Background: Primary care practice transformations require tools for policymakers and practice managers to understand the financial implications of workforce and reimbursement changes.
Objective: To create a simulation model to understand how practice utilization, revenues, and expenses may change in the context of workforce and financing changes.
Research design: We created a simulation model estimating clinic-level utilization, revenues, and expenses using user-specified or public input data detailing practice staffing levels, salaries and overhead expenditures, patient characteristics, clinic workload, and reimbursements. We assessed whether the model could accurately estimate clinic utilization, revenues, and expenses across the nation using labor compensation, medical expenditure, and reimbursements databases, as well as cost and revenue data from independent practices of varying size. We demonstrated the model's utility in a simulation of how utilization, revenue, and expenses would change after hiring a nurse practitioner (NP) compared with hiring a part-time physician.
Results: Modeled practice utilization and revenue closely matched independent national utilization and reimbursement data, disaggregated by patient age, sex, race/ethnicity, insurance status, and ICD diagnostic group; the model was able to estimate independent revenue and cost estimates, with highest accuracy among larger practices. A demonstration analysis revealed that hiring an NP to work independently with a subset of patients diagnosed with diabetes or hypertension could increase net revenues, if NP visits involve limited MD consultation or if NP reimbursement rates increase.
Conclusions: A model of utilization, revenue, and expenses in primary care practices may help policymakers and managers understand the implications of workforce and financing changes.