The K on the presence of a trigger varied between 0.2039 (slight agreement) and 0.7843 (substantial agreement), which corresponds with an average of 0.57 95% CI 0.15-0.99).2,39-41,43
Studies using other trigger tools have a PPV range between 39 and 96%, with an average of 54% (95%CI 25-84).24,25,44-46 Only two studies reported a K indicating the reproducibility of the trigger system. The K varied between 0,52 and 0,68.45,47
The observed variation in these numbers was not related to the number of cases that were investigated. There was also no association with the year a study was performed.
2.2 The AE assessment strategy (figure 2)
AE assessment in HMPS studies:
The K between medical doctors on the presence of an AE was on average 0.55 (moderate agreement, 95%CI 0.31-0.80).27,29-33,48
AE assessments in GTT studies:
Within the studies using the IHI trigger tool the K on the presence of an AE varied between 0.32 and 0.93, with an average of 0.64 (good agreement, 95%CI 0.42-0.86). The agreement on the severity of the AE was investigated in three studies, which showed an average K of 0.40 (fair agreement, 95%CI 0.06-0.73).1,2,19,38,39,41,43,49,50
AE assessments in other trigger systems:
Within studies using another trigger system, the K on the presence of an AE varied between 0.35 and 0.71, with an average of 0.38(fair agreement, 95%CI 0.15-0.62).47,51,52
Step 3:
Our search did not reveal any studies addressing this question.
Step 4:
We found 13 suitable studies concerning the costs-effectiveness or cost-benefit of the screening. The costs of an AE range between €800 (2013) and €51,804 (2007) in these studies with an average of €8739 (studies between 2001 and 2015). For preventable AE these numbers are between €3083 (2014) and €101,620 (2007) with an average of €36,0201.7,28,53-63 1(1 euro = 1.13 US dollar June 2016)
Step 5:
66 studies were identified that match our in and exclusion criteria. In figure 3 the results are shown according to the trigger tool used, with averages for every separate harm category.1,2,31,35,38,39,64-70. The definition for the different harm categories is as follows;
E: temporary harm to the patient and required intervention. F: temporary harm to the patient and required initial or prolonged hospitalization. G: permanent patient harm. H: intervention necessary to sustain life. I: death.71
Other studies use an alternative approach for detecting AEs like morbidity and mortality conferences, autopsy, malpractice claims analysis, error reporting systems, clinical surveillance etc.72 All these approaches have some advantages compared to our method but also considerable disadvantages.
Step 6:
For the IHI method the specificity ranged between 93% and 100%, and the sensitivity between 34% and 95%.38,67 We found no evidence for cost effectiveness and acceptability regarding this kind of screening in our search.
Step 7:
Preventable medical errors in hospitals have been estimated to result in costs between €16 billion and €37 billion a year nationwide in the United States.
Beyond the cost in human lives, preventable AEs also have effects on other costs. These costs include the expenses of necessary additional care, loss of income and household productivity of the patients, and disability.3
Goodman et al (2011) estimated the annual age-adjusted social costs based on the economic value of a life combined with the estimated annual number of deaths from adverse medical events. It was assumed that hospital injuries were comparable to workplace injuries to estimate the magnitude of the loss caused by these AEs. The social cost of all inpatient AEs (in US) was between 357 and 871 billion euros per year.73 In comparison: total social costs of traffic accidents in the US were estimated around 733 billion euros in 2010.74
Discussion & conclusion
With this extended literature search we found many studies addressing quality and safety strategies in health care. Remarkably we found little evidence for validity of the methods used. Furthermore, there was almost no research concerning the cost- and cost-effectiveness of retrospective medical record reviewing in these studies.
