Publication Type

Journal Article

Version

submittedVersion

Publication Date

10-2024

Abstract

Weaning patients from mechanical ventilators is a crucial decision in intensive care units (ICUs), significantly affecting patient outcomes and the throughput of ICUs. This study aims to improve the current extubation protocols by incorporating predictive information on patient health conditions. We develop a discrete-time, finite-horizon Markov decision process with predictions of future state to support extubation decisions. We characterize the structure of the optimal policy and provide important insights into how predictive information can lead to different decision protocols. We demonstrate that adding predictive information is always beneficial, even if physicians place excessive trust in the predictions, as long as the predictive model is moderately accurate. Using a comprehensive dataset from an ICU in a tertiary hospital in Singapore, we evaluate the effectiveness of various policies and demonstrate that incorporating predictive information can reduce ICU length of stay by up to 3.4% and, simultaneously, decrease the extubation failure rate by up to 20.3%, compared to the optimal policy that does not utilize prediction. These benefits are more significant for patients with poor initial conditions upon ICU admission. Both our analytical and numerical findings suggest that predictive information is particularly valuable in identifying patients who could benefit from continued intubation, thereby allowing for personalized and delayed extubation for these patients.

Keywords

Intensive care unit, mechanical ventilation, extubation, predictive information, treatment effect

Discipline

Health and Medical Administration | Operations and Supply Chain Management

Research Areas

Operations Management

Publication

Management Science

First Page

1

Last Page

23

ISSN

0025-1909

Identifier

10.1287/mnsc.2021.01427

Publisher

Institute for Operations Research and Management Sciences

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1287/mnsc.2021.01427

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