title: Commentary: how can we make diagnosis safer?.
abstract: Diagnostic errors are common and are a leading cause of patient dissatisfaction and malpractice suits. Because of its traditional heavy reliance on memory and lack of standardization, the diagnostic process is particularly error prone. A study by Zwaan and colleagues on diagnostic failures in treating dyspneic patients makes several important contributions: examining the process behind the diagnosis, seeking insights as to the reasons for the process failures by interviewing the treating physicians, and using the Delphi process with experts to map the optimal diagnostic process. There is considerable confusion about definitions in the field of diagnostic errors. The authors of this commentary use a Venn diagram to clarify distinctions and relationships between diagnosis processes errors, delayed diagnosis and misdiagnosis, and adverse outcomes. A key question is whether a much more rigorous process should be employed for diagnosis, specifically the routine use of algorithms or guidelines, and whether barriers to achieving it can be overcome. The authors propose an alternate simpler approach: six-part checklists for the top 20 or 30 clinical symptoms or problems. The elements of these checklists for minimizing diagnostic errors include essential data elements, don't-miss diagnoses, red-flag symptoms, potential drug causes, required referral(s), and follow-up instructions. These checklists could-and should-be developed by collaborative efforts of the main users, primary care physicians, and emergency physicians, working with specialist physicians on specific symptoms and diagnoses. Absent such professional commitment, progress in diagnostic accuracy is likely to be slow.
authors.forename: Gordon D
authors.initials: GD
authors.lastname: Schiff
authors.forename: Lucian L
authors.initials: LL
authors.lastname: Leape
mesh_terms: D003657:Decision Making; D003951:Diagnostic Errors / Q000706:statistics & numerical data*; D004417:Dyspnea / Q000175:diagnosis*; D006801:Humans; D010820:Physicians / Q000523:psychology* / Q000706:statistics & numerical data*