PP12 New indicators for measuring patient survival following ambulance service care. Issue 10 (28th September 2017) - Coster, Joanne; Jacques, Richard; Turner, Janette; Crum, Annabel; Nicholl, Jon; Niroshan Siriwardena, A
Abstract : Background: Developing better measures of quality and performance are key priorities for ambulance services. Using multi-stage, multi-stakeholder consensus studies, we developed eight new indicators of ambulance service quality and performance. We present here a new indicator to measure rates of survival to 7 days post incident in people with a serious emergency condition. Methods: We linked six months patient level Computer-Aided Dispatch (CAD) and electronic Patient Report Form (ePRF) data from one ambulance service with Hospital Episode Statistics (HES A and E/admitted patient data), and national mortality (ONS) information We identified a cohort of people with one of 16 serious emergency conditions, defined as conditions 'where death could potentially be prevented by a good emergency system'. We created age and condition adjusted models to calculate two survival measures. 1) Survival to hospital admission, within 7 days of the ambulance call; 2) For patients admitted to hospital, survival to 7 days from admission. Results: 11 264 patients met the inclusion criteria. 1) 10 647 survived to admission and 617 (5.5%) died pre-admission. Most pre-admission deaths were in older people (42%>80 years) and occurred within 1 day of the ambulance call (87.8%). People with ruptured aortic aneurysm and asphyxiation were more likely to die pre-admission than those with other conditions. 2) Of the 10 647 patients admitted to hospital, 94% survived. Survival rates decreased with age: 100% of children under 11 years compared whereas 84% of those aged over 90 years survived to 7 days. People with cardiac arrest, septic shock and ruptured aortic aneurysm were also more likely to die in hospital. Conclusions: These two indicators relate to people with a serious emergency condition who survive. Further work is being done to develop predictive models that can be used to assess trends over time. For example, survival may improve through taking the right patients to the right specialist care.