Using Rule-Based Logic to Streamline Patient Outcome Monitoring in a Multidisciplinary Diabetes Specialty Clinic

Abstract

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.