Concerning the first step we found only a few studies that report improvement in end-results. However, in our opinion the end goal of screening for AEs is first to detect them reliably and then to strive for a lower rate of preventable harm resulting in increased safety. Although much effort was put into detecting AEs there is little evidence in the literature for effective implementation of improvements to prevent them in the future. Moreover, Wolf et al (1996) showed that over a course of three years, the number of AEs decreased, but they found no significant change in the severity of the AEs. Also, in this study, only one doctor reviewed each medical record giving rise to concern about the reproducibility of this method in different hospitals.75
When evaluating the effectiveness of potential components of the screening instrument, we found it striking that so little studies make a distinction between preventable and non-preventable AEs. There is, in our opinion, little to gain from identifying non preventable AEs because apparently nothing can be done about them. The focus should therefore be on preventable AEs. Regrettably, there are to our knowledge no methods designed to solely extract cases with preventable AEs. Future research should therefore take this into account. Furthermore, the negative predictive value of the commonly used trigger systems is underexposed (only a few studies investigated non triggered cases for AEs) and the positive predictive value varies widely between the studies. This might be caused by the difference in performance of the trigger systems, however we cannot exclude that the prevalence of AEs in different hospitals and countries varies considerably. It appears to be uncommon to investigate the exact prevalence of AEs in a certain population. On the contrary, the prevalence of triggers is well known. However, to compare the PPV of trigger systems and triggers between studies information about the real prevalence of AEs in the investigated population is essential.
We found the inter observer agreement on the presence, preventability and the severity of the AEs disappointing. This is possibly caused by lack of an international standard on interpretation of the triggers or on the methods to identify AEs. Background and experience of those who trigger and those who investigate cases is therefore probably an important cause of this variation. This could affect the strength of the feedback to the medical departments and might give rise to discussions about the accuracy of the final judgment instead of leading to improvement of care.
Information about screening that should benefit the community at large rather than, or in addition to, the individual patient, is completely absent in these studies. The road to an AE is usually influenced by many different factors. It is a chain of events with numerous cross links to other processes that eventually leads to the adverse outcome. If one link is changed to prevent a rare but serious AE this might also influence other processes and hence the outcome of the community at large. Possibly this is mediated through a change in practice, which might not benefit everyone, imposing an extra burden on others or leads to unacceptable high costs. We think therefore that any changes should be closely monitored to reveal possible disadvantages for the community at large.
Rather than researching the cost-effectiveness of screening for AE, most studies focus on the total cost of (preventable) AEs. As a result of this, the annual direct medical costs in Dutch hospitals were estimated at a total of $400 million for all AEs and $181 million for preventable AEs in 2004. These total medical costs of AEs accounted for approximately 1% of the national health care budget.28 Bates et al described the costs of medical record review itself in 1995, specifically the cost for every (preventable) AE found and the cost for every admission that was screened.76 We found no studies thereafter addressing this subject. However, It is expected that in an already safe environment the costs for trying to achieve zero AEs rise exponentially.77
However, the studies included in step 4 only show the total costs of AEs in contrast to the cost-benefit or the cost-effectiveness of screening for AEs itself. That means we are not sure that these methods are cost effective or that the costs per QALY are acceptable. Only Adler et al (2015) studied the cost-benefit ratio of reducing AEs using a model approach.62 It is clear that the costs of AEs are high. Many cases have to be screened to find an AE (number needed to screen is 36 in the emergency department and 164 in other departments) and even more to find a preventable AE (53 in the emergency department).18,19 Also only three studies reported costs for preventable AEs and non-preventable AEs separately.28,56,61 We think information especially on the costs of preventable AEs is important because these are the ones that could be avoided by, for example, adapting protocols.
We interpreted step 5 as the total of AEs in a population sample in a health care environment. Again, because there is no international consensus on the definition of an AE and on the detection method, the results we found are diverse. Some of the studies we found used the same categories for harm (E/F/G/H/I). Where E means ’temporary harm to the patient and required intervention’ and I is ‘patient death’.16 However, different definitions of the harm categories, or even using no clear harm categories at all, made interpretation of the differences between these studies difficult. Retrospective medical record reviewing is used by several hospitals worldwide, but is a costly screening instrument with relies on, often incomplete, medical records. Furthermore, hindsight bias could also influence results. Although Resar et al (2003) state that the trigger tool methodology has the potential to track changes in protocols during time and judge the effect of this, it seems that this potential has not been investigated thoroughly.78 This is supported by Wilson et al (2005), who concluded that there was insufficient information to detect an increase in patient safety of a considerable effort to improve during a decade.79
Unfortunately, only two studies reported on the specificity and sensitivity of the IHI trigger tool. For the HMPS tool, the only study is Brennan et al (1991) calculated a specificity of 84% and a negative predictive value of 92%.21 Regarding the acceptability of the screening test, no studies were found.
