Introduction

The safeguarding of adults and children within healthcare settings is a complex and resource intensive challenge. There is also a drive to support people living with dementia to remain in their own homes for as long as possible with a key focus on care in the community. Furthermore, supporting people with complex needs, who have enduring mental health, dementia, learning disabilities and or autism, remain a key focus for acute hospitals, as often the emergency department becomes the only place to seek support in times of crisis, due to a lack of robust care pathways. Traditional assessment processes, while robust, are often hindered by fragmented information, inconsistent risk identification, and workforce pressures. Artificial Intelligence (AI) offers a transformative opportunity to support clinical expertise, streamline multi-agency collaboration, and enable earlier, more accurate interventions. The use of AI and technology to support health and social care organisations will be vital in mitigating the risks and challenges associated with this, as such focus on timely intervention that will avoid admission to acute hospitals.

This blog explores the potential of AI-driven solutions in safeguarding, dementia care and the management of complex cases offering practical frameworks for implementation, risk mitigation strategies, and ethical considerations. It is designed for health and social care leaders, safeguarding professionals, and policymakers seeking to future-proof their safeguarding, dementia care and the management of complex cases systems and processes while maintaining the highest standards of person-centred/person led care.

Current Analysis of Safeguarding, Dementia and the Management of Complex Cases in Health and Social Care

Key System Challenges

• Fragmented Pathways - information is distributed across agencies, creating gaps and delays in risk identification. A key challenge includes conflict in the interpretation of legislation, which often leads to professional disagreements. Each professional may cite different legal frameworks to justify why support cannot be provided, resulting in inconsistent outcomes for individuals.
• Resource Pressures  - increasing caseloads and workforce shortages limit the capacity for thorough, timely assessments. Delays often occur in triaging safeguarding concerns, referrals for support in dementia care, or addressing the needs of a complex person who may have mental health, learning disabilities and or autism,  and providing feedback to individuals and their families, which in turn slows down investigations or support required.
• Inconsistent Practice -  variability in staff experience and interpretation of legislation (e.g., the Mental Capacity Act, Mental Health Act) leads to inconsistent outcomes. This often delays the provision of advice and support. In complex cases, legal departments are frequently consulted, which can further delay resolution. Professionals may hesitate to act due to concerns about the implications of their decisions, as such to address this, professionals should be supported in making the right decisions. Complex decisions should not be made in isolation, and AI can provide a structured platform to outline the decision-making process, whilst, at the same time helping practitioners understand that AI does not replace professional judgment or accountability. Instead, it serves as a support tool, offering a more structured approach and ultimately, the final decision remains with the professional, who must determine whether the AI’s recommendations are credible and justifiable in achieving the best outcome for the individual.
• Documentation Overload -  critical information is often buried in lengthy records, making it difficult to identify patterns or emerging risks. AI can help professionals reduce the time spent on administrative tasks such as record-keeping, allowing them to dedicate more time to direct care and support with patients and service users they support.

Impact on Populations at Risk

• Delayed or missed interventions, particularly for people living with dementia, learning disabilities and or autism, complex mental health needs, or people from hard to reach communities.
• Increased risk of abuse and neglect and or harm due to uncoordinated or reactive responses. The focus should be on practice systems and processes that advocate the 6 principles of safeguarding, which are empowerment, prevention, protection, proportionality, partnership working and accountability and how these fits in within the context of AI systems and processes. The key focus will be on where the role of AI can be enhanced and strengthened to allow practitioners to spend invaluable time with patients and service users in practice.
• Staff burnout and reduced morale due to system inefficiencies is one of the key challenges for staff as the level of workload created within practice. This can create an environment where staff can become overwhelmed by admin tasks and record keeping that requires them to evidence what they are doing in practice. Therefore, the role of AI can be reviewed to establish where and how it can be used to support staff.

