Abstract
BACKGROUND: Trauma center designation in excess of need risks dilution of experience, reduction in research and training opportunities, and increased costs. The objective of this study was to evaluate the use of a novel data-driven approach (whole-system mathematical modelling of patient flow) to compare the configuration of an existing trauma system with a mathematically optimized design, using the State of Colorado as a case study.
METHODS: Geographical network analysis and multi-objective optimization. 105,448 patients injured in the State of Colorado between 2009 and 2013, who met the criteria for inclusion in the state mandated trauma registry maintained by the Colorado Department of Public Health & Environment were included. We used the Non-dominant Sorting Genetic Algorithm II (NSGA-II) to conduct a multi-objective optimization of possible trauma system configurations, with the objectives of minimizing total system access time, and the number of casualties who could not reach the desired level of care.
RESULTS: Modelling suggested that system configurations with high volume level I trauma centers could be mathematically optimized with two centers rather than the current three (with an estimated annual volume of 970-1,020 and 715-722 severely injured patients per year), 4-5 level II centers, and 12-13 level III centers. Configurations with moderate volume level I centers could be optimized with three such centers (with estimated institutional volumes of 439-502, 699-947, and 520-726 severely injured patients per year), 2-5 level II centers, and 8-10 level III centers.
CONCLUSIONS: The modelling suggested that the configuration of Colorado's trauma system could be mathematically optimized with fewer trauma centers than currently designated. Consideration should be given to the role of optimization modelling to inform decisions about the ongoing efficiency of trauma systems. However, modelling on its own cannot guarantee improved patient outcome; thus the use of model results for decision-making should take into account wider contextual information.
LEVEL OF EVIDENCE: Level IV, epidemiological STUDY TYPE: Geospatial analysis.
| Original language | English |
|---|---|
| Pages (from-to) | 762-770 |
| Number of pages | 9 |
| Journal | The journal of trauma and acute care surgery |
| Volume | 84 |
| Issue number | 5 |
| Early online date | 24 Jan 2018 |
| DOIs | |
| Publication status | Published - 1 May 2018 |
Bibliographical note
ACKNOWLEDGEMENTThe data used for this study were supplied by the Health Facilities and Emergency Medical Services Division of the Colorado Department of Public Health and Environment, which specifically disclaims responsibility for any analyses, interpretations, or conclusions it has not provided.
The data used for this study were supplied by the Health Facilities and Emergency Medical Services Division of the Colorado Department of Public Health and Environment, which specifically disclaims responsibility for any analyses, interpretations, or conclusions it has not provided.
Keywords
- trauma systems
- geospatial analysis
- multi-objective optimization
Access to Document
- Accepted Manuscript
This is a non-final version of an article published in final form in Journal of Trauma and Acute Care Surgery.
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Marion Campbell
- School of Medicine, Medical Sciences & Nutrition, Health Services Research Unit (HSRU) - Chair in HSRU
- Institute of Applied Health Sciences
Person: Academic