![]() ![]() Population-based comparisons are predicated on the assumptions of (1) population disease stability (i.e. The specific goals of this study are to (1) understand diagnostic variance among pathologists and (2) compare diagnostic variance using a population-based approach. We, thus, hypothesize that histomorphology is actually much more reproducible than seen in most practise environments, and that a lack of “control” within a practice environment (rather than imprecision in histomorphology) is the basis of the considerable diagnostic variation in pathological reporting encountered. As such, if diagnostic variation is explained by consistent bias among healthcare providers (as opposed to large swings in the diagnostic rates/diagnostic instability), it should be amendable to an intervention that (deconstructs the diagnostic process), reduces variation and, with calibration (including pathologist rate awareness), may be used to more appropriately stratify patients and improve outcomes. In the context of the above-mentioned philosophical debate, we support a reductionist approach of subdivision into component elements can allow for enhanced understanding with sufficient determination, and appropriate models, statistical methods and process management. The precision/lack of precision in histomorphology is the topic of this study. The later is a historical and longstanding philosophical debate: anti-reductionism (the body is impossible to subdivide effectively into components that enhance understanding) versus reductionism (the body can be subdivided effectively into components that enhance understanding). Such high error rates are often rationalized by the inherently imprecise nature of histomorphology and the innate difficulty in achieving high levels of precision within a complex system such as the human body. The gold standard diagnosis is commonly determined through the consensus of a panel of experts, rather than hard outcomes-driven data 1.įrom the perspective of manufacturing industries (where defect rates are commonly measured in parts per thousand or parts per million), significant disagreements/errors in pathology (that change the management) are common 2. Inter-rater agreement is inherently dependent on both the tissue type and the clinical diagnosis, with the usual results being poor to moderate. ![]() In pathology, it is traditionally measured with kappa values generated by small data sets interpreted by a variable number of pathologists. The moderate PDR stability over time supports the hypothesis that diagnostic rates are amendable to calibration via SPC and outcome data.ĭiagnostic variance can be measured in several ways. The number of pathologists (of 15) with zero or one ( p 600 CRPS each(total 52,760 CRPS), that pathologist, endoscopist, anatomical location and year were all strongly correlated (all p < 0.0001) with the diagnosis. Fifteen pathologists each interpreted > 150 CRPS/year in all years and together diagnosed 38,813. Pathologist diagnostic rates (PDRs) for high grade dysplasia (HGD), tubular adenoma (TA_ad), villous morphology (TVA + VA), sessile serrated adenoma (SSA) and hyperplastic polyp (HP), were assessed (1) for each pathologist in yearly intervals with control charts (CCs), and (2) with a generalized linear model (GLM). All colorectal polyp specimens(CRPS) for 2011–2017 in a region were categorized using a validated free text string matching algorithm. This work sought to quantify pathologists’ diagnostic bias over time in their evaluation of colorectal polyps to assess how this may impact the utility of statistical process control (SPC). ![]()
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