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Researchers at Harvard Medical School and Stanford University used data from AHRQ’s ) to develop an instrument that gauges how workforce and financing changes may affect utilization, revenue, and expenses in primary care practices.

The research was led by the Center for Primary Care, a division of Harvard Medical School that conducts research and interventions on how primary care is delivered and how practitioners are trained. Working with seven Boston-area academic medical centers and 28 primary care practices, as well as the Institute for Healthcare Improvement and Qualis Health, the Center and partners created an Academic Innovations Collaborative that aimed to transform primary care in Massachusetts.

Staffing questions immediately emerged. "Practices need to make the business case for adding staff and need to explain to their chief financial officers whether it would pay off if they increased capacity in primary care teams by adding nurse care managers or community health workers," said Russell S. Phillips, M.D., director of the Center for Primary Care and the William S. Applebaum Professor of Medicine and Professor of Global Health and Social Medicine at Harvard Medical School.

Dr. Phillips teamed up with researchers to create mathematical models that simulate real-world practice settings, in order to analyze how different variables could affect practices differently.

"Our model relied on a number of databases, the most important of which was MEPS," he noted.

The result was an instrument in which a number of statistical assumptions could be used to predict the impact of changing the primary care labor force on clinic operations, economic outcomes, and performance in patient-centered medical home models. For instance, one analysis revealed that hiring a nurse practitioner to work independently with a subset of patients diagnosed with diabetes or hypertension could increase net revenues, but only if nurse practitioner visits involved limited physician consultation or if nurse practitioner reimbursement rates increased.

The instrument can also be used to test the impact of new approaches to payment. Hospitals and primary care practices in the collaborative—together treating approximately 300,000 patients—can use this model to understand how to invest resources wisely. The model also has applications nationwide, because parameters are specified for each state.

"This model provides a starting point to evaluate primary care redesign," Dr. Phillips said. "It can be expanded to answer a range of questions, including the impact of new payment models on the structure and finances of primary care practices."