Trigger Tool–Based Automated Adverse Event Detection in Electronic Health Records: Systematic Review

Abstract

BackgroundAdverse 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.ObjectiveThe 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.MethodsPubMed, 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.ResultsA 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.ConclusionsWe 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.

The occurrence, types, consequences and preventability of in-hospital adverse events – a scoping review

“…Despite increasing interest among clinical practitioners and researchers to further investigate and develop the GTT, e.g., studies of the accuracy of automated methods for identifying AEs from electronic health record data [ 13 , 14 ], retrospective chart review methods such as the Harvard method remain widely used to identify AEs [ 8 ]. In particular, these have been used in large-scale studies to estimate national-level AE prevalence.…”

Is a modified Global Trigger Tool method using automatic trigger identification valid when measuring adverse events?

“…Automatic identification of triggers in electronic health records (EHRs) provides a digital, standardized and cost-effective approach to measure adverse events [13]. Rather than a reviewer searches for triggers, algorithms are written to automatically identify triggers.…”

Finding Dental Harm to Patients through Electronic Health Record–Based Triggers

“…Our triggers therefore have a relatively low predictive value, which means that many triggered charts did not contain an AE. A low positive predictive value may result in alarm/ alert fatigue and is burdensome to chart reviewers (Call et al 2014;Musy et al 2018). While the performance of these triggers is lower than those developed in medicine (Musy et al 2018), they are still far superior to random chart reviews, where we estimate that only 1.5% contain AEs (unpublished data).…”