Right-Sizing Primary Care Panels: A Workload Adjustment Approach
For primary care providers, the value-based shift in care requires care coordination and a better understanding of a provider’s panel size. This project sought to create a framework to assess and right-size primary care panels using a workload adjustment, in order to obtain metrics that could be used to adjust compensation, scheduling, and workflows.
The primary care panel size model was created from data analyzed across twenty primary practice sites, with patients empaneled to the primary care provider listed in the electronic health record. Encounter data was extracted for all office visits, telemedicine visits, telephone calls, and other miscellaneous patient interactions, and then used to calculate a workload score for each patient. The workload score was then used to adjust the panel size and obtain a more accurate representation of empanelment rates.
The panel size application attributes 138,350 patients to 215 physicians, 212 residents, and 33 nurse practitioners. Additional panel size analysis was limited to providers with panel sizes of at least 300 patients. Assuming a panel size of 1800 patients per provider, analysis showed an overall empanelment rate around 97%, with an unadjusted range of 30% to 249% and a workload adjusted range of 29% to 228%. Subset analysis showed some patients with higher workload than expected and other patients with lower workload than expected.
In conclusion, this project successfully demonstrated that an evidence-based approach to panel size calculation and workload adjustment is possible with a limited number of data points. Workload adjustment was felt by primary care leadership to be more representative of the day-to-day work of providers; compared to chronic disease related scoring systems. This project can now be used with individual practices and providers to adjust compensation, scheduling, and workflows.
Recommended CitationBabula, MD, Bracken and Kaminski, MD, MBA, M., "Right-Sizing Primary Care Panels: A Workload Adjustment Approach" (2020). Master of Science in Population Health Capstone Presentations. Presentation 4.