Physician Incentives and Treatment Choices in Heart Attack Management

Physician Incentives and Treatment Choices in Heart Attack Management
Dominic Coey

We estimate how physicians’ financial incentives affect their treatment choices in heart Attack management, using a large dataset of private health insurance claims. Different insurance plans pay physicians different amounts for the same services, generating the required variation in financial incentives.

We begin by presenting evidence that, unconditionally, plans that pay physicians more for more invasive treatments are associated with a considerably larger fraction of such treatments. To interpret this correlation as causal, we continue by showing that it survives conditioning on a rich set of diagnosis and provider-specific variables.

We perform a host of additional checks verifying that differences in unobservable patient or provider characteristics across plans are unlikely to be driving our results. We find that physicians’ treatment choices respond positively to the payments they receive, and that the response is quite large.

If physicians received bundled payments instead of fee-for-service incentives, for example, heart attack management would become considerably more conservative. Our estimates imply that 20 percent of patients would receive different treatments, physician costs would decrease by 27 percent, and social welfare would increase.

Another publication from the same category: Economics

Proceedings of the Sixteenth ACM Conference on Economics and Computation (EC '15). ACM, New York, NY, USA (2015)

Canary in the e-Commerce Coal Mine: Detecting and Predicting Poor Experiences Using Buyer-to-Seller Messages

Dimitriy Masterov, Uwe Mayer, Steve Tadelis

Reputation and feedback systems in online marketplaces are often biased, making it difficult to ascertain the quality of sellers. We use post-transaction, buyer-to-seller message traffic to detect signals of unsatisfactory transactions on eBay. We posit that a message sent after the item was paid for serves as a reliable indicator that the buyer may be unhappy with that purchase, particularly when the message included words associated with a negative experience. The fraction of a seller's message traffic that was negative predicts whether a buyer who transacts with this seller will stop purchasing on eBay, implying that platforms can use these messages as an additional signal of seller quality.