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.

What is this project about? This project aims to increase the number of people who survive after an "out of hospital cardiac arrest" by helping make sure that people who are trained in resuscitation feel able and confident enough to help. We will do this using text-messages to deliver behaviour change techniques. Why is it important? Every year in the United Kingdom, over 60,000 people have an "out of hospital cardiac arrest". A cardiac arrest is when someone's heart stops suddenly. When this happens outside a hospital e.g. at home or in a car park, this is known as an "out of hospital cardiac arrest" (OHCA). It results in death if the person is not resuscitated immediately. Currently, only one in ten people (10%) survive an out of hospital cardiac arrest in the UK. However, some areas in Europe have achieved higher rates of survival (up to a quarter, or 25% survive) which we'd like to match. What is currently being done about this? The areas in Europe that have achieved the highest rates of survival have achieved high rates of cardiopulmonary resuscitation (CPR) (giving mouth-to-mouth resuscitation and compressing the person's chest) by members of the public. If CPR can be started immediately, people are 4 times more likely to survive. In the UK, we are training as many members of the public as possible to perform CPR but this alone is not quite enough because only a small proportion of the people trained in CPR actually attempt it when they encounter someone in cardiac arrest. The reasons people give for not attempting CPR include things like lack of confidence in their CPR skills, being uncertain whether the person is actually having a cardiac arrest and being worried about doing harm. How will this study help? We can improve people's confidence by using something called "behaviour change techniques". They have been shown to be successful in helping people to stop smoking, lose weight or take more exercise, etc. and we think they can help with the behaviour of doing CPR. Delivering the behaviour change techniques using text-messages will allow us to stay in touch with people for a long time after their initial CPR training and to use videos, images etc. to make our messages appealing and more effective. We hope this approach will help people trained in CPR to remain confident, competent and ready to start CPR if required. What's involved in this study? We are a team with expertise in CPR training, resuscitation and behaviour change techniques. We will work with members of the public (some will have been trained in CPR), experts in CPR training, creative professionals (e.g graphic designers) to develop a text-message based programme for people to receive as part of their CPR training. The research will help us work out how to make the messages useful to those who receive them and to work out what should be included (e.g. simple text messages, aminations or short videos); when and how often we should contact people; how interactive the messages should be, etc. Once we have developed this programme of support, we will test it in a small number of people (20), so we can improve it, based on what they tell us. What happens after the study? We will apply for other funding, so that we can test our programme of support in a very large number of people. This will show how effective it is in helping people trained in CPR to be confident, competent and ready to start CPR if someone has an out of hospital cardiac arrest.