Development of a Drone-Supported Emergency Medical Service

There is a scientific consensus that the delivery of prompt emergency medical services (EMSs) guarantees a higher survival rate. An EMS is generally able to respond to 90% of higher priority calls in less than 9 minutes, with the best chance of survival being with a response time of 4–5 minutes. The major obstacle here is that a shorter response time would require the needed resources not to pass a certain threshold in a cost/benefit analysis. This paper aims to investigate the use of drones in as an EMS to improve response times. Although the literature already provides many examples of drones used for this purpose, they have all been developed as a prototype. This confirms the technical feasibility of a drone-based solution, but there is no evidence of the economic viability for such a service. The answer to this comes by analyzing the performance of an integrated-with-drones service as a whole. For this reason, we have redesigned the entire EMS model by including drones, and we have addressed the main issues, such as which types of service can be provided from drones, in which case, what the technical requirements for drones would be, and so on. Furthermore, we developed a specific procedure to keep the number of drones at a minimum level under the constraint of the minimum intervention time. The proposed model has been applied to a real EMS case for a city in the south of Italy. The outcome was that 96 drones were able to cover an area of 2,800 km², providing an intervention time of 4.5 minutes on average at an annual cost of less than €300,000. These results highlight that an integrated-with-drones service drastically improves the response time when compared with the traditional service, doing so at a viable cost.

Cost/Benefit analysis; Design optimization; Drones; Emergency medical service

    There is a general need for an effective and timely response to emergencies (Dulebenets et al., 2019). Here, drones seem to fit well because they can be used for rescue missions (Yeong et al., 2015), environmental protection (Marris, 2013), and performing missions in oceans. One of the most promising sectors for developing drones is the healthcare field, where they can function in logistic operations and could be used for hospital deliveries (Roca-Riu and Menendez, 2019), even in remote areas (Tatsidou et al., 2019); indeed, one of the most important advantages of using drones is the potential to decrease the travel time for diagnosis and treatment (Laksham, 2019). That makes drones suitable for reducing the time and thus increasing the effectiveness of Emergency Medical Service (EMS). Hence, the current paper aims to investigate the use of drones in EMSs, which are, "a comprehensive system which provides the arrangements of personnel, facilities, and equipment for the effective, coordinated and timely delivery of health and safety services to victims of sudden illness or injury” (Moore, 1999)

        The operations for an EMS include the process of a distress call on predefined protocols and translation into an alphanumeric priority code that includes the seriousness of the reported problem and location for the intervention. Based on these criteria, the most suitable and closest rescue vehicle is identified among those available to guarantee a timely and adequate response. The most common EMS performance measure is to respond to 90% of higher priority calls in less than 9 minutes (Fitch, 2005). Otherwise, the existing recommendations provided by medical and public safety experts typically advocate for 4–5 minutes for the response time (Pons et al., 2005). Although a shorter response time interval improves patient survival, covering most calls in less than 4 minutes tends to use resources in such a way that does not save the most patient lives overall (McLay, 2010). Clearly, a response time of 9 minutes is the result of a trade-off. In fact, a response time of 4–5 minutes would require such a certain amount of resources such as vehicles, staff and equipment, to not pass the costs and benefits. In the present paper, we intend to address two research statements.

RS1: The response time drops to 4–5 minutes by using drones in the EMS

Many prototypes that have already been tested have proven that drone use is an attainable goal, and no technological issues have emerged in their use. However, the performance of a prototype is one thing; integrating a fleet of drones in a real service is another matter, and it could affect the actual response time in many ways (availability of drones, effectiveness of the intervention, etc.). Accordingly, to give a complete answer to this question, we redesigned the entire EMS model by including drones and have addressed the issues coming from doing so, such as which kind of service can be provided from drones, in which case, the technical requirement for drones, and so forth. Addressed in such a way, RS1 also leads to the next research statement:

RS2: An EMS service including drones is economically feasible        

An existing EMS could achieve a response time of 4–5 minutes just by using traditional emergency vehicles. The problem is that it would absorb so many resources to the point of making the service unfeasible and inefficient. So there must be a stronger reason to use drones in an EMS than simply because it is possible to do so. Indeed, here, drones can ensure better service in a viable way. Accordingly, our purpose is to evaluate the use of drones from an economical point of view, which is possible only after having integrated drones in an EMS to evaluate all the economic impacts of their use.

The present paper is organized as follows: Section 2 summarizes the most recent and relevant scientific contributions on using drones in EMSs. Section 3 is for developing specifications and then designing the new drones-supported EMS. Section 4 proposes a real application. Section 5 discusses the results from designing and applying the service. Finally, Section 6 presents our conclusions.

          The main limitation of the present study is that we ran only one test for the model. However, given the extension of Avellino City and the characteristics of the territory, which are pretty rough, along with the many cases of heart disease per year, there is no reason to doubt comparable results, even in other cities.

        Despite the obvious advantages, there are, however, factors limiting the use of drones as first aid tool, that is, the legal aspects and the readiness of the rescuer. The drone carries a first aid kit that must be used by the person who finds the patient in a critical state. He or she must have a tough attitude and must not be seized by emotions to properly follow the instructions indicated by the doctor. This, though, is an unpredictable factor.

        Finally, we have addressed only the technical and economic feasibility in the current paper. According to the most recent literature on the cost/benefit analysis (Basten et al., 2019) and urban transportation (Nenni et al., 2019), it might be wise to assess the social and environmental feasibility of the service in future works.