Key Challenges in Traditional Assessments

• Manual Data Review – this is reliance on staff to collate and interpret large volumes of information and increases risk of oversight. The use of AI can eliminate the need for this to ensure that staff can focus on what matters the most in practice which is to spend invaluable time with patients and service users.
• Variable Risk Thresholds -differing interpretations of risk factors can lead to under- or over-reporting of concerns or triaging referrals timely. This can create challenges for staff when there is varied interpretation of this, as such AI can provide a summary of evidence based threshold which staff can consider to ensure that best practice guidance is reflected in this.
• Slow Escalation - early warning signs may be missed, resulting in delayed responses to safeguarding concerns or referrals for assessments and reviews. One of the key issues for staff is the delay in escalation of concerns and this is often seen in safeguarding reviews, as such the use of AI can enable practitioners to identify the risks much sooner and escalate this to ensure that the concerns are dealt with timely. This will include where duty of candour needs to be communicated to the patient or service user and their loved ones, as well as enable concerns to be dealt with timely through the right governance and the risks mitigated immediately.
• Limited Predictive Capacity - traditional methods are reactive, rarely identifying risk before incidents occur. The need to adopt a preventative approach to dealing with incidents or referrals for support and assessments will mean that risks are identified and triaged much sooner to enable the staff to address these timely before they escalate.

AI Technologies and Applications for Health and Social Care

Predictive Analytics

• AI-driven models can analyse multi-agency data to identify individuals at elevated risk, enabling proactive intervention.
• Example: Flagging patients or service users with repeated missed appointments, medication non-adherence, or changes in behaviour patterns.

Natural Language Processing (NLP)

• NLP tools can scan clinical notes, care plans, and incident reports to surface hidden safeguarding concerns or referrals and reviews.
• Supports identification of subtle changes or recurring themes that may signal emerging risk.

Pattern Recognition and Learning

• Algorithms can detect patterns in injuries, behavioural changes, or care interactions that may indicate abuse or neglect, as such outline themes and treads.
• Particularly valuable for non-verbal patients and service users or those with cognitive impairments.

Automated Screening and Triage

• AI-powered systems can conduct initial risk screenings, ensuring consistent application of safeguarding criteria or referrals an reviews.
• Enables staff to focus on the management complex cases while routine cases are efficiently triaged.

Multi-agency Data Integration

• AI can facilitate secure sharing and analysis of information across health, social care, and third-sector partners.
• Reduces duplication and supports holistic, person-centred safeguarding, dementia care and the management of complex cases.

Risk Management and Ethical Safeguards

Human-Centred Decision Making – this focuses on the importance that AI must not replace professional judgment, especially in complex or nuanced cases, and that final decisions should always rest with qualified professionals. Furthermore, data privacy and security must be taken into consideration. Compliance with GDPR, NHS Digital, and other relevant standards should be clearly outlined, alongside the implementation of robust data anonymisation, encryption, and access controls.

Bias prevention is also essential - regular audits of AI systems for bias, particularly regarding ethnicity, disability, or socioeconomic status, must be maintained. It is equally important to engage diverse stakeholders in system design and evaluation. The need to focus on treads and themes that might identify health inequalities will also be reflected in this which will provide invaluable data to analyse and address.

Transparency and accountability – this will be central to this, and AI decision-making processes and rationales should be clearly documented, and clear channels must be provided for staff to challenge or override AI-generated recommendations where this has been indicated.

The importance of professional development must also be emphasised. AI competencies should be embedded into ongoing staff development, fostering a culture of curiosity, critical thinking, and ethical reflection.

Practical Recommendations

• Start Small, Scale Thoughtfully - begin with pilot projects in areas of greatest need.
• Co-Produce with Staff - involve frontline practitioners in design, implementation, and evaluation.
• Invest in Continuous Learning - make AI literacy part of workforce development strategy.
• Maintain Person-Centred Focus - use AI to enhance, not dilute he dignity and autonomy of those supported.
• Monitor, Evaluate, Adapt -use robust metrics and feedback loops to drive iterative improvement.

Conclusion

AI represents a powerful tool for transforming safeguarding in health and social care, offering the potential for earlier intervention, more consistent practice, and reduced administrative burden. However, its success depends on thoughtful implementation, robust ethical safeguards, and a relentless focus on person-centred, evidence-based care.

How PMH Consultancy and Education Can Support

Staff Education and Competencies – we will support in delivering training on AI fundamentals, benefits, and limitations. PMH Consultancy and Education’s leadership and innovation courses are adapted to include AI literacy.

PMH Consultancy and Education Ltd stands ready to support organisations on this journey, offering bespoke consultancy, education, and leadership to ensure that AI and technology enhances what practitioners are able to do.

Contact PMH Consultancy and Education Ltd for a free consultation to discuss how we can help you harness AI for safer and more compassionate care.

By: Pat Hobson- Director and Independent Nurse Consultant, Dementia, Safeguarding and the Management of Complex Cases Expert and Author

Email:[email protected] or visit our website on www.pmhcande.com