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Temporal Trends and Future Predictions of Regional EMS System Utilization Using Statistical Modeling

Published online by Cambridge University Press:  06 December 2019

Michael J. Carr Open the ORCID record for Michael J. Carr [Opens in a new window] ,
Robert Bauter ,
Philip Shepherd ,
Vincent Robbins ,
A.J. McKechnie ,
Beatrice Cappuccia  and
Mark A. Merlin
Michael J. Carr*
Affiliation:
MONOC Hospital Service Corp EMS System, Wall Township, Neptune, New JerseyUSA Assistant Professor of Emergency Medicine, Emory University, Atlanta, GeorgiaUSA
Robert Bauter
Affiliation:
MONOC Hospital Service Corp EMS System, Wall Township, Neptune, New JerseyUSA Director of Clinical Services, MONOC Hospital Service Corp, Wall Township, Neptune, New JerseyUSA
Philip Shepherd
Affiliation:
MONOC Hospital Service Corp EMS System, Wall Township, Neptune, New JerseyUSA Clinical Technology Coordinator, MONOC Hospital Service Corp, Wall Township, Neptune, New JerseyUSA
Vincent Robbins
Affiliation:
MONOC Hospital Service Corp EMS System, Wall Township, Neptune, New JerseyUSA Chief Executive Officer and President, MONOC Hospital Service Corp, Wall Township, Neptune, New JerseyUSA
A.J. McKechnie
Affiliation:
MONOC Hospital Service Corp EMS System, Wall Township, Neptune, New JerseyUSA Department of Emergency Medicine, Newark Beth Israel Medical Center, Newark, New JerseyUSA
Beatrice Cappuccia
Affiliation:
Department of Emergency Medicine, Newark Beth Israel Medical Center, Newark, New JerseyUSA
Mark A. Merlin
Affiliation:
MONOC Hospital Service Corp EMS System, Wall Township, Neptune, New JerseyUSA Department of Emergency Medicine, Newark Beth Israel Medical Center, Newark, New JerseyUSA Chief Medical Officer and Medical Director, MONOC Hospital Service Corp, Wall Township, Neptune, New JerseyUSA
*
Correspondence: Michael J. Carr, MD Department of Emergency Medicine Emory University School of Medicine 531 Asbury Circle, Annex Suite N340 Atlanta, Georgia 30322 USA E-mail: [email protected]
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Abstract

Introduction:

Trends in utilization of Emergency Medical Services (EMS) systems can be used to extrapolate future use of an EMS system, which will be valuable for the budgeting and planning of finances and resources. The best model for incorporation of seasonal and regional fluctuations in utilization to predict future utilization is unknown.

Problem:

Authors aimed to trend patterns of utilization in a regional EMS system to identify the needs of a growing population and to allow for a better understanding of how the EMS system is used on a basis of call volume and frequency of EMS transportation. The authors then used a best-fitting prediction model approach to show how the studied EMS system will be used in future years.

Methods:

Systems data were retrospectively extracted by using the electronic medical records of the studied EMS system and its computer-assisted dispatch (CAD) database from 2010 through 2017. All EMS dispatches entering the system’s 9-1-1 public service access point were captured. Annual utilization data were available from 2010 through 2017, while quarterly data were available only from 2013 through 2017. The 9-1-1 utilization per capita, Advanced Life Support (ALS) utilization per capita, and ALS cancel rates were calculated and trended over the study period. The methods of prediction were assessed through a best-fitting model approach, which statistically suggested that Additive Winter’s approach (SAS) was the best fit to determine future utilization and ALS cancel rates.

Results:

Total 9-1-1 call volume per capita increased by 32.46% between 2010 and 2017, with an average quarterly increase of 0.78% between 2013 and 2017. Total ALS call volume per capita increased by 1.93% between 2010 and 2017. Percent ALS cancellations (cancelled en route to scene) increased by eight percent between 2010 and 2017, with an average quarterly increase of 0.42% (2013–2017). Predictions to end of 2019 using Additive Winter’s approach demonstrated increasing trends in 9-1-1 call volume per capita (R2 = 0.47), increasing trends of ALS utilization per capita (R2 = 0.71), and increasing percent ALS cancellation (R2 = 0.93). Each prediction showed increasing future trends with a 95% confidence interval.

Conclusions:

The authors demonstrate paramount per capita increases of 9-1-1 call volume in the studied ALS system. There are concomitant increases of ALS cancellations prior to arrival, which suggests a potential burden on this regional ALS response system.

Keywords

911 call volumeALS system utilizationEMS utilizationutilization forecastutilization prediction
Type
Original Research
Information
Prehospital and Disaster Medicine , Volume 35 , Issue 1 , February 2020 , pp. 32 - 40
Copyright
© World Association for Disaster and Emergency Medicine 2019