        The authors would like to thank Antonella D’Aquino for her help in implementing part of the model during her master’s thesis in management engineering at the University of Salerno.

Basten, V., Crévits, I., Latief, Y., Berawi, M.A., 2019. Conceptual Development of Cost Benefit Analysis based on Regional, Knowledge, and Economic Aspects of Green Building. International Journal of Technology, Volume 10(1), pp. 81–93

Dhivya, A.J.A., Premkumar, J., 2017. Quadcopter-based Technology for an Emergency Healthcare. In: 3^rd International Conference on Biosignals, Images and Instrumentation (ICBSII)

Dulebenets, M.A., Pasha, J., Abioye, O. F., Kavoosi, M., Ozguven, E.E., Moses, R., Boot, W.R., Sando, T., 2019. Exact and Heuristic Solution Algorithms for Efficient Emergency Evacuation in Areas with Vulnerable Populations. International Journal of Disaster Risk Reduction, Volume 39, p. 101114

Fitch, J., 2005. Response Times: Myths, Measurement & Management. JEMS: A Journal of Emergency Medical Services, Volume 30(9), pp. 47–56

Hugeng, H., Kurniawan, R., 2016. Development of the ‘Healthcor’ System as a Cardiac Disorders Symptoms Detector using an Expert System based on Arduino Uno. International Journal of Technology, Volume 7(1), pp. 78–87

Kartawijaya, T., Townsend, E., Tully, K., Isihara, P., Diedrichs, D. R., Flores, G., Ward, J., 2019. Is now the Time to Invest in Emergency Smart-navigated Multiple-response Quadcopter Fleets? Journal of Unmanned Vehicle Systems, Volume 7(2), pp. 145–155

Kim, S.J., Lim, G.J., Cho, J., Côté, M.J., 2017. Drone-aided Healthcare Services for Patients with Chronic Diseases in Rural Areas. Journal of Intelligent & Robotic Systems, Volume 88(1), pp. 163–180

Krishna, V.V., Shastri, S., Kulshrestha, S., Mariajossy, M.A., 2018. Design of Drone Ambulance. International Journal of Pure and Applied Mathematics, Volume 119(15), pp. 1813–1818

Laksham, K.B., 2019. Unmanned Aerial Vehicle (Drones) in Public Health: A SWOT Analysis. Journal of Family Medicine and Primary Care, Volume 8(2), pp. 342–346

McLay, L.A., 2010. Emergency Medical Service Systems that Improve Patient Survivability. Wiley Encyclopedia of Operations Research and Management Science. Available Online at https://onlinelibrary.wiley.com/doi/abs/10.1002/9780470400531.eorms0296

Marris, E., 2013. Drones in Science: Fly, and Bring me Data. Nature, Volume 498(7453), pp. 156–158

Moore, L., 1999. Measuring Quality and Effectiveness of Prehospital EMS. Prehospital Emergency Care, Volume 3(4), pp. 325–331

Nenni, M.E., Sforza, A., Sterle, C., 2019. Sustainability-based Review of Urban Freight Models. Soft Computing, Volume 23(9), pp. 2899–2909

Pons, P.T., Haukoos, J.S., Bludworth, W., Cribley, T., Pons, K.A., Markovchick, V.J., 2005. Paramedic Response Time: Does it Affect Patient Survival? Academic Emergency Medicine, Volume 12(7), pp. 594–600

Quanto costa un’Ambulanza del SSN e relativo Equipaggio di soccorso (How much an ambulance costs). Available Online at: http://www.coesitalia.eu/blog/delucidazioni-costi/, Accessed on 12 February 2020

Regulatory Article (RA) 1600: remotely piloted air systems (RPAS). Available Online at: https://www.gov.uk/government/publications/regulatory-article-ra-1600-remotely-piloted-air-systems-rpas, Accessed on 12 February 2020

Roca-Riu, M., Menendez, M., 2019. Logistic Deliveries with Drones: State of the art of Practice and Research. In: 19^th Swiss Transport Research Conference (STRC 2019). STRC, pp. 1–14

Scott, J., Scott, C., 2017. Drone Delivery Models for Healthcare. In: Proceedings of the 50^th Hawaii International Conference on System Sciences

Sutresman, O., Syam, R., Asmal, S., 2017. Controlling Unmanned Surface Vehicle Rocket using GPS Tracking Method. International Journal of Technology, Volume 8(4), pp. 709–718

Tatsidou, E., Tsiamis, C., Karamagioli, E., Boudouris, G., Pikoulis, A., Kakalou, E., Pikoulis, E., 2019. Reflecting upon the Humanitarian Use of Unmanned Aerial Vehicles (Drones). Swiss Medical Weekly, Volume 149(1314), pp. 1–6

Troudi, A., Addouche, S.A., Dellagi, S., Mhamedi, A.E., 2018. Sizing of the Drone Delivery Fleet Considering Energy Autonomy. Sustainability, Volume 10(9), pp. 1–17

Yeong, S.P., King, L.M., Dol, S.S., 2015. A Review on Marine Search and Rescue Operations using Unmanned Aerial Vehicles. International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, Volume 9(2), pp. 396–399

Zègre-Hemsey, J.K., Bogle, B., Cunningham, C.J., Snyder, K., Rosamond, W., 2018. Delivery of Automated External Defibrillators (AED) by Drones: Implications for Emergency Cardiac Care. Current Cardiovascular Risk Reports, Volume 12(25), pp. 1–9