Abstract
The objectives of this study were to use an open-source natural language-processing tool (NLP) to accurately assess total, anatomic (left and right colon), and advanced adenoma detection rates (ADRs) and to determine how these metrics differ between high- and low-performing endoscopists. An NLP tool was developed using the Apache Unstructured Information Management Architecture and queried all procedure records for screening colonoscopies performed in patients aged 50-75 years at a single institution from April 1998 to December 2013. Validation was performed on 200 procedures and associated pathology reports. The total, left colon, right colon, and advanced ADRs were calculated and physicians were stratified by total ADR (<20% and ≥20%). Comparisons of colonoscopy characteristics and ADR comparisons (advanced, left, right, and right/left ratio) were determined by t-tests and Wilcoxon rank-sum tests. The total ADR for 34,998 screening colonoscopies from 1998 to 2013 was 20.3%, as determined via NLP. The institutional left and right colon ADRs were 10.1% and 12.5%, respectively. The overall advanced ADR was 4.4%. Endoscopists with total ADRs ≥20% had higher left (12.4%) and right colon (16.4%) ADRs than endoscopists with ADRs <20% (left ADR=5.6%, right ADR=5.8%). Endoscopists with ADRs ≥20% had higher individual right/left ADR ratios than those with low ADRs (1.4 (interquartile range (IQR) 0.4) vs. 1.0 (IQR 0.4), P=0.02). There was a moderate positive correlation between advanced ADR detection and both right (Spearman's rho=0.5, P=0.05) and left colon (Spearman's rho=0.4, P=0.03) ADRs. Institutions should consider the use of anatomic and advanced ADRs determined via natural language processing as a refined measure of colonoscopy quality. The ability to continuously monitor and provide feedback on colonoscopy quality metrics may encourage endoscopists to refine technique, resulting in overall improvements in adenoma detection.
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