Health care inspection and government obligate hospitals to report on a large set of parameters. Gathering this information is time consuming and costly.80 In a recent Dutch report of the Dutch care authority (NZa) and the institute for care in the Netherlands (ZiN), the need for illness- and specialism transcending numbers are stressed.81 This will increase the burden even further. The National Patient Safety Foundation (United States) recommends the creation of a common set of safety metrics.82 However, focusing on gathering many different outcomes or triggers does not change practice to become more safe. We should therefore rather focus on a smaller number of preventable outcomes and direct our efforts and financial resources to improve. Another possibility is to focus more on the causes of harm like adverse drug events, nosocomial infections, venous thromboembolism, decubitus, falls and surgical complications.83
It is clear from the literature that the impact of AEs on both hospital costs and on patients’ quality of life is enormous. It is also obvious that detecting preventable AEs in hospitals is important, however in this review we show that a well validated method for detecting AEs and improving the quality of care is not yet available. Therefore, we recommend policy makers to scientifically evaluate the chosen methods for their utility and cost effectiveness with improvement of care as the end goal.
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Table 1: search terms used in the different WHO steps
Search terms
Step 1: ((mortality OR morbidity OR death OR dead OR lethality OR lethal) AND (chart OR dossier OR (medical record) OR (clinical record) OR (medical history)) AND (reviewing OR screening OR analysis)) AND ((AE) OR error OR mistake)
Step 2: (trigger or (inter-rater) or (intra-rater) or observer) AND ((dossier analysis) OR (medical record) or (clinical record) OR (medical history)) AND ((AE) OR error OR mistake)
Step 3: (disadvantage or con) AND ((chart review) OR (dossier analysis) OR (medical record) or (clinical record) OR (medical history)
Step 4: ((cost-benefit) OR (cost-effectiveness)) and ((global trigger tool) OR (chart review) OR (dossier analysis) OR (medical record) or (clinical record) OR (medical history)) AND ((AE) OR error OR mistake)
Step 5: ((incidence OR rate OR occurrence OR frequency OR prevalence) AND ((AE) OR (medical mistake) OR (medical error) OR incident OR harm)
Step 6: (cost or sensitivity or specificity) AND ((global trigger tool) OR (medical record review) OR (chart review)) AND (AE)
Step 7: ((social benefit) or (social cost)) AND ((AE) or (medical error)) AND ((trigger tool) OR (medical record review) OR (chart review))
Date of search was the 1st of February 2016. All search terms were applied in the databases of Pubmed, Embase and Cochrane.
Table 2: Results according to the different WHO steps
Step Results Result after title screening Result after abstract screening Additionally, from references
after abstract screening
1 Pubmed: 1473
Embase: 587
Cochrane: 54 125 1 2
2 Pubmed: 130
Embase: 25
Cochrane: 3 48 14 19
3 Pubmed: 369
Embase: 520
Cochrane: 5 1 0 0
4 Pubmed: 115
Embase: 171
Cochrane: 9 6 1 12
5 Pubmed: 360
Embase: 329
Cochrane: 14 174 12 2
6 Pubmed: 114
Embase: 49
Cochrane: 16 14 1 1
7 Pubmed: 8
Embase: 0
Cochrane: 2 3 0 2
Table 3: results of step 1
Time period Decline of AEs
Wolff et al (2001)
8 years inpatients 69/5111 -> 49/6615
2 years emergency 84/2577 -> 12/2496
Kennerly et al (2011) 19
2 years 31.1/100 -> 24.1/100
Suarez et al (2014) 20
6 years 73/240 -> 66/240
Figure legends
Figure 1. Boxplot of the PPV for every screening method separately. * marks an outlier.
Figure 2. Box-plot of the Kappa for agreement between doctors regarding the presence of an AE, for every screening method separately.
Figure 3. Bar chart of the harm categories of the AEs found in IHI and HMPS studies. E: temporary harm to the patient, required intervention. F: temporary harm to the patient, required initial or prolonged hospitalization. G: permanent patient harm. H: intervention necessary to sustain life. I: death.

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