Transparent comparisons of Emergency-Department prioritization policies: integrating tail risk, target attainment, and utility analysis

Studies comparing emergency department (ED) patient prioritization rules often use single averages, which can hide important clinical trade-offs. This paper presents and demonstrates a three-part evaluation framework designed for clear, multi-faceted comparisons of prioritization policies. The framework includes: (1) statistics that account for extreme outcomes, (2) profiles showing how well time targets are met, and (3) analysis based on stakeholder priorities. We illustrate the framework in a unified discrete-event simulation of a 30-bed mixed-acuity ED to show how conclusions can change across tails, thresholds, and stakeholder preferences; the numerical results are for illustration only and are not recommendations for any specific hospital. Our main contribution is the method itself: a consistent and repeatable way to reveal different but complementary information, helping decision-makers match policies to their local goals, limits, and risk tolerance. Before implementation, future work should apply this framework using data from specific hospitals and gathering input from their stakeholders.

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Publication Details

Journal
PLoS ONE
Published
2025-12-31
DOI
https://doi.org/10.1371/journal.pone.0326722
Primary Topic
Healthcare Operations and Scheduling Optimization
Type
article
Field-Weighted Citation Impact
0.00
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article

Transparent comparisons of Emergency-Department prioritization policies: integrating tail risk, target attainment, and utility analysis

Adam DeHollander, Sabrina Casucci, Mark Karwan
PLoS ONE
Healthcare Operations and Scheduling Optimization
article

Transparent comparisons of Emergency-Department prioritization policies: integrating tail risk, target attainment, and utility analysis

Adam DeHollander, Sabrina Casucci, Mark Karwan
article en

Abstract

Studies comparing emergency department (ED) patient prioritization rules often use single averages, which can hide important clinical trade-offs. This paper presents and demonstrates a three-part evaluation framework designed for clear, multi-faceted comparisons of prioritization policies. The framework includes: (1) statistics that account for extreme outcomes, (2) profiles showing how well time targets are met, and (3) analysis based on stakeholder priorities. We illustrate the framework in a unified discrete-event simulation of a 30-bed mixed-acuity ED to show how conclusions can change across tails, thresholds, and stakeholder preferences; the numerical results are for illustration only and are not recommendations for any specific hospital. Our main contribution is the method itself: a consistent and repeatable way to reveal different but complementary information, helping decision-makers match policies to their local goals, limits, and risk tolerance. Before implementation, future work should apply this framework using data from specific hospitals and gathering input from their stakeholders.

PLoS ONEVol. 20(12)
University at Buffalo, State University of New York (US)
Peace, Justice and strong institutions
Openalex Percentile: Top 23%
Healthcare Operations and Scheduling Optimization
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