Using a pathology driven rules engine CDS tool to improve concordance between documented follow up recommendations in the EHR after colonoscopy polypectomy and care guidelines

Poster Number: P21
Presentation Time: 05:00 PM - 06:30 PM

Abstract Keywords: Clinical Decision Support, Clinical Guidelines, Documentation Burden, Rule-based artificial intelligence
Primary Track: Applications
Programmatic Theme: Clinical Informatics

Failure to document colonoscopy follow up needs post-polypectomy can lead to delayed detection of colorectal cancer (CRC). Automating the update of a centralized follow-up date in the electronic health record (EHR) based on pathology results may increase the number of patients with guideline concordant CRC follow-up screening. As part of an operational quality improvement initiative, we developed an innovative rules-based clinical decision support tool to automate updates to the patient’s Health Maintenance (HM) follow-up date for CRC screening based on pathology results with the objective of improving concordance with guideline recommendations while reducing provider documentation burden. This study was an organizational-wide prospective pre-post design that compared standard care to the effect of an automated, EHR-based rules-engine CDS tool on the follow-up frequency documented in the patient’s EHR (i.e. HM section). Primary outcome measure is change in follow-up screening interval. Study population included 10,024 standard care and 19,184 intervention participants. The proportion of patients with a 10-year default follow-up frequency significantly decreased (87.4% to 41.6%, p<0.001). Failure to update follow-up dates for recommended screening can lead to missed colonoscopies and delayed detection of CRC. The implementation of an automated rules-engine-based CDS tool has the potential to increase the accuracy of colonoscopy follow-up screening dates recorded in patient EHR HM activities without increasing clinician documentation burden. The results of this study emphasize the need for more automated and integrated solutions for updating and maintaining EHR HM activities.

Speaker(s):
Adam Szerencsy, DO
NYU Langone Health

Author(s):
Elizabeth Stevens, PhD, MPH - NYU Grossman School of Medicine; Arielle Nagler, MD - NYU Grossman School of Medicine; Casey Monina, RN - NYU Langone MCIT; JaeEun Kwon, MPP - NYU Grossman School of Medicine; Amanda Olesen Wickline, None - NYU Langone MCIT; Gary Kalkut, MD - NYU Grossman School of Medicine; Dave Ranson, none - NYU Langone MCIT; Seth Gross, MD - NYU Grossman School of Medicine; Aasma Shaukat, MD, MPH - NYU Grossman School of Medicine; Adam Szerencsy, DO - NYU Langone Health;