Colonoscopy Indication Algorithm Performance Across Diverse Health Care Systems in the PROSPR Consortium.

Abstract

Background: Despite the importance of characterizing colonoscopy indication for quality monitoring and cancer screening program evaluation, there is no standard approach to documenting colonoscopy indication in medical records.

Methods: We applied two algorithms in three health care systems to assign colonoscopy indication to persons 50-89 years old who received a colonoscopy during 2010-2013. Both algorithms used standard procedure, diagnostic, and laboratory codes. One algorithm, the KPNC algorithm, used a hierarchical approach to classify exam indication into: diagnostic, surveillance, or screening; whereas the other, the SEARCH algorithm, used a logistic regression-based algorithm to provide the probability that colonoscopy was performed for screening. Gold standard assessment of indication was from medical records abstraction.

Results: There were 1,796 colonoscopy exams included in analyses; age and racial/ethnic distributions of participants differed across health care systems. The KPNC algorithm's sensitivities and specificities for screening indication ranged from 0.78-0.82 and 0.78-0.91, respectively; sensitivities and specificities for diagnostic indication ranged from 0.78-0.89 and 0.74-0.82, respectively. The KPNC algorithm had poor sensitivities (ranging from 0.11-0.67) and high specificities for surveillance exams. The Area Under the Curve (AUC) of the SEARCH algorithm for screening indication ranged from 0.76-0.84 across health care systems. For screening indication, the KPNC algorithm obtained higher specificities than the SEARCH algorithm at the same sensitivity.

Conclusion: Despite standardized implementation of these indication algorithms across three health care systems, the capture of colonoscopy indication data was imperfect. Thus, we recommend that standard, systematic documentation of colonoscopy indication should be added to medical records to ensure efficient and accurate data capture.