Modelling Emergency Medical Services in Indonesia

最近发生的喀拉喀托火山喷发和海啸、龙目岛和帕卢岛地震和海啸等事件再次引起了世界对印度尼西亚的关注。据估计，仅2018年一年，自然灾害就造成全国4000多人死亡，约300万人流离失所。全国各地普遍经历自然灾害、人为冲突和暴力，同时对紧急服务的需求也在不断增加。印度尼西亚全国缺乏紧急医疗运输是2.64亿人口获得和获得医疗保健的主要障碍。直到最近，印度尼西亚还没有公共救护车服务，但现在正朝着建立一个综合的国家应急系统迈进。然而，面临着许多挑战，包括广阔的地理区域、严重的交通拥堵和救护车数量不足。因此，高效和有效地利用这些宝贵的资源是至关重要的，将是我们研究的重点，帮助增强地方服务的能力，并为社会上最贫穷的人提供服务，他们负担不起私人运营的救护车。全国118紧急救护服务由印度尼西亚外科医生协会于2005年建立，是该国唯一的公共救护服务，目前在五个城市开展业务：雅加达、巨港、日惹、泗水和Makasser。虽然有一些私人救护车提供者，但价格非常昂贵，因此大多数印度尼西亚人无法负担得起，因此无法及时获得医疗服务，发病率和死亡率都很高。交通事故占该国创伤死亡人数的70%，所有死亡人数中近三分之一是由中风和缺血性心脏病/心脏病发作造成的，在这些疾病中，迅速获得适当治疗绝对至关重要。仅在雅加达，就有118辆救护车服务于潜在的1100万人口（世界上人口最多的第15个城市，也是东南亚最大的城市），占地661平方公里，但值得注意的是，目前只有15辆救护车分布在全市的5个地点。毫不奇怪，由于缺乏车辆，道路上的交通流量（据报道雅加达的交通拥堵是亚洲主要城市中最严重的），以及要覆盖的面积很大，反应时间和结果都非常糟糕。我们的研究计划将使用运筹学（OR）方法对紧急医疗服务（EMS）进行数学建模。我们将直接与印度尼西亚国家118救护车服务的创始人兼主任、医学博士Aryono Pusponegoro教授及其EMS团队合作，帮助预测紧急需求，并就车辆的最佳类型、能力和地理位置做出关键决策，以实现可持续的紧急服务。这些因素直接影响患者的生存概率、对重大灾害的反应能力以及所提供的护理的整体质量。我们雄心勃勃、富有冒险精神和变革性的研究项目以新颖的科学方法为基础，这些方法将：(i)开发一种方法，利用机器学习框架内的人口普查和调查数据，在没有历史呼叫数据的情况下预测时空需求；（ii）开发一种新方法，利用异构车队，考虑不同行驶时间/速度的多种车辆类型，考虑EMS的能力和分配，以最大限度地提高异质人口（多种医疗需求）中的生存。这将通过建立一个优化和模拟决策支持框架来实现，该框架将由国家118紧急救护车服务部门部署。

Recent events such as the Anak Krakatau eruption and tsunami, and the Lombok and Palu earthquakes and tsunami have once again brought the attention of the world on Indonesia. In 2018 alone, it is estimated that natural disasters claimed the lives of more than 4,000 people and displaced around 3 million people across the country. Natural disasters, manmade conflicts and violence are commonly experienced across the country, alongside a rising demand for emergency services. The lack of emergency medical transportation across Indonesia is a main barrier to the access and availability of medical care for the 264 million population. Until recently, Indonesia had no public ambulance service but is now moving towards an integrated national emergency response system. There are however many challenges faced including vast geographical areas, severe traffic congestion, and inadequate numbers of ambulances. Making efficient and effective use of such valuable resources is therefore critical and will be the focus of our research, as is helping to empower local services and provide for the poorest in society who cannot afford to take privately run ambulances. The National 118 Emergency Ambulance Service was established in 2005 by the Indonesian Surgeons' Association, and is the only public ambulance service in the country, operating currently in five cities: Jakarta, Palembang, Yogyakarta, Surabaya and Makasser. Whilst some private ambulance providers are available, these are hugely expensive and thus out of reach of the majority of the Indonesian population who consequently suffer from a lack of timely access to medical care and are at high risk of morbidity and mortality. Traffic accidents account for 70% of the country's trauma deaths, and nearly 1 in 3 of all deaths are caused by strokes and ischemic heart disease/heart attacks, where rapid access to appropriate treatment is absolutely vital. In Jakarta alone, 118 serves a potential population of 11 million people (the 15th most populous city in the World and the largest in Southeast Asia), covering an area of 661 square km, but remarkably with only 15 ambulances currently available based at 5 locations across the city. Not surprisingly response times and outcomes are very poor, because of the lack of vehicles, the sheer volume of traffic on the roads (Jakarta is reported to have the worst traffic jams of any major Asian city), and the large area to cover. Our research programme will use Operational Research (OR) methods for mathematical modelling of emergency medical services (EMS). We will work directly with the Founder and Director of the Indonesian National 118 Ambulance Service, Professor Aryono Pusponegoro MD, PhD, and his EMS team to help forecast emergency demand and make critical decisions on the optimal types, capacities and geographical locations of vehicles for a sustainable emergency service. Such factors directly impact on the probability of patient survival, ability to respond to major disasters, and the overall quality of care provided. Our ambitious, adventurous and transformative research programme is underpinned by novel scientific methods that will: (i) develop a methodology to forecast spatiotemporal demand without historical call data using census and survey data within a machine-learning framework, and (ii) develop a novel approach to consider EMS capacities and allocations for maximising survival within a heterogeneous population (multiple medical needs) using a heterogeneous fleet with consideration of multiple vehicle types each with differing travel times/speeds. This will be achieved via the creation of an optimisation and simulation decision-support framework that will be deployed by the national 118 emergency ambulance service.