Improving Efficiency and Equity of Ambulance Services through Advanced Demand Modelling

近年来，英格兰对救护车服务的需求急剧上升，预计未来几年的压力将越来越大。不断增长的需求与有限的救护车资源之间的差距使维持高质量服务成为一个主要挑战。2017年，NHS英格兰进行了一项名为救护车响应计划（阿普）的重大国家改革，旨在解决效率和性能问题。它注意到过度使用立即派遣决定和不适当地分配资源给事件。有关的关键问题：护理质量;成本效益;以及各地区和各人口群体之间平等提供服务。鉴于这种情况，从2017年到2018年，King的研究人员与伦敦救护车服务（LAS）合作开展了一个ESRC资助的项目- DASH，探索大数据如何改善救护车响应的决策。最终报告提出了六个新的数据倡议，其中三个与需求预测密切相关。鉴于NHS不断增长的压力，以及救护车服务了解其服务人群需求的必要性，拟议的博士项目旨在以LAS为案例研究开发一个先进的救护车服务需求预测模型。该研究旨在找出最相关的社会经济、环境和时空因素，并将这些因素建模为救护车需求的预测因子。博士课程的最后一部分将发展需求管理创新模型的含义，以供未来测试。

Demand for Ambulance Services in England has risendramatically over recent years, with growing pressureanticipated for future years. The disparity between theincreasing demand and limited ambulance resources makesthe major challenge for maintaining a high-quality service. In2017, NHS England undertook a significant national reformcalled the Ambulance Response Programme (ARP), designedto address efficiency and performance issues. It noted theover-use of immediate dispatch decisions and the insufficientallocation of resources to incidents. Key issues concerned:the quality of care; its cost-effectiveness; and the equality ofprovision across areas and population groups. Given suchsituation, from 2017 to 2018, King's researchers haveworked with the London Ambulance Service (LAS) on anESRC-funded project - DASH, exploring how big data couldimprove decision-making in ambulance response. The finalreport suggested six new data initiatives, out of which, threeare strongly related to demand prediction.In view of the growing pressures of NHS, and the necessity ofambulance services to understand the needs of thepopulations they serve, the proposed PhD project aims todevelop an advanced demand prediction model forambulance services taking LAS as a case study. Theresearch is to find the most correlated socioeconomic,environmental, and spatiotemporal factors and to modelthese factors as predictors of ambulance demand. The finalcomponent of the PhD will develop the implications of themodel as Demand Management innovations, for futuretesting.