%0 Journal Article
%T Using Rule-Based Logic to Streamline Patient Outcome Monitoring in a Multidisciplinary Diabetes Specialty Clinic
%A Joesph E. Capito
%J International Journal of Clinical Medicine
%P 225-231
%@ 2158-2882
%D 2025
%I Scientific Research Publishing
%R 10.4236/ijcm.2025.164014
%X Background: Data collection for research and quality improvement can be resource-intensive, often requiring manual chart reviews. This challenge was evident in a multidisciplinary diabetes clinic within an academic family medicine practice. Traditional methods for evaluating metrics relied on time-intensive processes, hindering efficient quality assessments. Objectives: To develop a streamlined, rules-based reporting solution within Epic EMR to simplify data collection and demonstrate patient outcome improvements, specifically hemoglobin A1c levels, in a multidisciplinary diabetes clinic. Methods: A series of custom rules were created to identify clinic visits, capture baseline and follow-up metrics, and generate automated reports for analysis. The rules included specifications for identifying clinic visit types, calculating first and last visit dates, and retrieving laboratory data for pre- and post-clinic comparisons. These rules were then used to populate columns in a reporting workbench for real-time data display. Results: The rules-based reporting solution yielded a dataset of 151 patients, with metrics including pre- and post-clinic hemoglobin A1c values, visit counts, and outcome trends. Conclusions: Informatics-driven solutions like this rules-based reporting system can significantly reduce the burden of data collection for research and quality improvement initiatives. This approach enhances the capacity for real-time outcome monitoring and supports the integration of evidence-based practices in clinical settings.
%K Electronic Health Records and Systems
%K Quality
%K Diabetes
%K Biosurveillance
%K Case Reporting
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=141947