An Approach to Systematically Matching Providers and Patients

The physician-patient relationship is widely believed to be a cornerstone of high-value healthcare. However, evidence suggests that existing relationships may be suboptimal, given the number of patients that fail to get recommended care and the prevalence of preventable hospitalizations. This is particularly evident in vulnerable, high-cost populations, such as patients with chronic conditions, whose preventable hospitalizations are estimated to cost $17 billion annually. Using insights from the economics of matching, along with basic demographic and utilization information in administrative data, I develop a model of patient and physician coproduction of health that aims to inform the creation of optimal provider-patient relationships. I apply this model in Medicaid managed care settings, focusing on enrollees with a new diagnosis of a chronic condition. I first estimate the effect of the physician-patient relationship on health outcomes, finding a significant effect of the composition of the provider-patient relationship on adverse health events. I then incorporate these findings into my model of the coproduction of health, where I demonstrate that sorting patients to providers in a systematic way based on observable attributes could reduce avoidable hospitalizations. I compare predicted health outcomes from existing matches to the predicted health outcomes produced by the systematic re-matching suggested by my model, and find an average decrease of 26% in avoidable hospitalizations. Back-of-the-envelope calculations suggests that this translates into approximately $300 million in potential annual savings in hospital costs for Medicaid managed care beneficiaries with one of these chronic conditions, alone. These findings suggest that a more systematic approach to matching patients and physicians—even one based on simple demographic information—has the potential to improve health and reduce avoidable spending.