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

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 modeling 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.

Geographical network analysis and multiobjective 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 and Environment were included. We used the Nondominant Sorting Genetic Algorithm II to conduct a multiobjective 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.

Modeling 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), four to five Level II centers, and 12 to 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), two to five Level II centers, and eight to ten Level III centers.

The modeling 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 modeling to inform decisions about the ongoing efficiency of trauma systems. However, modeling on its own cannot guarantee improved patient outcome; thus, the use of model results for decision making should take into account wider contextual information.

Epidemiological, Level IV.