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Volume 2024, Issue 7
  • ISSN: 1999-7086
  • EISSN: 1999-7094

Abstract

Research in the field of EMS (emergency medical services) has been primarily limited to individual aspects of EMS systems and patient care. However, there is a need for a broad outcome-based analysis of the impacts of the overall system design. The aim of this study was to explore the associations between various EMS system design characteristics and patient outcomes post-hospital discharge.

Data indicators were obtained according to the characteristics of 30 US EMS systems in large metropolitan areas, which account for 41.5% of the US population. Examples of indicators were the density of unit deployment, the use of ALS (advanced life support) first response, and the accreditation status of the dispatch center. Data were compared with local outcome/mortality data from national healthcare databases. Partial least squares structural equation modeling was used to identify relationships between the variables.

Of the nine latent variables studied, representing 29 individual indicators, local Medicare hospital rating scores (-.366 coefficient,  < 0.001, CI: -.485,-.134) and underlying socioeconomic and public health status (-.334,  < 0.001, CI: -.474, -.138) were the primary latent variables that showed practical significance (overall model 2  = .736). Outcome measures described the relationship between the variables but cannot isolate EMS as the sole causal factor in outcomes.

None of the EMS system-specific characteristics of the variables studied were associated with decreased patient mortality. The only two variables that showed a practically significant outcome were the indicators related to socioeconomic and public health data and the Medicare ratings of local hospitals.

© 2024 Nevin, licensee HBKU Press.
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2024-11-12
2024-11-21
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References

