Application of a machine learning model to predict the estimated primary care patient time consumption

Rising healthcare costs and a shortage of primary care providers in the United States create substantial strain on the healthcare system, underscoring the need for efficient allocation of limited resources. Accurate prediction of high primary care utilization can enable proactive care planning, targeted interventions, and workload optimization. We developed and evaluated the Friedman Score, a machine learning-based model that predicts estimated yearly primary care utilization categories, low use