16 December 2024

Mohi Khan, business transformation expert, takes a closer look at practical examples of AI deployment within the care and social care industry, highlighting key themes and risks for business leaders to consider.

He had the pleasure of chairing a panel session on the role of AI in health and social care at HealthInvestor’s Healthcare Summit. From a preliminary straw poll of the audience, it was apparent that AI is being widely considered. The panel explored some interesting examples of AI in action in the social care setting, before turning to the practical topic of how to get started. The panel agreed that setting out a vision for AI, aligned to and supporting the delivery of your organisation’s goals, underpinned by a solid data foundation and adoption programme, is key to success.

  • AI uses in social care
  • Preventive care
  • Service delivery
  • Detection and diagnosis
  • Operational processes
  • Maximise potential

AI uses in social care

While we see opportunities and benefits in common with the use of AI within the wider healthcare industry, there are a number of additional themes and risks for business leaders to consider for AI in a social care setting.

The principal areas for potential AI deployment are consistent with the wider healthcare setting, namely:

  • Preventive care and self-care
  • Service delivery
  • Detection and diagnosis
  • Administrative, operational processes.

As with the wider healthcare industry, there are opportunities for AI to be used throughout the organisation to create efficiencies and improve care delivery, from the patient-facing front-end to administrative and support functions.


Preventive care

AI can unlock opportunities for prevention and monitoring of a broad range of health, mental health and geriatric-related conditions in the context of the social and care setting.

For example, AI chatbot applications can help patients and carers with advice and support for their conditions.

Additionally, AI and digitally enabled tools can help to empower and free up time for social care workers by automating and enhancing assessment and reporting processes, including development of individual care plans.


Service delivery

In terms of service delivery, remote monitoring and predictive analytics can support enhanced outcomes for patients.

For example, in a domiciliary or residential care setting, a care worker may have a limited amount of in-person contact with the patient, but remote monitoring technologies, such as wearables, can track the activity, behaviour patterns and wellbeing of the patient on a 24-hour basis. This provides a larger set of data for an individual, and as you build those data points across populations, you can start to monitor and predict when certain interventions or checks might need to happen.

For example, care providers are using technology that enables visibility of patient conditions, when patients are eating and how much they're moving. From patterns of behaviour, they can support the care team’s assessment of patient health and wellbeing and whether any intervention is needed.

Detection and diagnosis

There are opportunities to use AI with remote monitoring technology, for example with at-home care of an elderly patient where inputs from carers, family and friends can help to build a complete picture. Predictive analytics can then be used to help produce more tailored individualised care plans and treatments. 


Detection and diagnosis

AI can bring opportunities to automate processes to enhance efficiency and productivity, such as with recruitment and retention or compliance and reporting. With the integration of AI in social care, the ultimate aim is to deliver a better service, to empower social care workers by freeing up their time to allow them to really focus on the higher risk and critical decision-making that they need to. For example, taking, sharing and analysing notes to support the care team.

During the panel, Ali Al-Mufti, Managing Director of Arcadia Care Homes, shared an example of this at the Healthcare Summit, where the residential care operations team are using AI to support with the administration around their CQC reporting requirements.

In a domiciliary or residential care setting, AI may offer an additional level of data to support care workers who play a key role in allocating local authority funds for care cases by helping them identify the more urgent or critical cases. It can also support decisions around the effective deployment of staff to determine staff numbers and skills required across site locations.


Operational processes

There are several important factors to consider in the social care setting for AI to really fulfil its promise:

Responsible development and adoption 

This issue impacts the entire healthcare industry but is particularly pertinent in social care, which typically deals with the most vulnerable sections of society, such as the elderly, individuals with mental health conditions, young adults and children. It is essential to ensure there is responsible development and adoption of AI that doesn’t impact ethical considerations and human rights, particularly around privacy and bias.

The role of AI is not to replace but to augment and enhance human capability, recognising the essential value of the human care worker at the heart of the care delivery model, where AI can equip them with tools that will never replace them.

The regulation is still playing catch up

Social care organisations need to keep an eye on the regulatory environment and government legislation. Due to the rapid nature of advances in technology, regulations are lagging behind, leaving care operators at the coalface of delivery with increasing pressure to make the right decisions around the deployment of AI in their care setting.

The interoperability of data sets

Typically, social care delivery is fragmented from the wider healthcare system, leading to fragmented data across the range of patient contact points in the healthcare system, such as GPs, social workers, local authorities, the NHS and the private and independent care sector. Charles Cross of Emma AI and Director of Ashley Care, observed that tackling data interoperability across these settings, and breaking down the silos in data that we currently see, will be essential as we move forward.

The data challenge is at two levels. Firstly, it’s the system-wide data between the various parts of healthcare and social care. Secondly, within that, it's the consent of the individual for the use of their data to support their care or as part of a data set of a wider population. Systems must be built on strong consent and data protection processes to ensure that data isn't going to be abused or exploited. This is particularly pertinent in the social care setting, where patients are providing sensitive and intimate information about their conditions. For example, when dealing with an AI-enabled chatbot, patient data must be secure.

Collaboration on ethical and security standards

To address the above issues, industry and government regulators must work together to ensure the ethical standards, data security and best practice around the interoperability of data to deliver optimum patient care. This is particularly relevant in social care, where patients may have conditions that affect their mental capacity or decision-making abilities.


Maximise potential

How our business transformation team can help your healthcare organisation

Our technology consultants can help you navigate your digital journey towards optimised processes and enhanced visibility and insights. With our deep industry knowledge and technical expertise, we can help you identify the most suitable AI solutions for your organisation’s needs. 

To discuss the business transformation needs of your healthcare organisation, please contact Mohi Khan, Joel Segal, Suneel Gupta or your usual RSM contact. 

Mohi Khan
Mohi Khan
Partner, Business Transformation
man in suit
Joel Segal
Head of Business Transformation
Suneel Gupta
Suneel  Gupta
Partner, Head of Private Healthcare
Mohi Khan
Mohi Khan
Partner, Business Transformation
man in suit
Joel Segal
Head of Business Transformation
Suneel Gupta
Suneel  Gupta
Partner, Head of Private Healthcare