Data-Driven Optimization of Practitioner Panel Sizes for Population-Centric Healthcare Delivery

Author preprint. This work was presented at AHTBE 2026 and has been selected for publication in Inspire Health Journal. The final Version of Record and journal DOI will be linked when available. This paper presents a data-driven approach to optimizing workload distribution among primary care providers within a large regional health network. By leveraging patient-level data and advanced simulation techniques, the study addresses the challenge of balancing practitioner capacity with the complexity of patient needs. A discrete-event simulation framework combined with statistical modeling of appointment behaviors is used to examine the effects of panel-size adjustments on healthcare access, provider workload, and system efficiency. The findings support complexity-sensitive panel sizing as an evidence-based approach to healthcare resource allocation and capacity management.