Important links
Abstract
Expert referrals, such as those from primary care physicians (PCPs) to medical specialists, should help to alleviate informational frictions between patients and specialists. But how well do these intermediating experts learn about and act on the quality of the available providers in their referral decisions? In this paper, we study PCP referrals to specialists using data on 4.5 million joint replacement surgeries for Medicare beneficiaries. We first document substantial heterogeneity in specialist quality and costs within geographic markets, and we present design-based evidence showing that PCPs adjust their referrals specifically based on the outcomes of their own patients. We then employ a structural learning model of PCP referral choices, to quantify the losses due to informational frictions and to simulate possible reallocations with improved information. The model also accounts for limitations on possible reallocations due to habit persistence and capacity constraints. We find that about one-quarter of the patients would be referred to a different specialist in the absence of informational frictions, with small but meaningful improvements in patient outcomes.