A Study of Congestion-Based Information Guidance Policy for Hierarchical Healthcare Systems

Miao Yu (), Jie Xu, Xiangling Li () and Dandan Yu ()
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Miao Yu: School of Management, Shenyang Jianzhu University, Shenyang, P. R. China
Jie Xu: Department of Systems Engineering and Operations Research, George Mason University, Fairfax, VA, USA
Xiangling Li: School of Medicine and Health Management, Guizhou Medical University, Guiyang, Guizhou, P. R. China
Dandan Yu: Information Center, The First Affiliated Hospital of Dalian, Medical University, Dalian, P. R. China

Asia-Pacific Journal of Operational Research (APJOR), 2024, vol. 41, issue 02, 1-27

Abstract: This paper develops a queueing system model to analyze the operations of a hierarchical healthcare system consisting of general hospitals (GHs) and community healthcare centers (CHCs). GHs typically provide a higher level of health care service than CHCs, and thus are preferred choices for many patients’ healthcare service needs. Consequently, GHs are often heavily congested and patients often incur excessive waiting time. In contrast, CHCs are often idle and resources are underutilized. To help balance the utilization of resources in GHs and CHCs, a congestion-based information guidance policy is proposed in this paper to inform patients in the GH service queue about the anticipated delay. Upon being informed the delay for GH service, patients may balk, remain in queue for GH service, or switch to receive service at CHCs. This policy is thus expected to relieve the congestion at GHs and promote CHC usage. To study the effects of the proposed policy, a hierarchical healthcare system is modeled as a queueing system with strategic patients. Stationary performance measures of the system are analytically characterized using a Markov chain model. Stochastic and numerical analyses provide insights on how to design information guidance policy that would help improve overall health care service quality under different scenarios.

Keywords: Stochastic modeling; hierarchical health care system; queueing model; delay information; congestion-based policy (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0217595923500173

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