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Evaluating emergency medical service performance measures

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Abstract

The ultimate goal of emergency medical service systems is to save lives. However, most emergency medical service systems have performance measures for responding to 911 calls within a fixed timeframe (i.e., a response time threshold), rather than measures related to patient outcomes. These response time thresholds are used because they are easy to obtain and to understand. This paper proposes a methodology for evaluating the performance of response time thresholds in terms of resulting patient survival rates. A model that locates ambulances to optimize patient survival rates is used for base comparison. Results are illustrated using real-world data collected from Hanover County, Virginia. The results indicate that locating ambulances to maximize seven and eight min response time thresholds simultaneously maximize patient survival. Nine and 10 min response time thresholds result in more equitable patient outcomes, with improved patient survival rates in rural regions.

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References

  1. Fitch J (2005) Response times: Myths, measurement and management. J Emerg Med Serv 30(9):46–56

    Article  Google Scholar 

  2. Erkut E, Ingolfsson A, Erdogan G (2008) Ambulance location for maximum survival. Nav Res Logist 55(1):42–55

    Article  Google Scholar 

  3. Davis R (2007) Atlanta becomes a template for improving cardiac-arrest survival rates. USA Today

  4. Stiell IG, Wells GA, Field BJ (1999) Improved out-of-hospital cardiac arrest survival through the inexpensive optimization of an existing defibrillation program: opals study phase ii. Ontario prehospital advanced life support. N Engl J Med 351(7):647–656

    Article  Google Scholar 

  5. Davis R (2005) Only strong leaders can overhaul EMS. USA Today

  6. Nichol G, Detsky AS, Stiell IG, Or K, Wells G, Laupacis A (2002) Effectiveness of emergency medical services for victims of out-of-hospital cardiac arrest: a metaanalysis. Ann Emerg Med 27:700–710

    Article  Google Scholar 

  7. Mosesso VN Jr., Newman MM, Ornato JP, Paris PM (2002) Law enforcement agency defibrillation (LEA-D): proceedings of the national center for early defibrillation police aed issues forum. Resuscitation 54:15–26

    Article  Google Scholar 

  8. McManus S (2001) The critical need for public prehospital care in Canada: utilizing the efficiencies of a fire-based EMS system. Report, Internation Association of Fiew Fighters, Ottawa, Ontario, Canada

    Google Scholar 

  9. Pons PT, Haukoos JS, Bludworth W, Cribley T, Pons KA, Markovchick VJ (2005) Paramedic response time: does it affect patient survival? Acad Emerg Med 12(7):594–600

    Article  Google Scholar 

  10. Ludwig GG (2008) EMS response time standards, July 8 2008. http://publicsafety.com/print/EMS-Magazine/EMS-Response-Time-Standards/1$2255

  11. Brotcorne L, Laporte G, Semet F (2003) Ambulance location and relocation models. Eur J Oper Res 147:451–463

    Article  Google Scholar 

  12. Goldberg JB (2004) Operations research models for the deployment of emergency service vehicles. EMS Manage J 1(1):20–39

    Google Scholar 

  13. Green LV, Kolesar PJ (2004) Improving emergency responsiveness with management science. Manage Sci 50:1001–1014

    Article  Google Scholar 

  14. Swersey AJ (1994) Operations research and the public sector. In: Handbooks in operations research and management science, vol 6, chapter The deployment of police, fire, and emergency medical units, pp 151–200. ElseVier, North-Holland

    Google Scholar 

  15. McLay LA (2009) A maximum expected covering location model with two types of servers. IIE Trans 41(8):730–741

    Article  Google Scholar 

  16. Daskin MS (1983) A maximum expected covering location model: formulation, properties and heuristic solution. Transp Sci 17(1):48–70

