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Journal of General Practice

ISSN: 2329-9126

Open Access

Using an Automated Data-driven, EHR-Embedded Program for Mailing FIT kits: Lessons from the STOP CRC Pilot Study

Abstract

Gloria D. Coronado, Tim Burdick, Amanda Petrik, Tanya Kapka, Sally Retecki and Beverly Green

Background: The Strategies and Opportunities to Stop Colorectal Cancer (STOP CRC) study is collaboration among two research institutions and health-systems partners. The main study, scheduled to begin in 2014, will assess effectiveness of an intervention program using electronic health record (EHR) clinical decision support (CDS) tools to improve rates of colorectal-cancer screening in federally qualified health centers (FQHCs). Very few studies, and no large studies, aimed at raising CRC screening rates have utilized an EHR-embedded system. Study design: We piloted the use of an EHR-embedded real-time patient registry reporting tool in a pilot study undertaken prior to beginning our main CRC screening study. The pilot study goal was to assess feasibility and effectiveness of two clinic-based approaches to raising rates of colorectal cancer screening among selected patients aged 50-74 who were not up-to-date with colorectal-cancer screening guidelines. We used work sessions and qualitative interviews with clinic personnel to assess performance of the tool, as well as to identify specific elements of the tool’s functionality needing refinement. Results: Two critical elements of the EHR tool allowed us to mail FIT kits efficiently to appropriate patients: (1) having a direct interface with the laboratory that processed the FITs, thus allowing for real-time updates to the registry; and (2) being able to place lab orders from a list of selected patients. We identified the following elements that needed refining: the use of Health Maintenance (EHR function for tracking screening eligibility and due dates incorporating STOP CRC inclusion and exclusion criteria), and the development of report templates for identifying patients eligible for each step. Conclusion: We found that most elements of our EHR-embedded program worked well and that specific refinement may improve the accuracy of identifying patient

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