Evidence map›Paper›PMID 37893787›Full record

ArticleHealthcare (Basel, Switzerland)2023

Hospital Resource Planning for Mass Casualty Incidents: Limitations for Coping with Multiple Injured Patients.

Daniel Staribacher, Marion Sabine Rauner, Helmut Niessner

Abstract read
In one paragraph

Article in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Daniel StaribacherMedical University Vienna, Spitalgasse 23, A-1090 Vienna, Austria.ORCID 0000-0001-5793-8436
Marion Sabine RaunerDepartment of Business Decisions and Analytics, Faculty of Business, Economics, and Statistics, University of Vienna, Oskar-Morgen-Stern-Platz 1, A-1090 Vienna, Austria.ORCID 0000-0002-3306-3097
Helmut NiessnerSimPlan Optimizations e. U., Ilse-Arlt-Straße 12/161, A-1220 Vienna, Austria.ORCID 0000-0001-7476-3252

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Using a discrete-event simulation (DES) model, the current disaster plan regarding the allocation of multiple injured patients from a mass casualty incident was evaluated for an acute specialty hospital in Vienna, Austria. With the current resources available, the results showed that the number of severely injured patients currently assigned might have to wait longer than the medically justifiable limit for lifesaving surgery. Furthermore, policy scenarios of increasing staff and/or equipment did not lead to a sufficient improvement of this outcome measure. However, the mean target waiting time for critical treatment of moderately injured patients could be met under all policy scenarios. Using simulation-optimization, an optimal staff-mix could be found for an illustrative policy scenario. In addition, a multiple regression model of simulated staff-mix policy scenarios identified staff categories (number of radiologists and rotation physicians) with the highest impact on waiting time and survival. In the short term, the current hospital disaster plan should consider reducing the number of severely injured patients to be treated. In the long term, we would recommend expanding hospital capacity-in terms of both structural and human resources as well as improving regional disaster planning. Policymakers should also consider the limitations of this study when applying these insights to different areas or circumstances.

Indexed as

disaster managementdiscrete-event simulationhospitalmass casualty incidentsmultiple regressionresource allocationsimulation-optimizationtrauma patient treatmenttriage

Identifiers

PMID37893787
PMCPMC10606697

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.