Developing and optimising an intervention prototype for addressing health and social care need in multimorbidity

<ns5:p> <ns5:bold>Research Aim</ns5:bold> </ns5:p> <ns5:p>The objective of this project was to translate artificial intelligence (AI) data models into a practical, low-burden primary care intervention. Specifically, the project aimed to co-design and refine an early-stage tool that enables healthcare teams to easily identify and address the social care needs of patients living with multiple long-term conditions (MLTCs).</ns5:p> <ns5:p/> <ns5:p> <ns5:bold>Background</ns5:bold> </ns5:p> <ns5:p>An increasing number of people live with multimorbidity—the presence of two or more chronic health conditions such as diabetes, heart disease, or depression. These illnesses frequently intersect with complex socio-economic challenges, including financial strain, unstable housing, and social isolation. While these social care needs profoundly impact clinical outcomes and overall quality of life, they are rarely identified during standard, time-constrained medical consultations.</ns5:p> <ns5:p>This project builds directly on the foundational, NIHR-funded AIM programme. The AIM study utilised machine learning to analyse anonymous health records, successfully identifying four distinct population clusters based on shared health and social care patterns. While stakeholders recognised the value of this AI data, they emphasised that algorithms must support, rather than replace, human clinical interactions. Furthermore, a comprehensive scoping review of nearly two thousand published studies revealed that existing digital tools focus almost exclusively on medical metrics like symptom tracking, often increasing the treatment burden on vulnerable patients. Consequently, a clear policy and clinical gap exists for an intervention that addresses broader life priorities.</ns5:p> <ns5:p/> <ns5:p> <ns5:bold>What We Did</ns5:bold> </ns5:p> <ns5:p> To bridge the gap between data science and clinical practice, the research team used a multi-stage, qualitative approach: <ns5:list list-type="bullet"> <ns5:list-item> <ns5:p>Stakeholder Interviews: In-depth qualitative interviews were conducted with 24 individuals living with MLTCs and 20 health and social care providers to understand real-world boundaries, resource constraints, and systemic expectations.</ns5:p> </ns5:list-item> <ns5:list-item> <ns5:p>Prototype Co-Design: Synthesising these insights with literature reviews, the team generated a non-digital paper prototype. The prototype consisted of a one-page consultation prompt for clinicians and a self-navigation priority checklist for patients.</ns5:p> </ns5:list-item> <ns5:list-item> <ns5:p>Iterative Usability Testing: The prototype underwent "think-aloud" usability testing with 16 patients and 15 primary care professionals. Feedback was analysed dynamically throughout the fieldwork, allowing for rapid, recorded adjustments to the prototype's layout, flow, and terminology to minimise cognitive load.</ns5:p> </ns5:list-item> </ns5:list> </ns5:p> <ns5:p> <ns5:bold>What We Found</ns5:bold> </ns5:p> <ns5:p>The research confirmed that patients view trusted primary care settings as appropriate hubs for initiating social care conversations, provided the approach is non-judgemental. However, clinicians expressed concern over severe time constraints and limited local referral pathways.</ns5:p> <ns5:p> The iterative testing crystallised six foundational user requirements for integrating health and social care tools: <ns5:list list-type="order"> <ns5:list-item> <ns5:p>Patient Autonomy: Individuals must retain control over which social priorities to address first.</ns5:p> </ns5:list-item> <ns5:list-item> <ns5:p>Transparent Intent: The purpose and limitations of the tool must be stated explicitly up front.</ns5:p> </ns5:list-item> <ns5:list-item> <ns5:p>Low Treatment Burden: Visual layouts and wording must remain highly simplified and calming.</ns5:p> </ns5:list-item> <ns5:list-item> <ns5:p>Validation of Past Experiences: The tool must sensitively account for patients who have previously struggled to access care.</ns5:p> </ns5:list-item> <ns5:list-item> <ns5:p>Clear Operational Roles: Primary care staff must have clearly defined boundaries regarding their social care responsibilities.</ns5:p> </ns5:list-item> <ns5:list-item> <ns5:p>System Integration: Any final intervention must align seamlessly with existing NHS electronic health records and standard appointment durations.</ns5:p> </ns5:list-item> </ns5:list> <ns5:bold>Patient and Public Involvement and Engagement (PPIE)</ns5:bold> </ns5:p> <ns5:p>Patient and public involvement was fundamental to ensuring the ethical application of AI-derived insights. PPI contributors collaborated closely with the research team to design all participant-facing study literature. Utilising a digital-first, visually accessible methodology, public advisors reviewed and refined the prototype's language. This direct involvement ensured that the wording was supportive, accessible to individuals with varying levels of literacy, and entirely free of institutional or stigmatising jargon.</ns5:p> <ns5:p/> <ns5:p> <ns5:bold>Conclusion</ns5:bold> </ns5:p> <ns5:p>By anchoring advanced algorithmic modelling in human-centred design, this project successfully created a viable framework to address social care needs within primary care. The research demonstrates that complex population data can be translated into clear, actionable, and low-burden clinical tools. This approach offers an equitable pathway toward integrated, holistic care that improves patient wellbeing without exacerbating NHS staff shortages or consultation pressures.</ns5:p> <ns5:p/> <ns5:p> <ns5:bold>Next Steps and Future Plans</ns5:bold> </ns5:p> <ns5:p>Backed by extended funding from the original NIHR grant, the project has entered a commercial partnership to translate the paper prototype into a live, NHS-compatible digital application named IndependAble. Future work will focus on securing NIHR Research for Social Care funding to launch a robust, multi-site evaluation. This upcoming trial will formally measure IndependAble's clinical utility, its impact on social care identification rates, and its operational sustainability within busy primary care services.</ns5:p>