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Novel IT Application for Reverse Triage Selection: A Pilot Study.

Gwen Pollaris1, Stéphanie Note1, Didier Desruelles1

  • 1Emergency Department,University Hospitals of Leuven,Leuven,Belgium.

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|November 11, 2017
PubMed
Summary

A new Reverse Triage Tool of Leuven (RTTL) application helps identify more patients for early discharge during mass casualty incidents. This evidence-based tool significantly improves patient selection efficiency and predictive validity.

Keywords:
IT applicationdisastermass casualty incidentreverse triagesurge capacity

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Area of Science:

  • Disaster Medicine
  • Public Health Preparedness
  • Health Informatics

Background:

  • Mass casualty incidents (MCIs) require efficient patient management and resource allocation.
  • Reverse triage aims to identify patients suitable for early discharge to optimize hospital capacity.
  • Clinical decision-making during reverse triage can be challenging and time-consuming.

Purpose of the Study:

  • To develop and evaluate an evidence-based information technology (IT) application for reverse triage.
  • To guide clinical decision-making in selecting patients for early discharge during MCIs.
  • To assess the efficiency and predictive validity of the developed tool.

Main Methods:

  • Developed the Reverse Triage Tool of Leuven (RTTL) based on 28 validated critical interventions (CI).
  • Integrated the RTTL with the health electronic record (HER) system at UZ Leuven.
  • Collected data from two patient groups over 3 weeks: one using RTTL (filtered group) and one not (random group).

Main Results:

  • The RTTL group had nearly double the number of patients selected for early discharge compared to the random group.
  • The predictive validity of the RTTL for identifying dischargeable patients was highly satisfactory.
  • The RTTL reduced the patient population requiring evaluation for early discharge by one-third.

Conclusions:

  • The RTTL application significantly enhances the efficiency of the reverse triage process in MCIs.
  • The tool doubles the probability of selecting appropriate patients for early discharge.
  • Further research is needed to optimize the IT application, with multidisciplinary reassessment remaining crucial.