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Adverse event detection by medical record review is reproducible, but the assessment of their preventability is not.

ABSTRACT: OBJECTIVE:To assess the reproducibility of adverse event evaluation by a medical record review committee. DESIGN:Cross-sectional reanalysis of medical records. INTERVENTION:Reviewers re-examined fifty medical records of deceased patients regarding the presence of adverse events, their potential preventability and their possible contribution to death. Also we investigated the root causes of the preventable AEs. Differences between the first and second assessment were calculated. RESULTS:The Kappa on the presence of an adverse event was 0.64 and 0.32 for the potential preventability. The intrarater agreement showed a Kappa of 0.61 on the adverse event presence and 0.64 for the potential preventability. Interrater agreement showed a Kappa of 0.66 for the adverse event presence and 0.03 for the potential preventability. CONCLUSION:We found a fair reproducibility for the detection of adverse events, but a poor reproducibility for the potential preventability. Possibly this was caused by lack of a definition for the preventability of adverse events. We think giving feedback to professionals using the results of medical record review remains valuable, but an improvement of its reproducibility is essential. To our opinion an international consensus on what exactly constitutes preventability of adverse events and agreement on a definition is necessary. This would result in more comparable studies in this field and could then be more informative on the ideal procedure to avoid certain potentially preventable adverse events in the future.

SUBMITTER: Klein DO

PROVIDER: S-EPMC6264838 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Adverse event detection by medical record review is reproducible, but the assessment of their preventability is not.

Klein Dorthe O DO Rennenberg Roger J M W RJMW Koopmans Richard P RP Prins Martin H MH

PloS one 20181129 11

<h4>Objective</h4>To assess the reproducibility of adverse event evaluation by a medical record review committee.<h4>Design</h4>Cross-sectional reanalysis of medical records.<h4>Intervention</h4>Reviewers re-examined fifty medical records of deceased patients regarding the presence of adverse events, their potential preventability and their possible contribution to death. Also we investigated the root causes of the preventable AEs. Differences between the first and second assessment were calcula ...[more]

PMID: 30496243

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Project description:BackgroundThere is a high degree of variability in assessing the preventability of adverse drug events, limiting the ability to compare rates of preventable adverse drug events across different studies. We compared three methods for determining preventability of adverse drug events in emergency department patients and explored their strengths and weaknesses.MethodsThis mixed-methods study enrolled emergency department patients diagnosed with at least one adverse drug event from three prior prospective studies. A clinical pharmacist and physician reviewed the medical and research records of all patients, and independently rated each event's preventability using a "best practice-based" approach, an "error-based" approach, and an "algorithm-based" approach. Raters discussed discordant ratings until reaching consensus. We assessed the inter-rater agreement between clinicians using the same assessment method, and between different assessment methods using Cohen's kappa with 95% confidence intervals (95% CI). Qualitative researchers observed discussions, took field notes, and reviewed free text comments made by clinicians in a "comment" box in the data collection form. We developed a coding structure and iteratively analyzed qualitative data for emerging themes regarding the application of each preventability assessment method using NVivo.ResultsAmong 1356 adverse drug events, a best practice-based approach rated 64.1% (95% CI: 61.5-66.6%) of events as preventable, an error-based approach rated 64.3% (95% CI: 61.8-66.9%) of events as preventable, and an algorithm-based approach rated 68.8% (95% CI: 66.1-71.1%) of events as preventable. When applying the same method, the inter-rater agreement between clinicians was 0.53 (95% CI: 0.48-0.59), 0.55 (95%CI: 0.50-0.60) and 0.55 (95% CI: 0.49-0.55) for the best practice-, error-, and algorithm-based approaches, respectively. The inter-rater agreement between different assessment methods using consensus ratings for each ranged between 0.88 (95% CI 0.85-0.91) and 0.99 (95% CI 0.98-1.00). Compared to a best practice-based assessment, clinicians believed the algorithm-based assessment was too rigid. It did not account for the complexities of and variations in clinical practice, and frequently was too definitive when assigning preventability ratings.ConclusionThere was good agreement between all three methods of determining the preventability of adverse drug events. However, clinicians found the algorithmic approach constraining, and preferred a best practice-based assessment method.

Project description:BACKGROUND:Adverse events in health care entail substantial burdens to health care systems, institutions, and patients. Retrospective trigger tools are often manually applied to detect AEs, although automated approaches using electronic health records may offer real-time adverse event detection, allowing timely corrective interventions. OBJECTIVE:The aim of this systematic review was to describe current study methods and challenges regarding the use of automatic trigger tool-based adverse event detection methods in electronic health records. In addition, we aimed to appraise the applied studies' designs and to synthesize estimates of adverse event prevalence and diagnostic test accuracy of automatic detection methods using manual trigger tool as a reference standard. METHODS:PubMed, EMBASE, CINAHL, and the Cochrane Library were queried. We included observational studies, applying trigger tools in acute care settings, and excluded studies using nonhospital and outpatient settings. Eligible articles were divided into diagnostic test accuracy studies and prevalence studies. We derived the study prevalence and estimates for the positive predictive value. We assessed bias risks and applicability concerns using Quality Assessment tool for Diagnostic Accuracy Studies-2 (QUADAS-2) for diagnostic test accuracy studies and an in-house developed tool for prevalence studies. RESULTS:A total of 11 studies met all criteria: 2 concerned diagnostic test accuracy and 9 prevalence. We judged several studies to be at high bias risks for their automated detection method, definition of outcomes, and type of statistical analyses. Across all the 11 studies, adverse event prevalence ranged from 0% to 17.9%, with a median of 0.8%. The positive predictive value of all triggers to detect adverse events ranged from 0% to 100% across studies, with a median of 40%. Some triggers had wide ranging positive predictive value values: (1) in 6 studies, hypoglycemia had a positive predictive value ranging from 15.8% to 60%; (2) in 5 studies, naloxone had a positive predictive value ranging from 20% to 91%; (3) in 4 studies, flumazenil had a positive predictive value ranging from 38.9% to 83.3%; and (4) in 4 studies, protamine had a positive predictive value ranging from 0% to 60%. We were unable to determine the adverse event prevalence, positive predictive value, preventability, and severity in 40.4%, 10.5%, 71.1%, and 68.4% of the studies, respectively. These studies did not report the overall number of records analyzed, triggers, or adverse events; or the studies did not conduct the analysis. CONCLUSIONS:We observed broad interstudy variation in reported adverse event prevalence and positive predictive value. The lack of sufficiently described methods led to difficulties regarding interpretation. To improve quality, we see the need for a set of recommendations to endorse optimal use of research designs and adequate reporting of future adverse event detection studies.

Dataset Information

Adverse event detection by medical record review is reproducible, but the assessment of their preventability is not.

Publications

Adverse event detection by medical record review is reproducible, but the assessment of their preventability is not.

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