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References

Platts-Mills, TF, Leacock, B, Cabañas, JG, Shofer, FS, Mclean, SA.Emergency medical services use by the elderly: analysis of a statewide database. Prehosp Emerg Care. 2010;14(3):329–333.CrossRefGoogle ScholarPubMed
Miller, MK, Denise Dowd, M, Gratton, MC, Cai, J, Simon, SD.Pediatric out-of-hospital emergency medical services utilization in Kansas City, Missouri. Acad Emerg Med. 2009;16(6):526–531.CrossRefGoogle ScholarPubMed
Shah, MN, Cushman, JT, Davis, CO, Bazarian, JJ, Auinger, P, Friedman, B.The epidemiology of emergency medical services use by children: an analysis of the National Hospital Ambulatory Medical Care Survey. Prehosp Emerg Care. 2008;12(3):269–276.CrossRefGoogle ScholarPubMed
Gerson, LW, Shvarch, L.Emergency medical service utilization by the elderly. Ann Emerg Med. 1982;11(11):610–612.CrossRefGoogle ScholarPubMed
Shah, MN, Glushak, C, Karrison, TG, et al.Predictors of emergency medical services utilization by elders. Acad Emerg Med. 2003;10(1):52–58.CrossRefGoogle ScholarPubMed
Svenson, JE.Patterns of use of emergency medical transport: a population-based study. Am J Emerg Med. 2000;18(2):130–134.CrossRefGoogle ScholarPubMed
Brown, AL, Mann, NC, Daya, M, et al.Demographic, belief, and situational factors influencing the decision to utilize emergency medical services among chest pain patients. Rapid Early Action for Coronary Treatment (REACT) study. Circulation. 2000;102(2):173–178.CrossRefGoogle ScholarPubMed
Munjal, KG, Silverman, RA, Freese, J, et al.Utilization of emergency medical services in a large urban area: description of call types and temporal trends. Prehosp Emerg Care. 2011;15(3):371–380.CrossRefGoogle Scholar
Wrigley, H, George, S, Smith, H, Snooks, H, Glasper, A, Thomas, E.Trends in demand for emergency ambulance services in Wiltshire over nine years: observational study. BMJ. 2002;324(7338):646–647.CrossRefGoogle ScholarPubMed
Pittet, V, Burnand, B, Yersin, B, Carron, PN.Trends of pre-hospital emergency medical services activity over 10 years: a population-based registry analysis. BMC Health Serv Res. 2014;14:380.CrossRefGoogle ScholarPubMed
Cushman, JT.EMS System Design. In: Cooney, DR, (ed). Cooney’s EMS Medicine. New York, New York USA: McGraw-Hill. http://accessemergencymedicine.mhmedical.com/content.aspx?bookid=1650&sectionid=107953302. Accessed January 05, 2019.Google Scholar
United States Census Bureau: State and County Quick Facts. United States Department of Commerce. Date last modified: July 1, 2017. https://www.census.gov/quickfacts/fact/table/nj,US/PST045217. Accessed June 24, 2018.Google Scholar
United States Centers for Medicare and Medicaid Services (CMS): National Health Expenditure Projections 2017-2026 Forecast Summary. United States Department of Health and Human Services (HHS). Date last modified: February 16, 2018. https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/Downloads/ForecastSummary.pdf. Accessed June 24, 2018.Google Scholar
Cannuscio, CC, Davis, AL, Kermis, AD, Khan, Y, Dupuis, R, Taylor, JA.A strained 9-1-1 system and threats to public health. J Community Health. 2016;41(3):658–666.CrossRefGoogle ScholarPubMed
National Center for Health Statistics. Health, United States, 2016: With Chartbook on Long-Term Trends in Health. Hyattsville, Maryland USA: National Center for Health Statistics; 2017.Google Scholar
Wolbrom, DH, Rahman, AM, Hilton, MT.92 Predictors of response times for collegiate Emergency Medical Services. Ann Emerg Med. 2016;68(4):SuppS38.CrossRefGoogle Scholar
Wilde, ET.Do emergency medical system response times matter for health outcomes? Health Econ. 2013;22(7):790–806.CrossRefGoogle ScholarPubMed
Al-shaqsi, SZ. Response time as a sole performance indicator in EMS: pitfalls and solutions. Open Access Emerg Med. 2010;2:1–6.CrossRefGoogle ScholarPubMed
Wei lam, SS, Zhang, ZC, Oh, HC, et al.Reducing ambulance response times using discrete event simulation. Prehosp Emerg Care. 2014;18(2):207–216.CrossRefGoogle ScholarPubMed
Yeh, EL, Cone, DC.Cancellation of responding ALS units by BLS providers: a national survey. Prehosp Emerg Care. 2000;4(3):227–233.CrossRefGoogle ScholarPubMed
Eckstein, M, Isaacs, SM, Slovis, CM, et al.Facilitating EMS Turnaround Intervals at Hospitals in the Face of Receiving Facility Overcrowding. Prehosp Emergency Care. 2005; 9(3); 267–275.CrossRefGoogle ScholarPubMed
The Official Tourism Website Of New Jersey: Economic Impact of Tourism in New Jersey, 2017. Tourism Economics – An Oxford Economics Company. Trenton, New Jersey USA. https://www.visitnj.org/sites/default/files/2017-nj-economic-impact.pdf. Accessed June 30, 2018.Google Scholar
Robbins, VD, Merlin, MA, Shotwell, D. Ambulance Crew Configuration: Are Two Paramedics Better Than One? Journal of EMS. Tulsa, Oklahoma USA: PennWell Corporation. https://www.jems.com/articles/2018/10/ambulance-crew-configuration-are-two-paramedics-better-than-one.html. Accessed January 5, 2019.Google Scholar
Hyndman, RJ, Athanasopoulos, G. Forecasting: Principles and Practice. 7.3 Holt-Winters’ seasonal method. https://otexts.com/fpp2/holt-winters.html. Accessed August 5, 2019.Google Scholar