Retrospective data collection and analytical techniques for patient safety studies

  • Authors:
  • Matthew B. Weinger;Jason Slagle;Sonia Jain;Nelda Ordonez

  • Affiliations:
  • San Diego Center for Patient Safety, University of California, San Diego and Health Services Research and Development Service, Veterans Affairs San Diego Healthcare System University of California ...;Anesthesia Ergonomics Research Laboratory, San Diego Center for Patient Safety, University of California, San Diego;Department of Family and Preventive Medicine, University of California, San Diego;Health Services Research and Development Service, Veterans Affairs San Diego Healthcare System and Department of Anesthesiology, San Diego Center for Patient Safety, University of California, San ...

  • Venue:
  • Journal of Biomedical Informatics - Patient safety
  • Year:
  • 2003

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Abstract

To enhance patient safety, data about actual clinical events must be collected and scrutinized. This paper has two purposes. First, it provides an overview of some of the methods available to collect and analyze retrospective data about medical errors, near misses, and other relevant patient safety events. Second, it introduces a methodological approach that focuses on non-routine events (NRE), defined as all events that deviate from optimal clinical care. In intermittent in-person surveys of anesthesia providers, 75 of 277 (27%) recently completed anesthetic cases contained a non-routine event (98 total NRE). Forty-six of the cases (17%) had patient impact while only 20 (7%) led to patient injury. In contrast, in the same hospitals over a two-year period, we collected event data on 135 cases identified with traditional quality improvement processes (event incidence of 0.7-2.7%). In these quality improvement cases, 120 (89%) had patient impact and 74 (55%) led to patient injury. Preliminary analyses not only illustrate some of the analytical methods applicable to safety data but also provide insight into the potential value of the non-routine event approach for the early detection of risks to patient safety before serious patient harm occurs.