DEVELOPMENT OF AN AUTOMATED ALGORITHM TO GENERATE GUIDELINE-BASED RECOMMENDATIONS FOR FOLLOW-UP COLONOSCOPY.
Physician adherence to published colonoscopy surveillance guidelines varies. We aimed to develop and validate an automated clinical decision support algorithm that can extract procedure and pathology data from the electronic medical record (EMR) and generate surveillance intervals congruent with guidelines, which might increase physician adherence.We constructed a clinical decision support (CDS) algorithm based on guidelines from the United States Multi-Society Task Force on Colorectal Cancer. We used a randomly generated validation dataset of 300 outpatient colonoscopies performed at the Cleveland Clinic from 2012 through 2016 to evaluate the accuracy of extracting data from reports stored in the EMR using natural language processing (NLP). We compared colonoscopy follow-up recommendations from the CDS algorithm, endoscopists, and task force guidelines. Using a testing dataset of 2439 colonoscopies, we compared endoscopist recommendations with those of the algorithm.Manual review of the validation dataset confirmed the NLP program accurately extracted procedure and pathology data for all cases. Recommendations made by endoscopists and the CDS algorithm were guideline-concordant in 62% and 99% of cases respectively. Discrepant recommendations by endoscopists were earlier than recommended in 94% of the cases. In the testing dataset, 69% of endoscopist and NLP-CDS algorithm recommendations were concordant. Discrepant recommendations by endoscopists were earlier than guidelines in 91% of cases.We constructed and tested an automated CDS algorithm that can use NLP-extracted data from the EMR to generate follow-up colonoscopy surveillance recommendations based on published guidelines.