Assessment of ambulance services performance by queuing theory, at the Center for Disaster and Emergency Management: A descriptive-analytical study

Mehdi Nasr Isfahani, MD; Azar Niknam, PhD Student; Mahoobeh Doosti-Irani, PhD Student

doi:10.5055/jem.0550

Authors

Mehdi Nasr Isfahani, MD
Azar Niknam, PhD Student
Mahoobeh Doosti-Irani, PhD Student

DOI:

https://doi.org/10.5055/jem.0550

Keywords:

emergency medical services, ambulance, queuing theory

Abstract

Background: The emergency departments of the hospitals and emergency medical services (EMSs) centers have a critical role for providing urgent medical care for patients. The statistical data of the present study were provided by the EMS headquarters of the city of Isfahan, from August to November 2017.

Results: The findings showed that on average, 210 missions were accomplished each day by the emergency call center, with an average duration of about 53 minutes, for each mission. In addition, the average time for response time (the time between a call and dispatch of the ambulance) was less than 3 minutes, and the average time for arrival time (the time between request of ambulance and the arrival to the scene) was 8.1 minutes. Adequacy of current number of ambulances and staff is evaluated.

Conclusion: Considering an average of 8.1 minutes for arrival time, we conclude that the EMS of Isfahan is within an acceptable range, compared to the international standards. In fact, it is shown that the infrastructures of EMS system including ambulance fleets, staff, and equipment are sufficient, and as an effective step for reducing the total time of the mission, the EMS has to operate seamlessly with the patient’s admission process in hospitals. Information such as workload hours, availability of resources and staff, etc. ought to be shared between the EMS and the hospital.

Author Biographies

Mehdi Nasr Isfahani, MD

Assistant Professor, Department of Emergency Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

Azar Niknam, PhD Student

PhD Student of Industrial Engineering, Department of Industrial Engineering, Yazd University, Yazd, Iran

Mahoobeh Doosti-Irani, PhD Student

PhD Student of Statistics, Department of Mathematical Sciences, Isfahan University of Technology, Isfahan, Iran

References

Isfahani MN, Davari F, Azizkhani R, et al.: Decreased emergency department overcrowding by discharge lounge: A computer simulation study. Int J Prevent Med. 2020; 11: 13.

Rafieian A, Valikhany A, Alinaghian M: A mathematical model for efficient ambulance location based on DSM-MALP integration. J Emerg Manag. 2020; 18(2): 153-162.

Henderson SG, Mason AJ: Ambulance service planning: Simulation and data visualisation. Oper Res Health Care. 2005; 70: 77-102.

Anbari E, Yarmohammadian M, Isfahani M: From investigation of hospital protocols and guidelines to designing a generic protocol for responding to chemical, biological, radiological, and nuclear incidents. Int J Health Syst Disas Manag. 2015; 3(4): 195-199.

Nasr-Esfahani M, Hatami M-H, Shaybani-Tehrani D, et al.: The quality of life of the staff of emergency departments of hospitals in Isfahan Province, Iran, in year 2016. Health Inform Manag J. 2019; 16(1): 24-30.

Abolhassani F, Tavakol M: A survey of satisfaction of people accompanying the patient in emergency department. Avicenna J Clin Med. 1995; 2(1).

Kirsch T: Emergency medicine around the world. Ann Emerg Med. 1998; 32(2): 237-238.

Nasr A, Alimohammadi N, Isfahani MN, et al.: Development and domestication of a clinical guideline for pharmacological pain management of trauma patients in prehospital setting. Arch Trauma Res. 2019; 8(2): 111.

Davari F, Isfahani MN, Rezvani M, et al.: Process management model in the emergency department of a university hospital: Reduction of patient waiting times by changes in human resources. J Res Med Dent Sci. 2018; 6(2): 578-585.

Singer M, Donoso P: Assessing an ambulance service with queuing theory. Comput Oper Res. 2008; 35(8): 2549-2560.

Restrepo M, Henderson SG, Topaloglu H: Erlang loss models for the static deployment of ambulances. Health Care Manag Sci. 2009; 12(1): 67.

Bakke HK, Wisborg T: Rural high north: A high rate of fatal injury and prehospital death. World J Surg. 2011; 35(7): 1615-1620.

Swain AH, Al-Salami M, Hoyle SR, et al.: Patient satisfaction and outcome using emergency care practitioners in New Zealand. Emerg Med Australas. 2012; 24(2): 175-180.

Su Q, Luo Q, Huang S: Cost-effective analyses for emergency medical services deployment: A case study in Shanghai. Int J Prod Econ. 2015; 163: 112-123.

Cheng J, Xu Z, Zhao D, et al.: The burden of extreme heat and heatwave on emergency ambulance dispatches: A time-series study in Huainan, China. Sci Total Environ. 2016; 571: 27-33.

Swalehe M, Aktas SG: Dynamic ambulance deployment to reduce ambulance response times using geographic information systems: A case study of Odunpazari District of Eskisehir Province, Turkey. Procedia Environ Sci. 2016; 36: 199-206.

Bidari A, Abbasi S, Farsi D, et al.: Evaluation of pre-hospital emergency operation in patients transferred to Hazrat Rasoul-e-Akram Hospital (PU). Med J Univ Med Sci Tabriz. 2007; 29(3): 4.

Bagheri Nezhad J, Niknam A: Analysis of the functioning of the private-ambulance services center based on queuing systems: A case study of a private ambulance service center in Tehran. Najaf Abad, Isfahan, Iran: First National Conference on Industrial Engineering and Systems, Islamic Azad University of Najaf Abad. 2012.

Hajinbi K, Riyahi L, Gholipour Varaki H: Indicators of pre-hospital emergency time in traffic accidents in Tehran with the mortality rate in the victims’ scene. Health Inform Manag. 2014; 11(3): 353-361.

Taylor C, Williamson D, Sanghvi A: When is a door not a door? The difference between documented and actual arrival times in the emergency department. Emerg Med J. 2006; 23(6): 422-443.