A capacity sharing approach to manage jointly transportation and emergency fleets at EMS organisations

ABSTRACT

Emergency Medical Services (EMS) first mission is to reach people requiring urgent medical attention and transport them to hospitals or care facilities. In many cases, EMS also provide a second mission, which concerns the non-emergency transportation of patients. These services have different characteristics and goals from a managerial standpoint and in practice, most EMS organisations split their fleet into two sub-fleets that are managed independently. However, both missions are in most of the cases carried out by the same types of ambulances and crews, suggesting that managing both fleets together might bring potential advantages. This study explores the potential advantages of a new management strategy that allows sharing resources between two separated ambulance fleets. In particular, the proposed strategy allows for dynamically modifying the size of each fleet considering that a subset of ambulances can change their mission during the day to better adapt to the system's state. This strategy offers an incomplete integration of the fleets, but has the worthy advantages of improving the overall system performance and being simple to implement by an EMS organisation. Numerical experiments on realistic instances demonstrate, using a discrete event simulation tool, the feasibility and benefits of the proposed strategy.

KEYWORDS:

• Ambulance management
• medical transportation
• emergency medical services

Acknowledgments

This research was partially supported by the Natural Sciences and Engineering Research Council of Canada through the Discovery Grants Program. The authors are also grateful for the remarks of the two anonymous referees, which greatly helped to improve the manuscript.

Data availability statement

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 from a statistical standpoint: the confidence interval around the average of the difference between ShP and Ind contains the value 0.

Additional information

Notes on contributors

Yannick Kergosien

Yannick Kergosien is an Associate Professor in the Department of Computer Sciences at Polytech Tours and a researcher at the Laboratory of Fundamental and Applied Computer Science (LIFAT). He holds an engineering degree (2007) Computer Sciences from Polytech Tours (France), an M.Sc. (2007) in Computer Sciences from University of Tours (France), and a Ph.D. (2010) in Computer Science from University of Tours (France). His research interests are focused on operations research in healthcare, scheduling and transportation science.

Valérie Bélanger

Valérie Bélanger is an associate professor at the Department of Logistics and Operations Management, HEC Montreal, in Canada. She holds an M.Sc. in mechanical engineering (Université Laval), as well as a Ph.D. in decision sciences (HEC Montréal). Her main research interests focus on health care logistics and emergency service management, both in the context of industrialised and developing countries. Her main methodological expertise is operations research, She is working actively with various organisations from the health care sector on projects related to patient and material transportation, and network design. She is a member of the Interuniversity Research Center on Enterprise Networks, Logistics and Transportation (CIRRELT).

Angel Ruiz

Angel Ruiz, Ph.D., is a full professor at the Faculty of business administration, Université Laval, in Canada. He earned his doctoral degree in control systems at the University of Technology of Compiegne, France. He is a member of the Interuniversity Research Center on Enterprise Networks, Logistics, and Transportation (CIRRELT). His main research interests are operations research applied to healthcare systems and emergency logistics management. He has studied diverse topics such as the development of decision support tools for waiting lists management and patient prioritisation, the design of logistics plans for the control of contagious or non-contagious diseases, the transportation of patients and the management of emergency medical services among others, and has contributed to other applied problems on healthcare logistics including the transportation and supply of goods, and the optimisation of logistics processes such as surgery or homecare activities.