As well as its clinical guidelines and appraisals of new medicines, NICE produces public health guidance. This is designed to deal with various public health problems. For example NICE has produced guidance on tobacco control, alcohol abuse, preventing obesity and promoting physical activity. The guidance is based on a rigorous examination of international evidence and an assessment of the cost effectiveness of the interventions. Until 2013 the public health system in England was led by the NHS. This responsibility transferred to local authorities in April 2013. At the same time NICE acquires responsibility for producing quality standards in social care and public health. The principal audience for the guidance and the quality standards are local authorities. Until now local authorities have had no necessary link nor obligation to comply with NICE guidance. As the public health system changes, it is important that NICE is able to adapt its portfolio. As an organisation whose main currency is scientific evidence, it is proposed to study the process of knowledge transfer from NICE to local authorities in a systematic and scientific way. This research, instigated by NICE, is designed to capture a scientific understanding of the system changes, what will be required to meet the needs of the system and how best to engage with it. The study will investigate how the public health and social care guidance and quality standards produced by NICE will be received and implemented within local government and what systems will be developed to use it. It will study the barriers to and facilitators of information flow and implementation between NICE and local government, and within local government organisations. In consultation with relevant professional associations, it will also seek to identify areas in which effective processes have been set up and good outcomes achieved. Finally, it aims to develop a feasible method for monitoring the implementation of NICE's guidance and quality standards in local authorities. The work will be led by a partnership between NICE, University College London and the Local Government Association and a steering group set up to ensure the input of those at the "coal face" e.g. Chief Executives of Local Authorities, Directors of Public Health. The research will be carried out in three phases: 1.A survey of officers and elected members in local authorities to obtain a detailed description of the current knowledge of public health, the new system, NICE and the evidence based approach to public health. 2.Five case studies in five different councils in England to examine the usage / non usage of NICE guidance and standards. The case studies will be undertaken to provide descriptions of local systems and infrastructures, the processes for planning, strategic working, and interagency activity, partnerships, and intra local authority activity and inter NHS-local authority activity, including the Clinical Commissioning Groups and work with Public Health England. The descriptions will be obtained by reviewing formal local documentation and web-based resources, including the Joint Strategic Needs Assessments, and by a series of interviews with portfolio holders, other councillors and key members of staff, especially, Directors of Public Health, Leisure and Recreation, Planning, Transport, Housing, Children's and Adult Services and members of the Health and Wellbeing Boards. If possible, the operation of Health and Well Being Boards will be observed. 3.A conference convened by LGA to share the initial findings from the survey and the case studies with a broader group of local authority stakeholders to further elaborate the evidence and test its findings. The project will aim to further understanding and closer collaboration between NICE and local government as well as producing information of value to both organisations as the new public health structures and functions are developed in England.

Clinicians, patients and policy makers lack access to accurate, real time information on new treatments for treating cancer. This is because such a large amount of information is continuously generated, and it is too complicated to be manually analysed in a timely fashion. This is sometimes referred to as a health 'infodemic'. Information analysed to create clinical evidence (known as systematic reviews) quickly goes out of date, and national bodies responsible for appraising new treatments such as the National Institute for Clinical Excellence are unable to keep up. It is increasingly hard to detect misinformation published within medical literature, and an increasing number of papers have to be withdrawn after publication. INDICATE is a deep learning tool for the autonomous generation of systematic reports and analysis of both structured and unstructured data from published literature on cancer. It has been developed through a collaboration between Imperial College London and Amazon Web Services, NICE and the British Medical Journal (BMJ). The aim is to develop a methodology for the real time analysis of healthcare infodemics that can be used to autonomously create clinical guidance and identify misinformation. This project will build on previous work to develop AI methodologies that automate how we search for medical literature and it will intelligently support peer reviewers as they appraise and assess the quality of research papers. This work has three main goals: 1. To develop a tool for detecting research fraud. 2. To asses if our AI tools can speed up the creation of NICE guidance. 3. To develop autonomous summary reports of clinical evidence of breast cancer treatment that could be used by medical publishers. The study group will work with clinicians, researchers and NICE to define and prioritise critical questions that require answering and to refine the user interface for the system. Moreover, we will prospectively validate the performance of the system to determine the accuracy and performance of its reporting mechanism. The validated data generated by this study will form the basis of a phase II study that scales the number of cancer types and the trial of the technology in a real world clinical environment.

