Health Care Operations Management

我的研究计划的目标是提高对各种医疗保健系统的运作方式的理解，并开发数学模型来支持医疗保健的设计和规划。 最近，在运筹学和运营管理的学术工作的重点已经转移到更大的重点，在这些领域的研究人员已经开发的数学模型中的基本假设的实证调查。 例如，越来越多的证据表明，“服务员”（例如，工厂的流水线工人或医院的护士）并不是以恒定的速度处理工作的。 服务器适应他们的环境（需要完成的工作量和可以完成工作的同事数量），当他们感到疲劳时，他们会放慢速度。 不太清楚的是这种自适应服务器行为对容量规划的影响。 在增加模型复杂性（通过结合对人类和系统行为的更真实的描述）与更真实的模型为规划目的提供的附加信息之间存在权衡。 从恒定处理速率模型得出的容量建议有多次优？ 当自适应服务器行为被纳入容量规划模型时，容量建议是否会以系统的方式发生变化？ 我希望开发出比现有模型更易于处理的数学模型，这些模型基于比现有模型更现实的关于服务器和用户行为以及医疗保健系统操作的假设。 我打算使用这样的模型来调查哪些方面是最重要的模型现实，以获得可靠的建议。 该研究将涉及随机建模系统，以及优化模型和统计模型。

The objectives of my research program are to improve understanding about the ways in which various health care systems operate and to develop mathematical models to support design and planning in health care. Recently, the focus of scholarly work in operations research and operations management has shifted toward greater emphasis on empirical investigation of fundamental assumptions made in the mathematical models that researchers in these fields have developed. There is increasing evidence, for example, that "servers" (for example, line workers in factories or nurses in hospitals) do not process work at a constant rate. Servers adapt to their environment (the amount of work that needs to be done and the number of coworkers available to do the work) and they slow down when they become fatigued. What is less clear are the implications of such adaptive server behaviors for capacity planning. There is a tradeoff between increasing model complexity, by incorporating a more realistic description of human and system behavior, and the additional information that more realistic models provide for planning purposes. How suboptimal are capacity recommendations that are derived from constant processing rate models? Do capacity recommendations change in systematic ways when adaptive server behavior is incorporated into capacity planning models? I hope to develop more tractable mathematical models than currently exist--models based upon more realistic assumptions about the behavior of servers and users and the operation of health care systems than existing models. I intend to use such models to investigate which aspects are most important to model realistically in order to obtain reliable recommendations. The research will involve stochastic modeling of queueing systems, as well as optimization models and statistical models.