“Evaluating Skilled Experts: Optimal Scoring Rules for Surgeons” by Kyna FONG
To identify high-performing and low-performing surgeons, healthcare policy makers have been keen to create "score cards" that evaluate surgeons based on outcomes. Despite the potential benefits, score cards may also create adverse incentives for surgeons to engage in harmful behavior, such as risk selection, in order to improve observed performance. In this paper we solve for optimal evaluation contracts for skilled experts in such settings. We present a model with heterogeneous types in which experts know their own private types, generate observable performance outcomes, and can take private actions, potentially harmful to consumers, that inflate those outcomes. An optimal contract takes the form of a scoring rule, typically characterized by four regions: (1) high score sensitivity to outcomes, (2) low score sensitivity to outcomes, (3) tenure, and (4) firing or license revocation. When improvement is possible, an optimal contract for the low quality expert is a fixed-length mentorship program. In terms of methods, we draw upon continuous-time techniques, as introduced in Sannikov (2007b). Since our problem involves both adverse selection and moral hazard, this paper features novel applications of continuous-time methods in contract design.