Risk Management and Healthcare Policy (Apr 2021)
Optimization for Locating Emergency Medical Service Facilities: A Case Study for Health Planning from China
Abstract
Yufan Deng,1,2 Yumeng Zhang,1,2 Jay Pan1,2 1HEOA Group, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, People’s Republic of China; 2Institute for Healthy Cities and West China Research Center for Rural Health Development, Sichuan University, Chengdu, Sichuan, People’s Republic of ChinaCorrespondence: Jay PanWest China School of Public Health, Sichuan University, No. 17, Section 3, Ren Min Nan Road, Chengdu, 610041, Sichuan, People’s Republic of ChinaEmail [email protected]: Rational location of emergency medical service (EMS) facilities could improve access to EMS, and thus assist in saving patients’ lives and improving their health outcomes. A considerable amount of spatial optimization research has been devoted to the development of models to support location planning in the context of EMS, with extensive applications in policy making around the world. However, in China, studies on the location of EMS facilities have not been paid enough attention to, let alone their practical applications. This paper conducted location optimization for EMS facilities in Chengdu, one of the biggest cities in southwest China with more than 16.5 million population, aiming to optimize the EMS system by adding (upgrading) a minimum number of EMS facilities to achieve a given population coverage.Methods: Location optimization was conducted according to regional health policy goal for the EMS system in Chengdu, China, 2017. The nearest-neighbor approach was used to calculate the shortest travel time based on geographical information system (GIS). The location set covering model was used to formulate the optimization problem under China’s context, and genetic algorithm (GA) was employed to determine the optimized locations.Results: The results showed that a minimum number of 55 new facilities were required to upgrade to EMS facilities to achieve the policy goal of 90% population coverage of EMS within 15 minutes. Access to EMS also improved substantially in terms of shortest travel time after facility upgrading. The weighted median shortest travel time to EMS facilities in Chengdu decreased by 14.57%, from 6.45 minutes to 5.51 minutes.Conclusion: Our study showed that the solution could effectively achieve the policy goal of population coverage with a minimum number of new EMS facilities. Our findings would support evidence-based decision-making in future EMS planning in China.Keywords: emergency medical care, EMS location, genetic algorithm, location set covering problem
