Maximizing geographical efficiency: An analysis of the configuration of Colorado’s trauma system

ABSTRACTBackgroundTrauma 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.MethodsGeographical 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.ResultsModelling 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.ConclusionsThe 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 EvidenceLevel IV, epidemiologicalStudy typeGeospatial analysis 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 Correspondence to: Jan Jansen, Division of Acute Care Surgery, Department of Surgery, University of Alabama at Birmingham, 1720 2nd Avenue South, Birmingham, Alabama 35294-0016. Email: jan.jansen@abdn.ac.uk. Phone: (205) 975-3030 Conflicts of Interest: The authors declare no conflicts of interest. Source of Funding: This project did not receive specific funding. Disclosure: The Health Services Research Unit at the University of Aberdeen, UK, receives funding from the Chief Scientist Office of the Scottish Government Health and Social Care Directorates. The opinions expressed in this article are those of the authors alone. © 2018 Lippincott Williams & Wilkins, Inc.

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