Pareto optimization for control agreement in patient referral coordination
Pareto optimization for control agreement in patient referral coordination
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患者转诊协调中控制协议的帕累托优化
DOI：
10.1016/j.omega.2020.102234
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发表时间：
2020-03
影响因子：
6.9
通讯作者：
Kong Nan
中科院分区：
文献类型：
--
作者：
Li Na;Zhang Yue;Teng De;Kong Nan
我国各级医院之间医疗资源利用不均衡的现象普遍存在。通过上级医院（ULH）和下级医院（LLH）的协调，鼓励ULH转诊，以便ULH可以将不太紧急的患者转移到LLH，以缓解使用不平衡。对于整个系统，患者转诊协调不仅取决于商定的转移量，而且可能更多地取决于患者流特性以及同时来自ULH和LLH的相互决策。因此，在本文中，我们研究了ULH和LLH之间的控制协议框架，帮助两家医院最大限度地提高各自的利益。更具体地说，控制协议框架允许每家医院应用基于阈值的政策来决定转诊（由ULH）或接受（由LLH）。并提出了一种基于Pareto的协商过程来实现控制协议。为了优化两家医院的阈值对，我们制定了一个帕累托优化问题，目标是两家医院的收入和货币化患者阻塞相关的利益。针对系统中患者流分析的复杂性，提出了一种基于多保真度模型的优化方法，该方法综合了离散事件仿真和离散事件仿真的优点。具体来说，我们采用了有序变换和最优抽样的思想。本文以上海市第六人民医院和第八人民医院的转诊协调实践为例，探讨了转诊协调的控制策略。我们的帕累托优化方法是适用于许多服务系统的等待时间敏感的客户。我们的工作提供了一个新的视角，业务层面的协调设计的服务外包/共享管理文献。
Imbalanced utilization of medical resources between hospitals at different levels is prevalent in China. Through coordination of an upper-level hospital (ULH) and a lower-level hospital (LLH), ULH-referrals are encouraged so that the ULH can transfer less urgent patients to the LLH to alleviate the utilization imbalance. To the entire system, the patient referral coordination is dependent not only on the agreed transfer quantity but perhaps more on the patient flow characteristics and simultaneously mutual decisions from both the ULH and the LLH. In this paper, we thus investigate a control agreement framework between the ULH and the LLH that helps both hospitals to maximize their respective benefits. More specifically, a control agreement framework allows each hospital applies a threshold-based policy to make decisions on either referral (by ULH) or acceptance (by LLH). And a Pareto-based negotiation process is proposed to carry out the control agreement. To optimize the threshold pair from both hospitals, we formulate a Pareto optimization problem with objectives being revenue-and-monetarized-patient-blockage related benefits of the two hospitals. Given the complexity in analyzing patient flows of the system, we develop a multi-fidelity model-based optimization approach, which integrates the advantages of queueing model and discrete event simulation. Specifically, we adopt the idea of ordinal transformation and optimal sampling. In a real-world case study, we investigate the control policy in light of the referral coordination practice between Shanghai No. 6 People's Hospital and No. 8 People's Hospital. Our Pareto-optimization approach is applicable to many service systems with waiting time sensitive customers. Our work provides a novel perspective of operational level coordination design for the service outsourcing/sharing management literature.
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