Measuring adverse events in acute care settings - Systematic reviews on diagnostic accuracy, inter-rater reliability and implementation of the Global Trigger Tool
College for Health Care Professionals Claudiana, Italy (Dietmar Ausserhofer) | University Hospital Basel, Patient Safety Office, Switzerland (René Schwendimann) | Department of Intensive Care Medicine, Bern University Hospital, Inselspital, Switzerland (Hans-Ulrich Rothen) | Department of Intensive Care Medicine, Bern University Hospital, Inselspital, Switzerland (Marie-Madlen Jeitziner) | Institute of Social and Preventive Medicine & Institute of Primary Health Care (BIHAM), University of Bern, Switzerland (Anne W.S. Rutjes) | Inselspital Bern University Hospital, Nursing & Midwifery Research Unit & Gynaecology Unit, Switzerland (Natascha Baumann) | Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden (Maria Unbeck)
2015 bis 2018
Adverse events (AEs) in healthcare entail enormous burdens to healthcare systems, institutions, and especially affected patients. To prevent their repetition, retrospective trigger tools are often applied, but also automated approaches using electronic health records.
This systematic review had two aims: 1) to describe current study methods and challenges regarding trigger tool-based AE detection methods in EHRs; and 2) to appraise the reviewed studies’ designs, then synthesize estimates of AE prevalence and diagnostic test accuracy of automatic detection methods, using manual trigger-based AE detection methods as a reference standard.
The search strategy followed Hausner et al.’s approach. PubMed, Embase, CINAHL, and the Cochrane Library were queried. We included observational studies applying trigger tools to detect AEs in EHRs in acute care settings, and excluded studies using non-hospital settings and outpatient clinics. We assessed bias risks and applicability concerns for all included studies.
Erwarteter Nutzen / Relevanz
This project aims to synthesize and to assess the evidence concerning the development of methods of AE detection in electronic health records using trigger tools.