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Updated: Nov 29, 2025
Systems Analysis of the Neuroinflammatory and Hemodynamic Response to Traumatic Brain Injury
Published on: May 27, 2022
Comparing geographic information system-based estimates with trauma center registry data to assess the effects of
Robert J Winchell1, Justine Broecker, Andrew J Kerwin
1From the Departments of Surgery, Weill Cornell Medicine (R.J.W.), New York, New York; Mayo Clinic in Florida (J.B.); University of Texas Health Sciences Center (B.E.), San Antonio; and Division of Acute Care Surgery, Department of Surgery (A.J.K., M.C.), University of Florida College of Medicine - Jacksonville, Jacksonville, Florida, Texas.
Background:
Geographic information systems (GISs) are often used to analyze trauma systems. Geographic information system-based approaches can model access to a trauma center (TC), including estimates of transport time and population coverage, when accurate trauma registry and emergency medical systems (EMS) data are not available. We hypothesized that estimates of trauma system performance calculated using a standard GIS method with public data would be comparable with trauma registry data.
Methods:
A standardized GIS-based method was used to estimate metrics of TC access in a regional trauma system in which the number of TCs increased from one to three during a 3-year period. Registry data from the index TC in the system were evaluated for different periods during this evolution. The number of admissions to the TC in different periods was compared with changes predicted by the GIS-based model, and the distribution of observed ground-based transportation times was compared with the predicted distribution.
Results:
With the addition of two TCs to the system, the volume of patients transported by ground to the index TC decreased by 30%. However, the model predicted a 68% decrease in population having the shortest predicted transport time to the index TC. The model predicted the geographic trend seen in the registry data, but many patients were transported to the index TC even though it was not the closest center. Observed transport times were uniformly shorter than predicted times.
Conclusion:
The GIS-based model qualitatively predicted changes in distribution of trauma patients, but registry data highlight that field triage decisions are more complex than model assumptions. Similarly, transport times were systematically overestimated. This suggests that model assumptions, such as vehicle speed, based on normal traffic may not fully reflect emergency medical systems (EMS) operations. There remains great need for metrics to guide policy based on widely available data.
Level Of Evidence:
Epidemiological, level III.