    Article  Google Scholar 

  17. Church R, ReVelle C (1974) The maximal covering location problem. Pap Reg Sci Assoc 32:101–108

    Article  Google Scholar 

  18. Batta R, Dolan JM, Krishnamurthy NN (1989) The maximal expected covering location problem: revisited. Transp Sci 23(4):277–287

    Article  Google Scholar 

  19. Larson RC (1974) A hypercube queuing model for facility location and redistricting in urban emergency services. Comput Oper Res 1(1):67–95

    Article  Google Scholar 

  20. Larson RC (1975) Approximating the performance of urban emergency service systems. Oper Res 23(5):845–868

    Article  Google Scholar 

  21. Jarvis JP (1975) Optimization in stochastic systems with distinguishable servers. Technical Report tr-19-75, MIT, Cambridge, MA

  22. Jarvis JP (1985) Approximating the equilibrium behavior of multi-server loss systems. Manage Sci 31(2):235–239

    Article  Google Scholar 

  23. Chelst KR, Jarvis JP (1979) Estimating the probability distribution of travel times for urban emergency service systems. Oper Res 27(1):199–204

    Article  Google Scholar 

  24. Chelst KR, Barlach Z (1981) Multiple unit dispatches in emergency services: models to estimate system performance. Manage Sci 27(12):1390–1409

    Article  Google Scholar 

  25. Larson RC, McKnew MA (1982) Police patrol-initiated activities within a systems queuing model. Manage Sci 28(7):759–774

    Article  Google Scholar 

  26. Larsen MP, Eisenberg MS, Cummins RO, Hallstrom AP (1993) Predicting survival from out-of-hospital cardiac arrest-a graphic model. Ann Emerg Med 22:1652–1658

    Article  Google Scholar 

  27. De Maio VJ, Stiell IG, Welss GA, Spaite DW (2003) Optimal defibrillation response intervals for maximum out-of-hospital cardiac arrest survival rates. Ann Emerg Med 42:242–250

    Article  Google Scholar 

  28. Valenzuela TD, Roe DJ, Cretin S, Spaite DW, Larsen MP (1997) Estimating effectiveness of cardiac arrest intervention-a logistic regression survival model. Circulation 96:3308–3313

    Google Scholar 

  29. Waaelwijn RA, de Vos R, Tijssen JGP, Koster RW (2001) Survival models for out-of-hospital cardiopulmonary resuscitation from the perspectives of the bystander, the first responder, and the paramedic. Resuscitation 51:113–122

    Article  Google Scholar 

  30. McLay LA (2009) Emergency medical service systems that improve patient survivability. In: Encyclopedia of operations research. Wiley, Hoboken

    Google Scholar 

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Acknowledgements

It is a pleasure to acknowledge Battalion Chief Henri Moore, Jr., Chief Fred C. Crosby, II, Mr. Lawrence Roakes, and Mr. Edward Buchanan of the Hanover County Fire and EMS Department in Hanover County, Virginia, for the knowledge, experience, and data they provided to support this research effort. Suggestions for the medical component of this research from Dr. Joseph P. Ornato, M.D., are gratefully acknowledged. The authors wish to thank the three anonymous referees for their helpful comments and suggestions, which has resulted in a significantly improved manuscript.

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Authors and Affiliations

  1. Department of Statistical Sciences & Operations Research, Virginia Commonwealth University, P.O. Box 843083, 1001 W. Main Street, Richmond, VA, 23284, USA

    Laura A. McLay

  2. Department of Industrial Engineering, Clemson University, Freeman Hall Box 340920, Clemson, SC, 29634, USA

    Maria E. Mayorga

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  1. Laura A. McLay
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  2. Maria E. Mayorga
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Correspondence to Laura A. McLay.

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McLay, L.A., Mayorga, M.E. Evaluating emergency medical service performance measures. Health Care Manag Sci 13, 124–136 (2010). https://doi.org/10.1007/s10729-009-9115-x

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  • DOI: https://doi.org/10.1007/s10729-009-9115-x

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