Average Response Curves for Treatment Time in the Emergency Department
Sebastian A. Alvarez Avenda\~no, Amy L. Cochran, Keith E. Kocher,, Brian W. Patterson, Gabriel Zayas-Cab\'an

TL;DR
This study models the impact of treatment time in the Emergency Department on admission rates, using a parametric approach to account for unmeasured confounding, and finds that longer treatment reduces admissions without increasing readmissions.
Contribution
It introduces a parametric model with latent variables to estimate treatment effects on admission rates in EDs, addressing unmeasured confounding.
Findings
Extending treatment time from 1 to 2 hours reduces admission rates from 41.6% to 32.7%.
Increasing treatment time by 30 minutes can lower admission rates by about 1%.
Treatment time extension has minimal impact on 30-day revisit and readmission rates.
Abstract
We estimate average responses curves for treatment time in the Emergency Department (ED). Extending treatment time is considered a promising solution for improving admission decisions. Providing empirical support for this solution, however, is difficult because this intervention (treatment) is a continuous time-to-event; is strongly influenced by unmeasured patient health needs and is jointly determined with the admission decision (admit vs. discharge); and may be only modifiable up to a shift in the realized time. We formalize the admission process as a directed acyclic graph and show that average responses curves for treatment time cannot be identified nonparametrically due to unmeasured confounding from patient health needs. We thus use a parametric model that includes a latent variable for health needs and a threshold regression model for the admission process. We fit this model to…
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Taxonomy
TopicsEmergency and Acute Care Studies · Healthcare Policy and Management · Healthcare Operations and Scheduling Optimization
