title: Detecting adverse events for patient safety research: a review of current methodologies.
abstract: Promoting patient safety is a national priority. To evaluate interventions for reducing medical errors and adverse event, effective methods for detecting such events are required. This paper reviews the current methodologies for detection of adverse events and discusses their relative advantages and limitations. It also presents a cognitive framework for error monitoring and detection. While manual chart review has been considered the "gold-standard" for identifying adverse events in many patient safety studies, this methodology is expensive and imperfect. Investigators have developed or are currently evaluating, several electronic methods that can detect adverse events using coded data, free-text clinical narratives, or a combination of techniques. Advances in these systems will greatly facilitate our ability to monitor adverse events and promote patient safety research. But these systems will perform optimally only if we improve our understanding of the fundamental nature of errors and the ways in which the human mind can naturally, but erroneously, contribute to the problems that we observe.
authors.affiliation: Department of Veterans Affairs, Tennessee Valley Healthcare System, GRECC, 1310 24th Avenue South, Nashville, TN 37212-2637, USA. harvey.murff@med.va.gov
authors.forename: Harvey J
authors.initials: HJ
authors.lastname: Murff
authors.forename: Vimla L
authors.initials: VL
authors.lastname: Patel
authors.forename: George
authors.initials: G
authors.lastname: Hripcsak
authors.forename: David W
authors.initials: DW
authors.lastname: Bates
mesh_terms: D003628:Database Management Systems; D016208:Databases, Factual; D003661:Decision Support Techniques; D004282:Documentation; D005103:Expert Systems; D016247:Information Storage and Retrieval / Q000379:methods*; D008485:Medical Audit / Q000379:methods*; D019300:Medical Errors / Q000379:methods* / Q000517:prevention & control / Q000706:statistics & numerical data; D016347:Medical Records Systems, Computerized; D015233:Models, Statistical; D010346:Patient Care Management / Q000379:methods; D011785:Quality Assurance, Health Care / Q000379:methods; D018570:Risk Assessment / Q000379:methods*; D012308:Risk Management / Q000379:methods*; D017751:Safety Management / Q000379:methods*; D013223:Statistics as Topic / Q000379:methods