THE PROBLEM There is strong evidence that the social and economic conditions in which we grow, live, work and age determine our health to a much larger degree than lifestyle choices. These social determinants of health, such as income, good quality homes, education or work, are not distributed equally in society, which leads to health inequalities. However, we know very little about how specific policies influence the social conditions to prevent ill health and reduce health inequalities. Also, most social determinants of health are the responsibility of policy sectors other than "health", which means policymakers need to promote health in ALL their policies if they are to have a big impact on health. SIPHER will provide new scientific evidence and methods to support such a shift from "health policy" to "healthy public policy". OUR POLICY FOCUS We will work with three policy partners at local, regional and national level to tackle their above-average chronic disease burden and persistent health inequalities: Sheffield City Council, Greater Manchester Combined Authority and Scottish Government. We will focus on four jointly agreed policy priorities for good health: - Creating a fairer economy - Promoting mental wellbeing - Providing affordable, good quality housing - Preventing long-term effects of difficult childhoods. OUR COMPLEX SYSTEMS SCIENCE APPROACH Each of the above policy areas is a complex political system with many competing priorities, where policy choices in one sector (e.g. housing) can have large unintended effects in others (e.g. poverty). There is often no "correct" solution because compromises between different outcomes require value judgements. This means that to assess the true benefits and costs of a policy in relation to health, policy effects and their interdependencies need to be assessed across a wide range of possible outcomes. However, no policymaker has knowledge of the whole system and future economic and political developments are uncertain. Ongoing monitoring of expected and unexpected effects of policies and other system changes is crucial so failing policies can be revised or dropped. We propose to use complex systems modelling, which has been developed to understand and make projections of what might happen in complex systems given different plausible assumptions about future developments. Our models will be underpinned by the best available data and prior research in each policy area. Our new evidence about likely policy effects across a wide range of outcomes will help policy partners decide between alternative policies, depending on how important different outcomes are to them (e.g. improving health or economic growth). We will develop support tools that can visualise the forecasts, identify policies that achieve the desired balance between competing outcomes and update recommendations when new information emerges. Whilst new to public health policy, these methods are well-established in engineering and climate science. We will 1. Work with policy partners to understand the policy systems and evidence needs 2. Bring together existing data and evidence on each policy system (e.g. links between policies and outcomes, interdependencies between outcomes) 3. Explore citizens' preferences for prioritising when not all outcomes can be achieved 4. Link policies and their health and non-health effects in computer models to analyse benefits and costs over time 5. Build an interactive tool to help policy decision-making, inform advocacy action and support political debate. SIPHER's MAIN OUTCOME We will provide policymakers with a new methodology that allows them to estimate the health-related costs and benefits of policies that are implemented outside the health sector. This will be useful to our partners, and others, who want to assess how scarce public sector resources can be spent to maximise the health and wellbeing benefits from all their activities.

THE PROBLEM There is strong evidence that the social and economic conditions in which we grow, live, work and age determine our health to a much larger degree than lifestyle choices. These social determinants of health, such as income, good quality homes, education or work, are not distributed equally in society, which leads to health inequalities. However, we know very little about how specific policies influence the social conditions to prevent ill health and reduce health inequalities. Also, most social determinants of health are the responsibility of policy sectors other than "health", which means policymakers need to promote health in ALL their policies if they are to have a big impact on health. SIPHER will provide new scientific evidence and methods to support such a shift from "health policy" to "healthy public policy". OUR POLICY FOCUS We will work with three policy partners at local, regional and national level to tackle their above-average chronic disease burden and persistent health inequalities: Sheffield City Council, Greater Manchester Combined Authority and Scottish Government. We will focus on four jointly agreed policy priorities for good health: - Creating a fairer economy - Promoting mental wellbeing - Providing affordable, good quality housing - Preventing long-term effects of difficult childhoods. OUR COMPLEX SYSTEMS SCIENCE APPROACH Each of the above policy areas is a complex political system with many competing priorities, where policy choices in one sector (e.g. housing) can have large unintended effects in others (e.g. poverty). There is often no "correct" solution because compromises between different outcomes require value judgements. This means that to assess the true benefits and costs of a policy in relation to health, policy effects and their interdependencies need to be assessed across a wide range of possible outcomes. However, no policymaker has knowledge of the whole system and future economic and political developments are uncertain. Ongoing monitoring of expected and unexpected effects of policies and other system changes is crucial so failing policies can be revised or dropped. We propose to use complex systems modelling, which has been developed to understand and make projections of what might happen in complex systems given different plausible assumptions about future developments. Our models will be underpinned by the best available data and prior research in each policy area. Our new evidence about likely policy effects across a wide range of outcomes will help policy partners decide between alternative policies, depending on how important different outcomes are to them (e.g. improving health or economic growth). We will develop support tools that can visualise the forecasts, identify policies that achieve the desired balance between competing outcomes and update recommendations when new information emerges. Whilst new to public health policy, these methods are well-established in engineering and climate science. We will 1. Work with policy partners to understand the policy systems and evidence needs 2. Bring together existing data and evidence on each policy system (e.g. links between policies and outcomes, interdependencies between outcomes) 3. Explore citizens' preferences for prioritising when not all outcomes can be achieved 4. Link policies and their health and non-health effects in computer models to analyse benefits and costs over time 5. Build an interactive tool to help policy decision-making, inform advocacy action and support political debate. SIPHER's MAIN OUTCOME We will provide policymakers with a new methodology that allows them to estimate the health-related costs and benefits of policies that are implemented outside the health sector. This will be useful to our partners, and others, who want to assess how scarce public sector resources can be spent to maximise the health and wellbeing benefits from all their activities.

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.