An Integrated Approach to Improving Itinerary Completion in Coordinated Care Networks

Yiqiu Liu (), Pengyi Shi (), Jonathan E. Helm (), Mark P. Van Oyen (), Lei Ying () and Todd Hucshka ()
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Yiqiu Liu: Guangzhou International Campus, South China University of Technology, Guangzhou 511442, China
Pengyi Shi: Krannert School of Management, Purdue University, West Lafayette, Indiana 47907
Jonathan E. Helm: Kelley School of Business, Indiana University, Bloomington, Indiana 47405
Mark P. Van Oyen: Industrial & Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109
Lei Ying: Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan 48109
Todd Hucshka: Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota 55902

Manufacturing & Service Operations Management, 2025, vol. 27, issue 1, 21-39

Abstract: Problem definition : Coordinated care network (CCN) is a burgeoning paradigm where patients’ diagnosis and treatment plans are developed based on collaboration between multiple, colocated medical specialties to holistically address patients’ health needs. A primary performance metric for CCNs is how quickly patients can complete their itinerary of appointments at multiple medical services in the network. Rapid completion is critical to care delivery but also presents a major operational challenge. Because information about a patient’s condition and treatment options evolves over the course of the itinerary, care paths are not known a priori. Thus, appointments (except for the first one) cannot be reserved in advance, which may result in significant delays if capacity is not allocated properly. Methodology/results : We study capacity allocation for the patient’s first (root) appointment as the primary operational lever to achieve rapid itinerary completion in CCNs. We develop a novel queueing-based analytical framework to optimize this root appointment allocation, maximizing the proportion of patients completing care by prespecified deadlines. Our framework accounts for the complex interactions among all patients in the network through the blocking process, which contrasts with conventional siloed planning. We provide an exact characterization of the itinerary time and develop a mean-field approximation with convergence guarantees that permits tractable solutions for large-scale network problems. In a simulation case study of Mayo Clinic, our solution improves on-time completion from 60% under the current plan to more than 93%. Managerial implications : We demonstrate that root appointment allocation is a multifaceted problem and that ignoring any of those facets can lead to poor performance. Simultaneously accounting for all of these complexities makes manual template design or traditional optimization methods inadequate, highlighting the significance of our integrated approach.

Keywords: care coordination; capacity allocation; queueing networks with deadlines (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:27:y:2025:i:1:p:21-39

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