主　题：Performance-Based Contracts for Outpatient Medical Services
主讲人：英国剑桥大学商学院高级讲师/管理科学研究生项目主任
协调人：商学院管理科学与工程系 刘国山教授
时　间：7月14日（星期四）下午14：00
Abstract：In recent years, the performance-based approach to contracting for medical services has been gaining popularity across different healthcare delivery systems, both in the US (under the name of ``Pay-for-Performance'''''''''''''''''''''''''''''''', or P4P),and abroad (``Payment-by-Results'''''''''''''''''''''''''''''''', or PbR, in the UK). One common element of performance-based compensation is the inclusion of patient service access metrics, in addition to the quality of clinical outcomes, in the process of performance evaluation for a provider of healthcare services. For example, the implementation of the ``Payment-by-Results'''''''''''''''''''''''''''''''' approach includes appointment scheduling targets designed to shorten patient waiting time, and adherence to these targets is monitored through a dedicated online outpatient appointment system, ``Choose-and-Book''''''''''''''''''''''''''''''''.

The goal of our research is to build a unified performance-based contracting (PBC) framework that incorporates patient access-to-care requirements and that explicitly accounts for the complex outpatient care dynamics facilitated by the use of an online appointment scheduling system. In our model, a service provider needs to allocate his service capacity among three patient groups: urgent patients whose service cannot be postponed, and two groups of non-urgent patients, dedicated patients who insist on getting served by their first-choice provider and flexible patients who will choose another provider if the online appointment system shows no available appointments with their first-choice provider. The principal wants to minimize her cost (payments made to the provider offset by the waiting-time penalty) of achieving the expected waiting-time target. We model the appointment dynamics in the presence of a mixed-patient population as that of an M/D/1 queue and analyze several contracting approaches under adverse selection (asymmetric information) and moral hazard (private actions) settings. We study the first-best and the second-best solutions, as well as their specific contracting implementation schemes. Our results show that simple and popular schemes used in practice cannot implement the first-best solution and that the linear PBC cannot implement the second-best solution. In order to overcome these limitations, we propose a threshold-penalty PBC approach and show that it coordinates the system for an arbitrary patient mix and that it achieves the second-best performance for the setting where all patients are dedicated.