中国人民大学

基于性能的合同门诊医疗服务

Performance-Based Contracts for Outpatient Medical Services

英国剑桥大学商学院高级讲师/管理科学在职学习项目主任

摘要：近年来，性能为基础的方法，以承包的医疗服务

已获得在不同的医疗服务系统的普及，无论是在美国（下

按业绩付费“的”名称或P4P），和国外（“缴费结果”，或PBR，

英国）。基于绩效的薪酬的一个共同点是列入病人

服务访问度量的过程中，除了临床结果的质量

医疗服务提供商的绩效评价。例如，实施

``付款的结果“的方法，包括任命调度目标设计

缩短病人的轮候时间，并坚持这些目标是通过一个专门的监控

网上门诊预约系统，“选择和图书”。

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''.

我们的研究目标是建立一个统一的基于性能的合同（PBC）的框架结合病人获得保健的要求和明确的帐目

复杂的门诊服务，使用网上预约促进动态系统。在我们的模型中，服务提供商，需要分配在三个他的服务能力患者群体的服务不能推迟的紧急病人，两组非紧急的病人，坚持他们的第一选择服务专用患者他们将选择另一个供应商的供应商和灵活的患者，如果网上预约系统显示没有可用的任命，他们的第一选择供应商。委托人想尽量减少她的成本（提供商付款等待时间的刑罚抵消）达到了预期的等待时间的目标。我们在任命动力学模型作为一个M/D/1队列混合患者人群的存在和分析几个逆向选择（信息不对称）和道德风险下的承包办法（私人行为）设置。

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. 

我们研究的第一个最好的，第二个最好的解决方案，以及其具体的承包实施方案。我们的研究结果表明，简单和流行在实践中使用的计划不能实施的第一个最佳的解决方案和线性中国人民银行不能实施的第二个最好的解决办法。为了克服这些限制，我们建议一个门槛罚款中国人民银行的做法表明，它的坐标系统对于任意

病人的结构和它达到的最佳性能设置，所有患者的第二是专用的。

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 rbitrary

patient mix and that it achieves the second-best performance for the setting where all patients are dedicated.