A new national team will systematically evaluate innovations in UK health and social care—from digital health apps to hospital reorganisations—to determine which ones actually work, for whom, and under what conditions. Many promising new treatments, technologies, and service models fail to deliver their intended benefits once deployed in the real world. Others produce unintended consequences or widen inequalities. Yet evaluations are often conducted too late, in isolation, or without the right expertise. CARE-NET fills this gap by embedding rigorous, tailored evaluation into the rollout of innovations from the start, rather than treating it as an afterthought. If successful, the programme will give NHS leaders, social care managers, and policymakers reliable evidence on what to scale up and what to abandon—saving public money and improving patient and carer experiences. By involving patients, clinicians, and frontline staff throughout each evaluation, the team aims to produce findings that are actually used, not just published. The result could be a more efficient, equitable health and care system that learns from its own experiments in real time.
View original technical description
BACKGROUND Innovations in health and care hold great promise for improving quality, reducing inconsistency and inequity, and generating value for money. Yet the impact of new interventions is patchy: not all achieve their aims; they may have unintended consequences; and their impact is affected by the contexts into which they are introduced. CARE-NET, a national service evaluation team based at THIS Institute at the University of Cambridge and RAND Europe, will lead a five-year programme of evaluations of innovations in UK health and social care, from new technologies to organisational changes. METHODS AND APPROACH The CARE-NET team brings together expertise in a wide range of evaluation approaches (e.g. formative, summative, economic), methodologies (qualitative, quantitative and literature review) and disciplines (from anthropology and sociology to epidemiology and data science). For each innovation to be evaluated, the team will develop a tailored approach in consultation with the NIHR, other key ‘clients’ for the evaluation, and wider stakeholders. Study design will be informed by the evaluation questions to be answered, the nature of the innovation and the context of implementation, using a framework which includes evaluability assessment, incorporation of issues of equality, diversity and inclusion, stakeholder input, assessment of risks, and identification of appropriate resources. Each evaluation will be enriched by active collaboration with stakeholders, feedback and discussion of emergent findings to inform later stages, and ongoing sharing of learning with the breadth of relevant audiences. INVOLVEMENT, ENGAGEMENT AND GOVERNANCE CARE-NET will be supported and guided by a programme advisory group and by a public contributor advisory group including patients, carers, and people who draw on social care. Stakeholder engagement and involvement in each evaluation will also be crucial. Central to achieving this will be ‘expert collaborative groups’ that will bring together public contributors, health and social care professionals, managers, policymakers and others in productive, dialogical meetings with research staff—ensuring meaningful involvement in all stages of studies and reflected in evaluation outputs. In some cases additional groups will be convened, for example to amplify the voices of less-heard staff groups or marginalised communities. ANTICIPATED IMPACT AND DISSEMINATION Findings from individual CARE-NET evaluations and from the programme as a whole will be shared with a wide range of audiences using a variety of creative means. Besides contributions to the academic literature, our co-productive approach to evaluation delivery—involving the full range of relevant stakeholders throughout the evaluation cycle—will provide a solid foundation for knowledge translation, mobilising networks of collaborators across the health and care system and developing outputs in a range of forms (from visual abstracts, through two-page summaries of findings, to extended podcast debates) to suit all audiences.
Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.
Is something wrong? Let us know