Continuity of Care in the Age of AI: Supporting Safer Handovers in Primary and Community Health and Social Care

Why It Matters

Improving handover continuity can cut avoidable harm and support a strained care workforce, delivering better outcomes for patients reliant on multidisciplinary services.

Key Takeaways

• 65% of domiciliary workers report informational gaps at shift start
• Safety prompts enforce critical actions without overriding professional judgement
• Integrated wellbeing support requested by 90.5% of surveyed carers
• Framework emphasizes equity, governance, and frontline co‑design

Pulse Analysis

Handovers between general practitioners, district nurses, mental‑health workers and home carers have long been a weak link in the care chain, contributing to medication errors, duplicated tests and delayed interventions. As health systems grapple with staff shortages and rising demand, human‑centred artificial intelligence offers a pragmatic bridge: technology that augments, rather than replaces, professional judgement. By automatically synthesising patient data into concise, change‑focused summaries, AI can ensure that each shift begins with a clear, consistent picture of the individual's needs, reducing the cognitive load on clinicians and lowering the risk of information loss.

The proposed four‑component framework builds on this premise. Cross‑team communication tools create a shared situational awareness platform, allowing multidisciplinary teams to view real‑time updates and flag emerging risks. A safety‑prompt layer nudges staff to complete critical actions—such as medication reconciliation—without dictating clinical decisions, preserving autonomy while standardising essential processes. Crucially, the model embeds workforce wellbeing, offering alerts and resources that acknowledge carers’ stress and fatigue. Empirical data from a pilot of 21 domiciliary workers underscore the relevance: 65% reported gaps at shift start, over half prioritised medication reminders, and more than 90% sought integrated wellbeing support.

Translating this vision into practice demands rigorous governance, equity safeguards, and co‑design with frontline professionals. Ethical guidelines must address data privacy, algorithmic bias, and the potential for over‑reliance on automated cues. Moreover, equitable rollout ensures that rural and underserved communities benefit equally from AI‑enhanced handovers. As health policy increasingly champions digital transformation, this framework positions AI as a population‑health lever—one that can improve continuity of care, protect vulnerable patients, and sustain a resilient workforce across the UK's primary, community, and social‑care ecosystems.