The objective of Procure4Health project is to overcome the barriers to European wide adoption of innovation procurement by creating an open community of health and care procurement stakeholders. Its 33 founding partners are actively promoting innovation procurement through knowledge sharing and capacity building, networking and matchmaking, identification of common needs and the launch of joint actions to address them as well as influencing policy on procurement of innovation.

This proposal is motivated by the need to reduce the public deficit. One way to do this is by achieving efficiency savings in procurement for large public institutions such as the National Health Service, city councils, or the Ministry of Defence. We propose to contribute towards this goal by attempting to better align the stylised theoretical analysis of tendering - a form of trading mechanism - with the facts on the ground. The focus of our study is the provision and use of information in the tendering process, building on two recent methodological developments: "Information Design" and "Simple Auctions". Trading mechanisms have been the subject of a great deal of study, especially in the last half a century. More recently, the enormously successful sale of the 3G mobile phone licences by simultaneous auctions - £22.5 billion was raised for the public purse and the band of radio frequency was efficiently assigned - in 2000, provided vivid evidence of how useful this theory can be. The literature on "auctions" is focussed on finding the optimal trading mechanism, which maximizes expected benefits. However, on the one hand, this optimization assumes that the information available to the bidders is predetermined. This is often too strong an assumption as the bid taker may have significant leeway in choosing what information to gather and disclose. On the other hand, the optimization traditionally leaves both the complexity of the mechanism and its use of the information revealed by the bidders unconstrained. This often results in very complicated "optimal" mechanisms, which are hard to implement in practice. We propose to push out the research frontier by analysing what information, and in which form, is presented to the potential traders and how information revealed by them is used by the designer to determine prices and trades. The first of these novel ideas is information design: the optimal provision of information to a group of interacting agents by a designer with a certain objective. By strategically choosing its method for scoring the bids and by seeking out and revealing additional facts that might affect the cost of suppliers, the designer can create interdependence between the agents' information; this can then be exploited through the competitive bidding process, ultimately benefiting the designer's objective. The second idea is based on the observation that due to the complex objective of the buyer (quality, timing, transparency, sustainability etc. in addition to price) most actual tenders are multi-dimensional: the bids submitted include several different factors besides price. While a pre-announced scoring rule can transform these bids into readily comparable one-dimensional scores, it does not eliminate the complexity of bids and of the bidders' beliefs about the bids of others. For practical reasons, the designer needs to compensate for this innate complication by simplifying the mechanism, resulting in additional restrictions on the set of mechanisms she can choose from. These restrictions imply that families of mechanisms previously discarded as sub-optimal, now become relevant. To capture this scenario, we analyse decentralised mechanisms, where conditional on trading, prices are independent of the bids of competitors. In the context of scoring auctions, this would correspond to a discriminatory "first-score" auction. According to the existing theoretical literature, when the quantity traded is not set beforehand, these auctions are not optimal. Together, these two approaches make it possible to advance our understanding of issues like simultaneous bidding and realistic mechanisms that deal with interdependent valuations. While doing that we will also pay particular attention not to be hemmed in by the artificial boundary between micro- and macro-economic analyses, so that our insights can be exported to system-wide markets, such as the labour and credit markets.

