Improving Health Outcomes Through Better Capacity Allocation in a Community-Based Chronic Care Model

Operations Research, 61 (6), 1277-1294

19 Pages Posted: 2 Nov 2010 Last revised: 27 Mar 2019

See all articles by Sarang Deo

Sarang Deo

Indian School of Business (ISB), Hyderabad - Operations Management

Seyed Iravani

Northwestern University - Department of Industrial Engineering and Management Sciences

Tingting Jiang

Northeastern University

Karen Smilowitz

Northwestern University - Department of Industrial Engineering and Management Sciences

Stephen Samuelson

Mobile C.A.R.E. Foundation

Date Written: December 1, 2013

Abstract

This paper studies a model of community-based healthcare delivery for a chronic disease. In this setting, patients periodically visit the healthcare delivery system, which influences their disease progression and consequently their health outcomes. We investigate how the provider can maximize community-level health outcomes through better operational decisions pertaining to capacity allocation across different patients. To do so, we develop an integrated capacity allocation model that incorporates clinical (disease progression) and operational (capacity constraint) aspects. Specifically, we model the provider's problem as a finite horizon stochastic dynamic program, where the provider decides which patients to schedule at the beginning of each period. Therapy is provided to scheduled patients, which may improve their health states. Patients that are not seen follow their natural disease progression. We derive a quantitative measure for comparison of patients' health states and use it to design an easy-to-implement myopic heuristic that is provably optimal in special cases of the problem. We employ the myopic heuristic in a more general setting and test its performance using operational and clinical data obtained from Mobile C.A.R.E. Foundation, a community-based provider of pediatric asthma care in Chicago. Our extensive computational experiments suggest that the myopic heuristic can improve the health gains at the community level by up to 15% over the current policy. The benefit is driven by the ability of our myopic heuristic to alter the duration between visits for patients with different health states depending on the tightness of the capacity and the health states of the entire patient population.

Keywords: capacity allocation, chronic disease, mobile care, disease progression, appointment scheduling

Suggested Citation

Deo, Sarang and Iravani, Seyed and Jiang, Tingting and Smilowitz, Karen and Samuelson, Stephen, Improving Health Outcomes Through Better Capacity Allocation in a Community-Based Chronic Care Model (December 1, 2013). Operations Research, 61 (6), 1277-1294, Available at SSRN: https://ssrn.com/abstract=1700909 or http://dx.doi.org/10.2139/ssrn.1700909

Sarang Deo (Contact Author)

Indian School of Business (ISB), Hyderabad - Operations Management ( email )

India

HOME PAGE: http://www.isb.edu/faculty-research/faculty/directory/deo-sarang

Seyed Iravani

Northwestern University - Department of Industrial Engineering and Management Sciences ( email )

Evanston, IL 60208-3119
United States

Tingting Jiang

Northeastern University ( email )

220 B RP
Boston, MA 02115
United States

Karen Smilowitz

Northwestern University - Department of Industrial Engineering and Management Sciences ( email )

Evanston, IL 60208-3119
United States

Stephen Samuelson

Mobile C.A.R.E. Foundation ( email )

United States

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