title: A system of analyzing medical errors to improve GME curricula and programs.
abstract: The report of the Institute of Medicine (IOM) To Err is Human recommended that both mandatory and voluntary event-reporting systems be established to identify and learn from errors. Because of the tight coupling of graduate medical education (GME) programs and the delivery of care, any event-reporting system used in a teaching hospital should be able to document the types of errors that are being made by graduate medical trainees (GTs). The authors performed an analysis of the root causes of events involving GTs that were recorded in hospital-based near-miss reporting systems. The root causes were classified using the Eindhoven Classification Model, medical version. Case histories of three separate events, one from an accident and emergency department in the United Kingdom, and two from a large teaching hospital in the United States, are used to illustrate the method. In all three cases, lack of knowledge on the part of the trainee contributed to the incident. Inadequate educational preparation had the potential for causing significant harm to the patient. Organizational causes were also present in each case, which illustrates the need to examine not only educational issues but also procedural and management issues related to GME. In each case, the analysis revealed in striking clarity deficiencies of educational content and problems of program structure. The authors conclude that doing a root-cause analysis in conjunction with a near-miss event-reporting system in a teaching hospital can be a valuable source of documented information to guide needed educational and system changes to GME programs.
authors.affiliation: Office of Medical Education, University of Texas Southwestern Medical Center at Dallas, Dallas, Texas, USA. jbattles@ahrq.gov
authors.forename: J B
authors.initials: JB
authors.lastname: Battles
authors.forename: C E
authors.initials: CE
authors.lastname: Shea
mesh_terms: D003479:Curriculum; D003663:Decision Trees; D003951:Diagnostic Errors; D004503:Education, Medical, Graduate / Q000592:standards*; D006801:Humans; D019300:Medical Errors; D011996:Records; D014481:United States