Structural estimation of intertemporal externalities with application in ICU admissions
File(s)
Accepted version
Author(s)
Shen, Yiwen
Chan, Carri
Zheng, Fanyin
Escobar, Gabriel
Type
Journal Article
Abstract
Problem definition: In many service systems, the system manager needs to balance between addressing the needs of current customers and ensuring the system’s ability to serve future customers. Such balancing behavior is particularly important in capacity-constrained systems with heterogeneous service levels, in which the manager needs to decide which level of service to provide to the current customer, taking into account the intertemporal externalities of their decisions.
Methodology/results: We develop a dynamic discrete choice model to describe the decision-making process in a gate keeper system with multiple classes of servers and customers. The discount factor in the model captures how much the decision-maker internalizes the intertemporal externalities of their customer routing decisions. In contrast to most empirical studies in the literature which use a pre-specified discount factor, we establish joint identification of the discount factor and the utility parameters from data. We then apply the model to empirically study the Intensive Care Unit (ICU)
admission decisions for Emergency Department (ED) patients. Using a large hospitalization data set, we find that there is large heterogeneity in the estimated discount factors across hospitals. Via counterfactual simulations, we show that understanding the balancing behavior from data is important for hospitals to manage their ICU congestion.
Managerial implications: Our results suggest that it is important to understand how the decision-maker internalizes the intertemporal externalities from data. In addition, the balancing behavior regarding current customers and future available capacity provides a potential channel for improving system performance.
Methodology/results: We develop a dynamic discrete choice model to describe the decision-making process in a gate keeper system with multiple classes of servers and customers. The discount factor in the model captures how much the decision-maker internalizes the intertemporal externalities of their customer routing decisions. In contrast to most empirical studies in the literature which use a pre-specified discount factor, we establish joint identification of the discount factor and the utility parameters from data. We then apply the model to empirically study the Intensive Care Unit (ICU)
admission decisions for Emergency Department (ED) patients. Using a large hospitalization data set, we find that there is large heterogeneity in the estimated discount factors across hospitals. Via counterfactual simulations, we show that understanding the balancing behavior from data is important for hospitals to manage their ICU congestion.
Managerial implications: Our results suggest that it is important to understand how the decision-maker internalizes the intertemporal externalities from data. In addition, the balancing behavior regarding current customers and future available capacity provides a potential channel for improving system performance.
Date Acceptance
2026-08-20
Citation
Manufacturing & Service Operations Management
ISSN
1523-4614
Publisher
Institute for Operations Research and Management Sciences
Journal / Book Title
Manufacturing & Service Operations Management
Copyright Statement
Copyright This paper is embargoed until publication. Once published the author’s accepted manuscript will be made available under a CC-BY License in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy).
License URL
Publication Status
Accepted