Source · Select Committees · Public Accounts Committee
Recommendation 16
16
Accepted
NHS Resolution exploring AI to analyse negligence claims data for insights.
Recommendation
Some clinical negligence firms are reportedly using artificial intelligence to triage claims more efficiently and effectively. NHS Resolution holds almost 30 years of experience and data concerning compensation claims.33 NHS Resolution told us it is starting to explore how technology can mine its database to learn more about how claims are made up in terms of damages and the underlying causes of claims.34 It also explained how it has been working with the Getting It Right First Time programme, which is part of NHS England, to bring claims data together with other NHS metrics to better understand what causes claims in different clinical specialties.35 28 C&AG’s Report, para 3.24 29 Q 83 30 C&AG’s Report, para 3.27 31 Society of Clinical Injury Lawyers (CCN0005); Switalskis Solicitors (CCN0014) 32 Q 81 33 C&AG’s Report, paras 3.25, 3.28 34 Q 36 35 Q 53 13 2 Putting the costs of clinical negligence on a more sustainable path Problems with maternity care in England
Government Response Summary
NHS England is developing and evaluating AI models on Learn from Patient Safety Events (LFPSE) data to identify discrepancies and emerging themes and is assessing the feasibility of enabling secure, real-time analytics via the Federated Data Platform (FDP) to underpin a scalable national infrastructure for AI assisted safety surveillance.
Government Response
Accepted
HM Government
Accepted
3. PAC conclusion: We are concerned there is far too little data on the factors behind clinical negligence, given its huge impact on people’s lives and NHS finances. 3b. PAC recommendation: The Department, NHS England and NHS Resolution should explore the use of artificial intelligence to analyse live data, detect discrepancies and outliers quickly, and improve the speed of early warning systems. 3.6 The government agrees with the Committee’s recommendation. Recommendation implemented 3.7 This work is underway. NHS England is actively developing and evaluating AI models on Learn from Patient Safety Events (LFPSE) data, including topical analysis and novelty detection approaches, to identify discrepancies, emerging themes and unusual risk patterns earlier. This work will directly support a more responsive early warning capability across the NHS by enabling faster detection of outliers and strengthening human led safety assessment. 3.8 NHS England is also undertaking work to assess the feasibility of enabling secure, real-time analytics via the Federated Data Platform (FDP) to underpin a scalable national infrastructure for AI assisted safety surveillance. This includes exploring market solutions capable of analysing largescale free text and incident data, with the aim of improving the speed and consistency of early warning systems and reducing manual burden. 3.9 NHSR is also looking at how AI can be used to analyse data to learn more about the underlying causes of claims. NHSR recognises that sharing its data with NHS England provides a full picture of potential harm.