Alan Slater
University of Huddersfield, Huddersfield, HD13DH, UK
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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