An Automated Data Algorithm To Distinguish Screening and Diagnostic Colorectal Cancer Endoscopy Exams

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Abstract
Despite questions about accuracy, automated data are used increasingly for research and quality measurement. The goal of this study was to develop an automated data algorithm designed to distinguish screening and diagnostic endoscopy (sigmoidoscopy and colonoscopy) exams. We assessed the algorithm's ability to correctly classify the exams using paper medical records as the “gold standard.” The algorithm used diagnostic codes to identify the indication of the endoscopies. The algorithm's ability to classify the indication varied by endoscopy exam. The sensitivities for identifying diagnostic sigmoidoscopy and colonoscopy were 48.1% and 23.8%, respectively. The algorithm missed most of the diagnostic endoscopies. Conversely, the sensitivities for identifying screening sigmoidoscopy and colonoscopy were high (87.9% and 84.4%, respectively) but were associated with low specificities. Our findings suggest that studies relying solely on automated data overestimate screening rates if indication is not considered. The automated algorithm presented here needs further improvements to better differentiate screening from diagnostic exams.

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