Coordinated Care: Capacity Allocation to Improve Itinerary Completion in Queueing Networks

64 Pages Posted: 4 Nov 2020

See all articles by Yiqiu Liu

Yiqiu Liu

Arizona State University (ASU) - Ira A. Fulton School of Engineering

Pengyi Shi

Purdue University - Krannert School of Management

Jonathan Helm

Indiana University - Kelley School of Business - Department of Operation & Decision Technologies

Mark P. Van Oyen

University of Michigan at Ann Arbor

Lei Ying

University of Michigan

Todd Huschka

Mayo Clinic

Date Written: May 19, 2020

Abstract

Coordinated care is a burgeoning paradigm where patients receive diagnosis and treatment planning involving collaboration between two or more medical specialties to facilitate rapid and effective solutions to complex conditions.

A key service metric for coordinated care organizations is how quickly they can move patients through their sequence of appointments at multiple clinical services in the network. Each patient's care path is uncertain when the appointment capacities are being planned, because information about the patient's condition evolves over the course of the patient's care process. Hence, the planning of root (first) appointments is the primary operational lever, as the other appointments evolve stochastically. In this work, we develop a discrete-time queueing network to optimize this root appointment allocation over a cyclic time horizon to maximize the proportion of patients that can complete their care by a class-dependent deadline.

The model accounts for several salient features of coordinated care networks, including parallel appointment requests, stochastic paths, and time-varying features. We provide an {exact} characterization of the sojourn time in the network with a doubly-stochastic phase-type distribution and leverage a mean-field model with convergence guarantees to address intractability. We then develop a policy improvement framework that approximates the original stochastic optimization by a sequence of linear programs (LP), where the sojourn time model is parameterized in the policy evaluation step. The LPs are computationally efficient, and our algorithm can solve large-scale stochastic optimization for networks of realistic sizes (e.g., 26 service stations). In a case study of the Mayo Clinic, our solution improves on-time completion to more than 93\%, from 60\% under the current plan. We demonstrate that this is a multifaceted problem, and that ignoring any those facets can lead to poor performance. Simultaneously accounting for all these complexities makes manual template design challenging and highlights the practical significance of our optimization algorithm.

Keywords: Healthcare Operations, Queueing Networks with Deadlines, Mean-field Model

JEL Classification: I1, C02

Suggested Citation

Liu, Yiqui and Shi, Pengyi and Helm, Jonathan and Van Oyen, Mark P. and Ying, Lei and Huschka, Todd, Coordinated Care: Capacity Allocation to Improve Itinerary Completion in Queueing Networks (May 19, 2020). Available at SSRN: https://ssrn.com/abstract=3667095 or http://dx.doi.org/10.2139/ssrn.3667095

Yiqui Liu

Arizona State University (ASU) - Ira A. Fulton School of Engineering ( email )

Tempe, AZ
United States

Pengyi Shi

Purdue University - Krannert School of Management ( email )

1310 Krannert Building
West Lafayette, IN 47907-1310
United States

Jonathan Helm (Contact Author)

Indiana University - Kelley School of Business - Department of Operation & Decision Technologies ( email )

Business 670
1309 E. Tenth Street
Bloomington, IN 47401
United States

Mark P. Van Oyen

University of Michigan at Ann Arbor ( email )

500 S. State Street
Ann Arbor, MI 48109
United States

Lei Ying

University of Michigan ( email )

Todd Huschka

Mayo Clinic ( email )

200 First Street SW
Rochester, MN (507) 284-2511 55905
United States

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
13
Abstract Views
69
PlumX Metrics