Deakin University
watts-strategiestoreduce-2019.pdf (1.2 MB)

Strategies to reduce diagnostic errors: a systematic review

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© 2019 The Author(s). Background: To evaluate the effectiveness of audit and communication strategies to reduce diagnostic errors made by clinicians. Methods: MEDLINE complete, CINHAL complete, EMBASE, PSNet and Google Advanced. Electronic and manual search of articles on audit systems and communication strategies or interventions, searched for papers published between January 1990 and April 2017. We included studies with interventions implemented by clinicians in a clinical environment with real patients. Results: A total of 2431 articles were screened of which 26 studies met inclusion criteria. Data extraction was conducted by two groups, each group comprising two independent reviewers. Articles were classified by communication (6) or audit strategies (20) to reduce diagnostic error in clinical settings. The most common interventions were delivered as technology-based systems n = 16 (62%) and within an acute care setting n = 15 (57%). Nine studies reported randomised controlled trials. Three RCT studies on communication interventions and 3 RCTs on audit strategies found the interventions to be effective in reducing diagnostic errors. Conclusion: Despite numerous studies on interventions targeting diagnostic errors, our analyses revealed limited evidence on interventions being practically used in clinical settings and a bias of studies originating from the US (n = 19, 73% of included studies). There is some evidence that trigger algorithms, including computer based and alert systems, may reduce delayed diagnosis and improve diagnostic accuracy. In trauma settings, strategies such as additional patient review (e.g. trauma teams) reduced missed diagnosis and in radiology departments review strategies such as team meetings and error documentation may reduce diagnostic error rates over time. Trial registration: The systematic review was registered in the PROSPERO database under registration number CRD42017067056.



BMC medical informatics and decision making





Article number



BioMed Central


London, Eng.





Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2019, The Author(s)