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

Musy, Sarah N; Ausserhofer, Dietmar; Schwendimann, René; Rothen, Hans Ulrich; Jeitziner, Marie-Madlen; Rutjes, Anne; Simon, Michael (2018). Trigger Tool-Based Automated Adverse Event Detection in Electronic Health Records: Systematic Review. Journal of medical internet research, 20(5), e198. Centre of Global eHealth Innovation 10.2196/jmir.9901

Musy JMedInternetRes 2018.pdf - Published Version
Available under License Creative Commons: Attribution (CC-BY).

Download (1MB) | Preview

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.

Item Type:

Journal Article (Review Article)


04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Social and Preventive Medicine (ISPM)
04 Faculty of Medicine > Medical Education > Institute of General Practice and Primary Care (BIHAM)
04 Faculty of Medicine > Department of Intensive Care, Emergency Medicine and Anaesthesiology (DINA) > Clinic of Intensive Care
04 Faculty of Medicine > Department of General Internal Medicine (DAIM) > Clinic of General Internal Medicine

UniBE Contributor:

Musy, Sarah; Rothen, Hans Ulrich; Jeitziner, Marie-Madlen and Rutjes, Anne


600 Technology > 610 Medicine & health
300 Social sciences, sociology & anthropology > 360 Social problems & social services




Centre of Global eHealth Innovation




Tanya Karrer

Date Deposited:

07 Jun 2018 16:37

Last Modified:

23 Oct 2019 14:59

Publisher DOI:


PubMed ID:


Uncontrolled Keywords:

electronic health records patient harm patient safety review, systematic




Actions (login required)

Edit item Edit item
Provide Feedback