JOURNAL ARTICLE

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

  • Published In: Asia-Pacific Journal of Operational Research, 2024, v. 41, n. 2. P. 1 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Yu, Miao; Xu, Jie; Li, Xiangling; Yu, Dandan 3 of 3

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. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Asia-Pacific Journal of Operational Research. 2024/04, Vol. 41, Issue 2, p1
  • Document Type:Article
  • Subject Area:Consumer Health
  • Publication Date:2024
  • ISSN:0217-5959
  • DOI:10.1142/S0217595923500173
  • Accession Number:176465860
  • Copyright Statement:Copyright of Asia-Pacific Journal of Operational Research is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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