title: Classification of error in anatomic pathology: a proposal for an evidence-based standard.
abstract: Error in anatomic pathology (EAP) is an appropriate problem to consider using the disease model with which all pathologists are familiar. In analogy to medical diseases, diagnostic errors represent a complex constellation of often-baffling deviations from the "normal" condition. Ideally, one would wish to approach such "diseases of diagnosis" with effective treatments or preventative measures, but interventions in the absence of a clear understanding of pathogenesis are often ineffective or even harmful. Medical therapy has its history of "bleeding and purging," and error-prevention has a history of "blaming and shaming." The urge to take action in dealing with either medical illnesses or diagnostic failings is, of course, admirable. However, the principle of primum non nocere should guide one's action in both circumstances. The first step in using the disease model to address EAP is the development of a valid taxonomy to allow for grouping together of abnormalities that have a similar pathogenesis. It is apparent that disease categories such as "tumor" are not valuable until they are further refined by precise and accurate classification. Likewise, "error" is an impossibly broad concept that must be parsed into meaningful subcategories before it can be understood with sufficient clarity to be prevented. One important EAP subtype that has been particularly difficult to understand and classify is knowledge-based interpretative (KBI) error. Not only is the latter sometimes confused with distinctly different error types such as human lapses, but there is danger of mistaking system-wide problems (eg, imprecise or inaccurate diagnostic criteria) for the KBI errors of individual pathologists. This paper presents a theoretically-sound taxonomic system for classification of error that can be used for evidence-based categorization of individual cases. Any taxonomy of error in medicine must distinguish between the various factors that may produce mistakes, and importantly, whether they are individual, small system (e.g., my histology laboratory), or big system (e.g., published diagnostic criteria). Because no overarching governing agency exists to coordinate this initiative, the recognition of need and effective implementation of EAP counter-measures must emanate from our specialty group itself.
authors.affiliation: Foucell@aol.com
authors.forename: Elliott
authors.initials: E
authors.lastname: Foucar
mesh_terms: D015982:Bias; D003951:Diagnostic Errors / Q000145:classification*; D019317:Evidence-Based Medicine / Q000592:standards*; D005500:Follow-Up Studies; D006801:Humans; D051188:Knowledge Bases; D019300:Medical Errors / Q000145:classification*; D016347:Medical Records Systems, Computerized; D015588:Observer Variation; D010338:Pathology, Clinical / Q000706:statistics & numerical data*; D010380:Peer Review; D017712:Peer Review, Research / Q000592:standards; D011786:Quality Control