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

Abstract Comparative studies of emergency-department (ED) patient-prioritization rules frequently rely on single, average-based metrics, obscuring clinically important trade-offs. We introduce a three-part evaluation framework that combines tail-sensitive summary statistics, threshold-attainment curves, and stakeholder-informed utility analysis, and apply it in a discrete-event simulation of a 30-bed mixed-acuity ED. Tail statistics expose extremes—for example, the 99th-percentile length-of-stay (LOS) gap between strategies exceeds ten times the corresponding mean gap—and reveal cohort-specific inequities masked by overall averages. Threshold-attainment curves map the fraction of patients meeting every LOS target, showing where strategy rankings switch as service standards tighten or relax. Utility contours translate multi-cohort performance into a single stakeholder score, demonstrating how preferred rules pivot when institutional priorities shift from rapid discharge of low-acuity patients to protection of higher-acuity throughput. Together, the techniques uncover complementary, non-redundant information: each one highlights strengths and weaknesses invisible to the others. The framework therefore offers a transparent, replicable template for selecting prioritization policies that align with local clinical objectives, resource constraints, and risk tolerances. Competing Interest Statement The authors have declared no competing interest. Funding Statement The author(s) received no specific funding for this work. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study did not involve human participants, identifiable human data, or clinical interventions, and therefore did not require IRB approval or oversight. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability We made the data and code publicly available on the following GitHub repository: https://github.com/adamdeho/ER-Evaluation-Techniques.git.