Data Awareness for Sending Help (DASH)

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This project explores integration of new and emerging data sources for potential impact on emergency response. In an emergency medical situation, ambulances must get to those in need as quickly as possible in order to provide care and, ultimately, to save lives. Decisions about which ambulance should respond to each incident need to be made rapidly. However, making such decisions is complicated: incidents can occur simultaneously or in short succession over a wide area; the locations of ambulances are constantly changing; and there are many environmental factors that can affect response times, such as traffic and weather conditions. In such a complex and dynamic environment, a form of automated decision support, known as computer assisted dispatch (CAD), is often installed to help staff make these decisions.In recent years, there has been a sharp expansion in the volume and types of data sources that might potentially be linked to CAD, presenting new possibilities for improving current decision-support systems. Doing so could enable ambulances to respond faster as emergency situations develop, improving emergency care, lowering costs, increasing efficiency and improving health outcomes for patients. Potentially useful data might come from any of the following sources: the general population (via social media and other mobile Apps); specific user segments (via in-home/wearable sensors, particularly for high-risk patient groups); urban infrastructure (via public transport monitors, embedded road sensors or weather stations); and other public sector actors (such as collaborating emergency response agencies or healthcare providers).This proposed Policy Demonstrator Project, entitled "Data Awareness for Sending Help" (DASH), aims to explore the potential of these "new data" sources for improving ambulance response times. The project builds on a new research collaboration between King's College London (KCL) and the London Ambulance Service (LAS), which is evaluating novel methods for ambulance dispatch by simulating ambulance call-outs based on historical LAS system logs. DASH will lay the groundwork for extending this preliminary study in important new directions, by predicting changes in response times due to integration of additional data sources.There are a number of challenges that DASH aims to address, both technical, in terms of how feasible it is to access and use new data sources reliably, and social or ethical, in terms of how acceptable and appropriate it is to use data in this way, particularly data about individuals. DASH asks three specific research questions:(1) What are the benefits and risks for emergency service agencies, healthcare providers and the public related to linking new and emerging data sources to emergency response? A comprehensive literature review and targeted focus groups will highlight which new data sources could be tapped and will weigh benefits, costs and risks associated with data-enhanced emergency response, across various sectors.(2) What are the technical challenges involved in linking new and emerging data sources to CAD technologies to provide the most important benefits? A technical investigation will consider practical aspects of linking new data sources to CAD and will explore innovative modelling methods that could be applied in a low-cost but high-impact and secure manner.(3) How can practitioners and policymakers learn from our study of linking new and emerging data sources to the London Ambulance Service? DASH will produce a set of outputs designed to inform practitioners and policymakers, including: a Policy Brief outlining our findings; a Case Study that assesses linking new data and associated methodologies to LAS's CAD system; a Software Prototype, built on previous work, that demonstrates how a data-enhanced CAD system might work; and a report on the broader applicability of the findings to other emergency response agencies in the UK.