The longer life expectancy now enjoyed by most people with a learning disability is to be celebrated, for example people with Down's syndrome may now live to their 60s, a big step forward from the 1980s when life expectant was still in the teens. However, people with a learning disability generally are also at increased risk of dementia at a younger age and there are occasions when it is no longer preferred or practical for a person with a learning disability to remain in their home as dementia progresses. In such situations a move to a care home may take place, often at time of crisis or as dementia advances. Two longstanding problems have become evident: 1) people with a learning disability and dementia are often moved to a care home without being supported to share information about themselves or their wishes for future accommodation needs or preferences and 2) staff in care homes for older people can be hesitant about people with a learning disability and dementia moving in. This is due to uncertainty about how to meet their needs and the perception that the support needed is too great, or too different from other residents. This Catalyst proposal increases potential for appropriate engagement and activities before, during and after a move to a care home by older people with a learning disability and dementia. There two issues will be addressed in this project in the following ways: 1. To develop, pilot and refine a new resource for people with a learning disability and dementia. Consistent with previous work of the Principal Investigator, this is expected to offer flexibility whilst ensuring the voice of the person is heard. It will be tailored to outcomes that individuals want from their accommodation and their support, and will help care home staff to better understand the needs and preferences of the person, and hopes for their new home. We will consult with people with a learning disability initially on appropriate format and content and will co-produce the final resource. This may be paper-based, electronic, audio or something different, but will be in a format considered appropriate and useable by people with a learning disability. The challenge will be determining an appropriate format and ensuring usability, both in the current place called home and in a care home, recognising limited WIFI accessible to some people with a learning disability. 2. To develop and pilot licensed training or an information resource/podcast series or similar for care home staff. Within the parameters of funding available, this will focus on transition to a care home by people with a learning disability and dementia. This requires consultation with care home staff initially to understand more about their knowledge gaps and will identify the most appropriate format that can be accessed and used in practice by the highest number of care home staff across the UK. A challenge will be developing something that is both feasible and sustainable. It will need to be accessed by care home providers at low cost, recognising sector challenges in releasing staff to take part in training or education. A further challenge is to consider how we will know if the training/learning has been successful beyond 'in the moment' feedback when completed.

The current AI paradigm at best reveals correlations between model input and output variables. This falls short of addressing health and healthcare challenges where knowing the causal relationship between interventions and outcomes is necessary and desirable. In addition, biases and vulnerability in AI systems arise, as models may pick up unwanted, spurious correlations from historic data, resulting in the widening of already existing health inequalities. Causal AI is the key to unlock robust, responsible and trustworthy AI and transform challenging tasks such as early prediction, diagnosis and prevention of disease. The Causality in Healthcare AI with Real Data (CHAI) Hub will bring together academia, industry, healthcare, and policy stakeholders to co-create the next-generation of world-leading artificial intelligence solutions that can predict outcomes of interventions and help choose personalised treatments, thus transforming health and healthcare. The CHAI Hub will develop novel methods to identify and account for causal relationships in complex data. The Hub will be built by the community for the community, amassing experts and stakeholders from across the UK to 1) push the boundaries of AI innovation; 2) develop cutting-edge solutions that drive desperately needed efficiency in resource-constrained healthcare systems; and 3) cement the UK's standing as a next-gen AI superpower. The data complexity in heterogeneous and distributed environments such as healthcare exacerbates the risks of bias and vulnerability and introduces additional challenges that must be addressed. Modern clinical investigations need to mix structured and unstructured data sources (e.g. patient health records, and medical imaging exams) which current AI cannot integrate effectively. These gaps in current AI technology must be addressed in order to develop algorithms that can help to better understand disease mechanisms, predict outcomes and estimate the effects of treatments. This is important if we want to ensure the safe and responsible use of AI in personalised decision making. Causal AI has the potential to unearth novel insights from observational data, formalise treatment effects, assess outcome likelihood, and estimate 'what-if' scenarios. Incorporating causal principles is critical for delivering on the National AI Strategy to ensure that AI is technically and clinically safe, transparent, fair and explainable. The CHAI Hub will be formed by a founding consortium of powerhouses in AI, healthcare, and data science throughout the UK in a hub-spoke model with geographic reach and diversity. The hub will be based in Edinburgh's Bayes Centre (leveraging world-class expertise in AI, data-driven innovation in health applications, a robust health data ecosystem, entrepreneurship, and translation). Regional spokes will be in Manchester (expertise in both methods and translation of AI through the Institute for Data Science and AI, and Pankhurst Institute), London (hosted at KCL, representing also UCL and Imperial, leveraging London's rapidly growing AI ecosystem) and Exeter (leveraging strengths in philosophy of causal inference and ethics of AI). The hub will develop a UK-wide multidisciplinary network for causal AI. Through extended collaborations with industry, policymakers and other stakeholders, we will expand the hub to deliver next-gen causal AI where it is needed most. We will work together to co-create, moving beyond co-ideation and co-design, to co-implementation, and co-evaluation where appropriate to ensure fit-for-purpose solutions Our programme will be flexible, will embed trusted, responsible innovation and environmental sustainability considerations, will ensure that equality diversity and inclusion principles are reflected through all activities, and will ensure that knowledge generated through CHAI will continue to have real-world impact beyond the initial 60 months.

Paracetamol overdose (POD) is common, with approximately 100,000 cases attending hospital emergency departments in the UK each year (same as heart attacks). Of these patients around half (50,000) need urgent treatment yet, despite an effective antidote being available, around 5,000 patients every year still develop drug-induced liver injury (DILI). The prompt administration of the antidote acetylcysteine (NAC) to patients at risk of DILI is crucial to prevent potentially life-threatening liver failure. Treatment efficacy decreases substantially as the delay between POD and starting NAC increases. NAC is almost 100% effective if started within 8 hours of overdose but offers only modest benefits if started after around 20 hours. There are no specific symptoms or clinical signs of early liver damage therefore healthcare workers rely on blood tests measured in a central hospital laboratory to pick up liver injury after POD. At hospital presentation, prior to blood results being available, the current method for early risk stratification after POD uses the patient-reported time of overdose to estimate the delay between POD and NAC. This approach is inherently subjective, cannot be used in staggered overdoses and accidental therapeutic excess ingestions (around one third of cases) and has sub-optimal accuracy even when applicable. There is a pressing need for a rapid, cost-effective, point-of-care assay to identify high-risk patients at hospital presentation with sufficient sensitivity and specificity for targeted early treatment. The standard serum biomarker for DILI diagnosis, alanine aminotransferase (ALT), increases too slowly post-POD to accurately diagnose DILI within the NAC optimal therapeutic window. Multiple studies have repeatedly demonstrated that a circulating biomarker called cytokeratin-18 (K18) can be used to accurately detect DILI within 8 hours of overdose (the optimal time window for effective NAC treatment). Using DPFS funding, our multi-disciplinary team have developed a point-of-care (POC) assay called the POC-DILI Diagnostic, which combines a K18 Lateral Flow Assay (K18 LFA) with a bespoke Handheld Raman Reader (HRR). The K18 LFA uses antibodies and gold nanoparticles (AuNP) to create a sandwich assay with K18. The AuNPs are functionalised with a Raman reporter to produce a quantitative Surface Enhanced Raman Scattering (SERS) assay, read out by the HRR from the K18 LFA strip. In banked samples, the POC-DILI Diagnostic has achieved its target product profile, with high sensitivity and specificity in identifying DILI and time-to-result within 20 minutes (milestone 1 achieved). A prospective performance evaluation study has now commenced (POC-DILI study).