The DASH project explores the potential impact of integrating new and emerging data sources on emergency response and wider policy.
Ambulances aim to get to emergency medical situations quickly to provide care and save lives. Complicated decisions need to be made rapidly about which ambulance should respond to each incident, under dynamic conditions of uncertainty: various incidents can occur at any time anywhere over a wide area; the locations of ambulances are constantly changing; and a range of factors like traffic and weather conditions can affect response times. Ambulance services use automated decision support known as computer-assisted dispatch systems (CAD) to help staff make these decisions.
The volume and types of data that might be linked with CAD have expanded sharply over the last few years. These might support ambulances in their efforts to respond faster and improve health outcomes for patients. Potentially useful data might come from: 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 or embedded road sensors); or other public sector actors (such as collaborating emergency response agencies or healthcare providers).
This is an exciting subject through which to explore the impact of big data on public policy more generally. Ambulance services are highly responsive to public demand, putting them at the forefront of efforts to explore the opportunities and limits of effcient data-driven demand management. They operate under high pressure in real time, meaning that the velocity as well as the volume aspects of technological developments are relevant. These challenges are raising important questions, including issues of public value, resourcing and privacy, which have yet to be fully considered in public debates.
DASH is a Policy Demonstrator Project building on the existing research collaboration between King’s College London (KCL) and the London Ambulance Service (LAS), which is evaluating innovative methods for ambulance demand modelling. DASH addresses 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?
DASH aims to highlight new data sources that could be tapped and to weigh benefits, costs and risks associated with data-enhanced emergency response through a comprehensive literature review and targeted surveys and focus groups.
2 | What are the technical challenges involved in linking new and emerging data sources to cad technologies to provide the most important benefits?
DASH aims to consider practical aspects of linking new data sources to cad and 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 aims to inform practitioners and policy makers using: a policy brief outlining the project’s findings; a case study that assesses linking new data and associated methodologies to LAS systems; a software prototype demonstrating how a data-enhanced cad system might work; and a report on the applicability of the findings to other emergency response agencies in the UK.
DASH is funded by the Economic & Social Research Council (ESRC) grant ref. ES/P011160/1 (April 2017 – March 2018).