1340-OR: Small Time Investment, Big Gain: Continuous Outcome Monitoring and Guided Interventions in a Rural Diabetes Education Program



Introduction and Objective: Sustainable quality improvement in diabetes care remains challenging in resource-constrained health systems, where time and staffing often limit feasibility. This study evaluated whether a minimally time-consuming, continuously applied monitoring approach could support sustained glycemic improvement and guide data-informed interventions in a rural diabetes education program.Methods: Retrospective pre-post analyses were conducted on monthly cohorts of adults with type 1 or type 2 diabetes entering the program between October 2023 and September 2024. An EHR report was developed to automatically capture baseline and follow-up HbA1c values at 7 and 13 months. Practice-level HbA1c reductions were evaluated across cohorts and combined, with paired t-tests used to assess significance. Individual-level HbA1c trajectories were used to identify subjects who might benefit from follow-up and guide outreach interventions. All analyses (using Microsoft Excel only) and interventions were performed by a single clinician and required approximately 2-4 hours per month.Results: There were 188 subjects with monthly cohort sizes ranging from 10 to 25. Mean baseline HbA1c ranged from 7.5% to 9.5% across cohorts. At 7 months, average HbA1c reductions ranged from 0.6% to 2.4%, with most cohorts showing significant improvement (p<0.05). At 13 months, reductions were sustained and further improved (0.8% to 2.3%) in most cohorts. When cohorts were combined, mean HbA1c reduction was 1.4% at 7 months and 1.5% at 13 months (both p<0.0001). Subjects who completed assessment-guided follow-up actions demonstrated additional HbA1c improvement at 13 months more frequently (52.6%) than those who did not (10.0%).Conclusion: The approach was associated with sustained HbA1c improvement and facilitated targeted interventions. By embedding outcome assessment into routine clinical workflow with minimal time investment, it offers a sustainable and scalable model for quality improvement in resource-constrained settings.

Disclosure

X. Zhang: None. T. McGettigan: None.



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