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Application of electronic trigger tools to identify targets for improving diagnostic safety
  1. Daniel R Murphy1,2,
  2. Ashley ND Meyer1,2,
  3. Dean F Sittig1,3,4,
  4. Derek W Meeks1,2,
  5. Eric J Thomas4,
  6. Hardeep Singh1,2
  1. 1 Center for Innovations in Quality, Effectiveness and Safety, Michael E DeBakey Veterans Affairs Medical Center, Houston, Texas, USA
  2. 2 Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
  3. 3 School of Biomedical Informatics, University of Texas Health Science Center, Houston, Texas, USA
  4. 4 Department of Medicine, University of Texas-Memorial Hermann Center for Healthcare Quality and Safety, Houston, Texas, USA
  1. Correspondence to Dr Daniel R Murphy, Center for Innovations in Quality, Effectiveness and Safety, Michael E DeBakey Veterans Affairs Medical Center, Houston, TX 77030, USA; drmurphy{at}


Progress in reducing diagnostic errors remains slow partly due to poorly defined methods to identify errors, high-risk situations, and adverse events. Electronic trigger (e-trigger) tools, which mine vast amounts of patient data to identify signals indicative of a likely error or adverse event, offer a promising method to efficiently identify errors. The increasing amounts of longitudinal electronic data and maturing data warehousing techniques and infrastructure offer an unprecedented opportunity to implement new types of e-trigger tools that use algorithms to identify risks and events related to the diagnostic process. We present a knowledge discovery framework, the Safer Dx Trigger Tools Framework, that enables health systems to develop and implement e-trigger tools to identify and measure diagnostic errors using comprehensive electronic health record (EHR) data. Safer Dx e-trigger tools detect potential diagnostic events, allowing health systems to monitor event rates, study contributory factors and identify targets for improving diagnostic safety. In addition to promoting organisational learning, some e-triggers can monitor data prospectively and help identify patients at high-risk for a future adverse event, enabling clinicians, patients or safety personnel to take preventive actions proactively. Successful application of electronic algorithms requires health systems to invest in clinical informaticists, information technology professionals, patient safety professionals and clinicians, all of who work closely together to overcome development and implementation challenges. We outline key future research, including advances in natural language processing and machine learning, needed to improve effectiveness of e-triggers. Integrating diagnostic safety e-triggers in institutional patient safety strategies can accelerate progress in reducing preventable harm from diagnostic errors.

  • electronic health records
  • health information technology
  • triggers
  • medical informatics
  • patient safety
  • diagnostic errors
  • diagnostic delays

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  • Contributors All authors contributed to the development, review and revision of this manuscript.

  • Funding Work described is heavily drawn from research funded by the Veteran Affairs Health Services Research and Development Service CREATE grant (CRE-12-033), the Agency for Healthcare Research and Quality (R18HS017820) and the Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety (CIN 13–413). Dr Murphy is additionally funded by an Agency for Healthcare Research & Quality Mentored Career Development Award (K08-HS022901), and Dr Singh is additionally supported by the VA Health Services Research and Development Service (Presidential Early Career Award for Scientists and Engineers USA 14-274), the VA National Center for Patient Safety, the Agency for Health Care Research and Quality (R01HS022087) and the Gordon and Betty Moore Foundation. Drs Sittig and Thomas are supported in part by the Agency for Health Care Research and Quality (P30HS023526). These funding sources had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data; and preparation, review or approval of the manuscript.

  • Competing interests None declared.

  • Patient consent Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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