  1. Greenwood-Ericksen, M.B., & Kocher, K. (2019). Trends in Emergency Department Use by Rural and Urban Populations in the United States. JAMA Network Open, 2(4). https://doi.org/10.1001/jamanetworkopen.2019.1919.
     [Google Scholar]
  2. Shah MN. The formation of the emergency medical services system. Am J Public Health. 2006 Mar;96(3):414–23. doi: 10.2105/AJPH.2004.048793.
     [Google Scholar]
  3. Taymour RK, Abir M, Chamberlin M, Dunne RB, Lowell M, Wahl K, et al. Policy, practice, and research agenda for emergency medical services oversight: A systematic review and environmental scan. Prehosp Disaster Med. 2018 Feb;33(1):89–97. doi: 10.1017/S1049023X17007129.
     [Google Scholar]
  4. Goldberg SA, Chadwick E, Makam S, Shah HJ, Bass MB, Hasdianda MA, et al. Twenty years after the EMS research agenda: Trends in emergency medical services publications and research funding. Ann Emerg Med. 2023 Jul;82(1):94–100. doi: 10.1016/j.annemergmed.2023.02.017.
     [Google Scholar]
  5. Howard I, Cameron P, Wallis L, Castren M, Lindstrom V. Quality indicators for evaluating prehospital emergency care: A scoping review. Prehosp Disaster Med. 2018 Feb;33(1):43–52. doi: 10.1017/S1049023X17007014.
     [Google Scholar]
  6. Myers JB, Slovis CM, Eckstein M, Goodloe JM, Isaacs SM, Loflin JR, et al. Evidence-based performance measures for emergency medical services systems: A model for expanded EMS benchmarking. Prehosp Emerg Care. 2008 Jan 2;12(2): 141–51. doi: 10.1080/10903120801903793.
     [Google Scholar]
  7. Martin-Gill C, Gaither JB, Bigham BL, Myers JB, Kupas DF, Spaite DW. National prehospital evidence-based guidelines strategy: A summary for EMS stakeholders. Prehosp Emerg Care. 2016 Mar 3;20(2):175–183. doi: 10.3109/10903127.2015.1102995.
     [Google Scholar]
  8. March AJ, Somes J, Martin-Gill C, McEvoy M, Freel J. What should you know about evidence-based guidelines. EMS World. 2019 Dec;48(10):42–6.
     [Google Scholar]
  9. Dunford J, Domeier RM, Blackwell T, Mears G, Overton J, Rivera-Rivera EJ et al. Performance measurements in emergency medical services. Prehosp Emerg Care. 2002 Jan-Mar;6(1):92–8. doi: 10.1080/10903120290938896.
     [Google Scholar]
  10. Siriwardena AN, Shaw D, Donohoe R, Black S, Stephenson J. National Ambulance Clinical Audit Steering Group. Development and pilot of clinical performance indicators for English ambulance services. Emerg Med J. 2010 Apr 1;27(4):327–31. doi: 10.1136/emj.2009.072397.
     [Google Scholar]
  11. US Census Bureau. 2020 Community Data [Internet]. 2020 [cited 2022 Dec 26]. Available from: https://www.census.gov/acs/www/data/data-tables-and-tools/data-profiles/2020/.
  12. Phan K, Tian DH, Cao C, Black D, Yan TD. Systematic review and meta-analysis: Techniques and a guide for the academic surgeon. Ann Cardiothorac Surg. 2015 Mar;4(2):112–22. doi: 10.3978/j.issn.2225-319X.2015.02.04.
     [Google Scholar]
  13. Hair JF, Hult GTM, Ringle CM, Sarstedt M, Danks NP, Ray S. Partial least squares structural equation modeling (PLS-SEM) using R [Internet]. Cham: Springer International Publishing; 2021 [cited 2023 Sep 2]. Available from: https://library.oapen.org/bitstream/handle/20.500.12657/51463/9783030805197.pdf?sequence = 1 .
     [Google Scholar]
  14. Vinzi E, Trinchera L, Amato S. Handbook of partial least squares. Berlin: Springer-Verlag; 2010.
     [Google Scholar]
  15. Haenlein M, Kaplan AM. A beginner's guide to partial least squares analysis. Understanding Statistics. 2004 Nov;3(4):283–297. doi: 10.1207/s15328031us0304_4.
     [Google Scholar]
  16. Hair J, Anderson R, Babin B, Tatman R, Black W. Multivariate data analysis. Upper Saddle River, NJ: Prentice Hall; 2010.
     [Google Scholar]
  17. Kline P. An easy guide to factor analysis. Routledge; 2014.
     [Google Scholar]
  18. Jarvis JL, Hamilton V, Taigman M, Brown LH. Using red lights and sirens for emergency ambulance response: How often are potentially life-saving interventions performed? Prehosp Emerg Care. 2021 Jul-Aug;25(4):549–55. doi: 10.1080/10903127.2020.1797963.
     [Google Scholar]
  19. Silverman RA, Galea S, Blaney S, Freese J, Prezant DJ, Park R, et al. The “Vertical Response Time”: Barriers to ambulance response in an urban area. Acad Emerg Med. 2007 Sep;14(9):772–8. doi: 10.1197/j.aem.2007.04.016.
     [Google Scholar]
  20. Fitch J. Response times: Myths, measurement and management. J Emerg Med Services. 2005 Sep; 30(9):47–56.
     [Google Scholar]
  21. Blackwell TH, Kaufman JS. Response time effectiveness: Comparison of response time and survival in an urban emergency medical services system. Acad Emerg Med. 2002 Apr;9(4):288–95. doi: 10.1111/j.1553-2712.2002.tb01321.x.
     [Google Scholar]
  22. Sanghavi P, Jena AB, Newhouse JP, Zaslavsky AM. Outcomes after out-of-hospital cardiac arrest treated by basic vs advanced life support. JAMA Intern Med. 2015 Feb 1;175(2):196–204. doi: 10.1001/jamainternmed.2014.5420.
     [Google Scholar]
/content/journals/10.5339/jemtac.2024.33
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The association between EMS system characteristics and post-hospital discharge patient outcomes
Journal of Emergency Medicine, Trauma and Acute Care 2024, 33 (2024); https://doi.org/10.5339/jemtac.2024.33
/content/journals/10.5339/jemtac.2024.33
/content/journals/10.5339/jemtac.2024.33
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  • Article Type: Research Article
Keyword(s): EMS patient outcomes, EMS system, EMS system design and prehospital patients

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