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They have over 4,500 staff, who work across a wide range of roles based in 70 ambulance stations. They serve more than seven million people who live and work in the London area. Their service operates over an area of approximately 620 square miles, from Heathrow in the west to Upminster in the east, and from Enfield in the north to Purley in the south.

What the CSAIF funded: LAS were given a grant of £120,000 to integrate GoodSAM - a smartphone app that alerts registered first-aiders to nearby cardiac arrests - into their 999 systems. £20,000 of this was ringfenced for evaluation. View the full impact evaluation.

About the evaluation

Level on the Standards: Level 2 - they have captured data that shows positive change, but cannot confirm they caused this.

Evaluator: Artifactual

Aim: This evaluation had two main objectives. Firstly to understand the experience of using the app to respond to an alert in order to improve it, and secondly to quantify any impact the integration of the app has had on bystander responding and survival outcomes.

Key findings: The results are mixed but encouraging. The findings from the quantitative data benefiting from comparison data indicate a positive trend for bystander-initiated CPR rate and use of defibrillators, although survival to hospital admission and survival to hospital discharge indicated a negative trend. However for all four outcomes, the evaluation suggests it is too early to draw robust conclusions due to the small number of incidents attended by GoodSAM responders during the evaluation period.

The qualitative and survey data provide process data rather than impact data but yielded useful information towards understanding the respondents’ experience and how the service could be improved.

Methodology:

  • Quantitative monitoring data, post-test only but using suitable comparison data to strengthen its robustness sufficiently for Level 2
  • Interview data, post-test only, focusing on process data (experience of the service and how it could be improved), from 13 out of 32 respondents
  • Survey data, post-test only, used to triangulate interview data (process data), from 7 out of 19 respondents

Why is this a Level 2 evaluation?

While the evaluation does not use pre-post data, it uses suitable comparison data for the monitoring data which can make up for the lack of pre-post data. The findings on the monitoring data are mixed (some positive, some negative) but due to the small sample size no thorough conclusions can be drawn. In combination with the process data collected, it seems the service is sufficiently promising to produce positive impact to satisfy the relevant criteria.

About the Evidence Journey

Progress: LAS and GoodSAM moved from Level 1 to Level 2 on the Nesta Standards of Evidence. This shows that, even in these early stages, GoodSAM has identified some evidence of impact. The next step would be to significantly increase the sample size so that conclusions can accurately be drawn from the quantitative data.

Lessons Learned: With such a small sample size, it has not been possible to draw any conclusions from the quantitative work. This will change in time as more incidents are responder to by GoodSAM first-aiders. Within this initial evaluation, the qualitative data has been invaluable in identifying ways in which the GoodSAM app can be improved. For example, the app now has a function that allows responders to debrief from incidents. This was created in direct response to the finding that responders expected to be contacted after they had been to an incident.

Next Steps: GoodSAM is in the process of being integrated into the 999 systems of two other ambulance trusts, with more likely to follow. The next step is to engage academic partners to conduct a study into the effectiveness of GoodSAM. It will benefit from a much larger sample size due to the increase in volume of incidents going through GoodSAM. This will be completed in 2019. In the meantime, GoodSAM will continue to work with ambulance trusts to analyse usage data and debrief reports on an ongoing basis.