The Application of Information Systems to Improve Ambulance Response Times in the UK

IF 1.1 Q2 SOCIAL SCIENCES, INTERDISCIPLINARY Electronic Journal of Information Systems in Developing Countries Pub Date : 2023-09-21 DOI:10.30564/jeis.v5i2.5881
Alan Slater
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Abstract

Emergency ambulance services in the UK are tasked with providing pre-hospital patient care and clinical services with a target response time between call connect to on-scene attendance. In 2017, NHS England introduced four new response time categories based on patient needs. The most challenging is to be on-scene for a life-threatening situation within seven minutes of the call being connected when such calls are random in terms of time and place throughout a large territory. Recent evidence indicates emergency ambulance services regularly fall short of achieving the target ambulance response times set by the National Health Service (NHS). To achieve these targets, they need to undertake transformational change and apply statistical, operations research and artificial intelligence techniques in the form of five separate modules covering demand forecasting, plus locate, allocate, dispatch, monitoring and re-deployment of resources. These modules should be linked in real-time employing a data warehouse to minimise computational data and generate accurate, meaningful and timely decisions ensuring patients receive an appropriate and timely response. A simulation covering a limited geographical area, time and operational data concluded that this form of integration of the five modules provides accurate and timely data upon which to make decisions that effectively improve ambulance response times.
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信息系统在英国的应用,以提高救护车的反应时间
英国紧急救护车服务的任务是提供院前病人护理和临床服务,并在呼叫连接到现场出勤之间设定目标响应时间。2017年,英国国家医疗服务体系根据患者需求引入了四种新的响应时间类别。最具挑战性的是,在接到威胁生命的电话后7分钟内赶到现场,而这些电话在一个大范围内的时间和地点都是随机的。最近的证据表明,紧急救护车服务经常达不到国家卫生服务体系(NHS)设定的救护车响应时间目标。为了实现这些目标,他们需要进行转型变革,并以五个独立模块的形式应用统计、运筹学和人工智能技术,包括需求预测,以及资源的定位、分配、调度、监测和重新部署。这些模块应该通过数据仓库实时连接起来,以最大限度地减少计算数据,并产生准确、有意义和及时的决策,确保患者得到适当和及时的响应。一项涵盖有限地理区域、时间和操作数据的模拟得出的结论是,这种将五个模块整合在一起的形式提供了准确和及时的数据,可以根据这些数据做出决策,有效地缩短救护车的响应时间。
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来源期刊
CiteScore
3.60
自引率
15.40%
发文量
51
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