IMPACT AMR is a transdisciplinary network of national and international researchers and stakeholders seeking to address key policy questions around antimicrobial resistance (AMR) mitigation investment priorities through the development of an evaluation framework to compare the impact and attributes of mitigation interventions. There is rising attention to the problem of AMR and political impetus to mitigate it, however currently there is little to guide decision-makers about which of the many interventions to address AMR, across multiple sectors and domains, will be best to invest in to reduce the AMR burden most effectively, feasibly, acceptably and economically. We address the need to establish consensus on key AMR impact metrics as a basis for developing intervention priorities across all One Health (i.e. human, animal, and environmental) settings. In methodological terms we draw on experience and learning in other complex policy spheres, and climate change mitigation in particular. The network will work with the AMR community and wider stakeholders to collate evidence to evaluate the technical, economic, behavioural, social and policy effectiveness, feasibility and acceptability of existing AMR interventions. A systematic approach to evaluate interventions with these criteria would not normally be possible, and requires the opportunity for transdisciplinary working, which extends beyond the boundaries of academia to bring science into conversation with policy, publics, civil society, industry, farmers, food manufacturers, water industry and health practitioners. The evaluations put forward by the multi-stakeholder groups and desk reviews will be set in an evolving appraisal framework that will cumulate data across different domains, including (a) optimising antimicrobial use, (b) managing infection without antimicrobial use, (c) preventing infection and (d) minimizing antimicrobial pollution. The network activities will take place across four workstreams which will serve as a basis for identifying impacts of AMR interventions, prioritising interventions, identifying data gaps and directing onward research in intervention development. The two tasks of defining impact and identifying priority AMR interventions form the central ambition of the IMPACT AMR network and will be tackled as Workstreams (WS) 1 and WS2, involving a range of consultative, review, mapping and framework development activities. WS3 will work across four domains of AMR intervention, each involving human, animal and environmental health, to map existing intervention evidence, evaluate impact, and to stimulate the development of new research to apply the IMPACT AMR framework and address emergent gaps. WS4 will provide a coordination and communication function within the network and with external stakeholders. This network presents a unique opportunity to drive forward answers to this critical challenge, filling a conspicuous gap in the AMR policy landscape and providing a framework to link in data and parameters from other networks, initiatives and organisations with a focus on reducing the AMR burden. The IMPACT AMR network presents value-for-money in both its approach to galvanising wide-ranging stakeholders around a tangible goal as well as for those tasked with making AMR expenditure decisions. Initially UK focused, the network is poised to support global efforts for AMR prioritisation in a second phase of funding.

Climate change is the biggest global health threat of the 21st century. The more we ignore the climate emergency the bigger the impact will be on health and the need for care with poor environmental health contributing to major diseases, including cardiac problems, asthma and cancer. Many of the actions to mitigate and adapt to climate change and improve environmental sustainability also have positive health benefits; the Lancet Commission has described tackling climate change as "the greatest global health opportunity of the 21st century". The challenges faced present an incredible opportunity to do things differently - to take a design-led approach in designing and making through high-reward demonstrator projects to help transform the health ecosystem. Through wider public engagement we aim to advance societal understanding of design's impact, and the opportunities, barriers, behaviour changes and tools needed to transition to a green approach. This research will unite a wide range of disciplines, research organisations, regional and local industry, and other public sector stakeholders, with policy-makers. The Design HOPES Green Transition Ecosystem (GTE) Hub will sustain a phased long-term investment to embed design-led innovation, circularity, sustainability and impact for the changing market, across product, service, strategy, policy and social drivers to evolve future design outcomes that matter to the people and planet. Our research is organised around seven core Thematic Workstreams, based on the NHS Scotland Climate Emergency and Sustainability Strategy (2022-2026). Design HOPES will be delivered and managed by interdisciplinary teams with significant expertise in design and making, co-creation, health and social care, with professionals with a sustainability remit, and businesses working in the design economy. Design HOPES encompasses a rich disciplinary mix of knowledge, skills, and expertise from a range of design disciplines (i.e., product, textile, interaction, games, architecture etc.) and other disciplines (computer science, health and wellbeing, geography, engineering, etc.) that will be focused on people and planet (including all living things), from the micro to macro, from root cause to hopeful vision, from the present to the future, and from the personal to the wider system. Design HOPES will design and make things and test them to see how they work, which will help more ideas and things emerge. The Hub will be an inclusive, safe, collaborative space that will bring in multiple and marginalised perspectives and view its projects as one part of a wider movement for transformational change whilst not overlooking existing assets and how we can re-use, nurture and develop these sustainably. Design HOPES aims to be an internationally recognised centre of excellence, promoting and embedding best practice through our collaborative design-led thinking and making approaches to build a more equitable and sustainable health and social care system. We will create new opportunities to support both existing services and new design-led health innovations in collaboration with NHS Boards across Scotland, the Scottish Government, patient and public representatives, health and social care partners, the third sector, academia and industry. Our seven Thematic Workstreams and associated projects will deliver a rich mix of tangible outcomes such as new innovative products, services, and policies (e.g., sustainable theatre consumables, packaging, clothing, waste services, etc.) during the funded period. With award-winning commercialisation and entrepreneurial support from the collaborating universities, we will also look to create new "green' enterprises and businesses. We will achieve this internationally recognised centre of excellence using design-led thinking and making to build a more equitable and sustainable health and social care system.

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.