Optimizing Trauma Systems: A Geospatial Analysis of the Victorian State Trauma System

Ann Surg. 2023 Feb 1;277(2):e406-e417. doi: 10.1097/SLA.0000000000004904. Epub 2023 Jan 10.

Abstract

Objective: The aim of this study was to develop a data-driven approach to assessing the influence of trauma system parameters and optimizing the configuration of the Victorian State Trauma System (VSTS).

Summary background data: Regionalized trauma systems have been shown to reduce the risk of mortality and improve patient function and health-related quality of life. However, major trauma case numbers are rapidly increasing and there is a need to evolve the configuration of trauma systems.

Methods: A retrospective review of major trauma patients from 2016 to 2018 in Victoria, Australia. Drive times and flight times were calculated for transport to each of 138 trauma receiving hospitals. Changes to the configuration of the VSTS were modeled using a Mixed Integer Linear Programming algorithm across 156 simulations.

Results: There were 8327 patients included in the study, of which 58% were transported directly to a major trauma service (MTS). For adult patients, the proportion of patients transported directly to an MTS increased with higher transport time limit, greater probability of helicopter emergency medical service utilization, and lower hospital patient threshold numbers. The proportion of adult patients transported directly to an MTS varied from 66% to 90% across simulations. Across all simulations for pediatric patients, only 1 pediatric MTS was assigned.

Conclusions: We have developed a robust and data-driven approach to optimizing trauma systems. Through the use of geospatial and mathematical models, we have modeled how potential future changes to trauma system characteristics may impact on the optimal configuration of the system, which will enable policy makers to make informed decisions about health service planning into the future.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Administrative Personnel
  • Adult
  • Algorithms
  • Child
  • Humans
  • Inpatients*
  • Quality of Life*
  • Victoria