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

Jan Olaf Jansen, Ernest E. Moore, Handing Wang, Jonathan J. Morrison, James Hutchison, Marion K. Campbell, Angela Sauaia

Research output: Contribution to journalArticle

1 Citation (Scopus)
4 Downloads (Pure)

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 languageEnglish
Pages (from-to)762-770
Number of pages9
JournalThe journal of trauma and acute care surgery
Volume84
Issue number5
Early online date24 Jan 2018
DOIs
Publication statusPublished - 1 May 2018

Fingerprint

Trauma Centers
Wounds and Injuries
Registries
Decision Making
Public Health
Costs and Cost Analysis
Research

Keywords

  • trauma systems
  • geospatial analysis
  • multi-objective optimization

Cite this

Maximizing geographical efficiency : An analysis of the configuration of Colorado’s trauma system. / Jansen, Jan Olaf; Moore, Ernest E.; Wang, Handing; Morrison, Jonathan J.; Hutchison, James; Campbell, Marion K.; Sauaia, Angela.

In: The journal of trauma and acute care surgery, Vol. 84, No. 5, 01.05.2018, p. 762-770.

Research output: Contribution to journalArticle

Jansen, Jan Olaf ; Moore, Ernest E. ; Wang, Handing ; Morrison, Jonathan J. ; Hutchison, James ; Campbell, Marion K. ; Sauaia, Angela. / Maximizing geographical efficiency : An analysis of the configuration of Colorado’s trauma system. In: The journal of trauma and acute care surgery. 2018 ; Vol. 84, No. 5. pp. 762-770.
@article{b0fbe5081f9d4b8baf0ac3cacbbb7fe6,
title = "Maximizing geographical efficiency: An analysis of the configuration of Colorado’s trauma system",
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.",
keywords = "trauma systems, geospatial analysis, multi-objective optimization",
author = "Jansen, {Jan Olaf} and Moore, {Ernest E.} and Handing Wang and Morrison, {Jonathan J.} and James Hutchison and Campbell, {Marion K.} and Angela Sauaia",
note = "ACKNOWLEDGEMENT 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.",
year = "2018",
month = "5",
day = "1",
doi = "10.1097/TA.0000000000001802",
language = "English",
volume = "84",
pages = "762--770",
journal = "The journal of trauma and acute care surgery",
issn = "2163-0755",
publisher = "Lippincott Williams and Wilkins",
number = "5",

}

TY - JOUR

T1 - Maximizing geographical efficiency

T2 - An analysis of the configuration of Colorado’s trauma system

AU - Jansen, Jan Olaf

AU - Moore, Ernest E.

AU - Wang, Handing

AU - Morrison, Jonathan J.

AU - Hutchison, James

AU - Campbell, Marion K.

AU - Sauaia, Angela

N1 - ACKNOWLEDGEMENT 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.

PY - 2018/5/1

Y1 - 2018/5/1

N2 - 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.

AB - 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.

KW - trauma systems

KW - geospatial analysis

KW - multi-objective optimization

U2 - 10.1097/TA.0000000000001802

DO - 10.1097/TA.0000000000001802

M3 - Article

VL - 84

SP - 762

EP - 770

JO - The journal of trauma and acute care surgery

JF - The journal of trauma and acute care surgery

SN - 2163-0755

IS - 5

ER -