Claims inflation — Can AI reduce the cost of care?

On the 3 July 2025 the UK Government published its 10 year Health Plan for England, 10 Year Health Plan for England: fit for the future - GOV.UK, with a chapter dedicated to the transformation of healthcare through the integration of AI. We explore the role of AI in personal injury care claims and whether it may help to curb claims inflation.

The use of AI and automation has exploded in recent years, think of Chat GPT, smart home devices, self-driving cars, etc. The future is likely to bring developments we can barely comprehend at this time, for example will your ear buds be able to detect e.g. seizures, bipolar disorder, early-stage Alzheimers or even a brain tumour - as well as play your favourite tunes? This may not be far off given that a patent was issued in 2023 for an Airpods Sensor which measures the electrical activity of the user’s brain.

AI used to supplement or replace elements of care packages has, however, been slower to progress although AI and technological solutions already implemented in some care settings include:

• sensors that monitor people overnight and provide audio to carers on shift;
• a face recognition app which can detect if a non-verbal patient is in pain; and
• the use of robots in the training of carers and therapists in aspects of physical care.

AI tools in home-based care packages include the use of assistive robots which stay in the service user’s home to remind them when to eat, drink and take their medication. There are, however, reports that robots tended to create additional work since they need to be moved around, maintained, cleaned, updated and their function explained in detail to residents.

AI and robotics will undoubtedly play an increasing role in the future of personal injury claims. Compensators should expect to see claimants seeking damages for these modern technologies which introduce an additional layer of cost and complexity to personal injury claims, especially if bespoke expert evidence is required. The question is whether there will be a reciprocal reduction in the level of contact hours and overall cost of the care package. 

We have consulted with Colin Beacock, Registered Nurse and Senior Associate of TG Associates, who has been monitoring AI developments in the care landscape.  Colin draws our attention to the following: 

Person-centred care and rehabilitation planning

In the Personal Injury sector, Rehabilitation Case Managers primarily have backgrounds in either Nursing or Occupational Therapy. The use of AI systems is driving an acceptance that the need for person-centred data within these professions must shift away from being a perceived burden that encumbers practitioners, to an essential component of high-quality care and rehabilitation. The professional bodies responsible for these areas of practice are extending their guidance on the use of AI in health and social care practice accordingly. 

In June 2025 the Nursing Times produced an article on the integration of AI into clinical practice in which they emphasised the “overwhelming documentation burden faced by nurses, and how AI could be harnessed to free up time to care”. The Nursing and Midwifery Council (NMC) programme of work for 2025/6 includes a review of the NMC Code of Conduct, NMC standards and NMC revalidation criteria, each of these will include standards for professional practice in the use of AI. The Health Care Professionals Council (HCPC) have mapped AI usage to its existing practice standards and included guidance for education providers on how AI should be integrated into curricula for Occupational Therapists. 

As yet, the principal area of application of AI within the Personal Injury sector appears to be within rehabilitation case management. Co-ordinating and maintaining multi-disciplinary rehabilitation and care management of people with complex needs is a demanding process in which maintenance of person-focussed data is a key area of function for Case Managers. Maintaining direction and attainment of goal-driven rehabilitation outcomes across various areas of practice is crucial in these services and relies heavily upon consistent measurement of outcomes and modification of planned interventions. 

The application of AI has significant potential in this area of practice, particularly in privately funded packages as part of ongoing litigation. Using person-focussed data enables the Case Manager to plan and organise care and activities that are tailored to the individual needs and lifestyle preferences of the client, whilst alleviating waste and driving growth toward their optimum independent function. AI will assist this process through its ability to analyse vast amounts of medical, therapy and care information and recommend future options for modified forms of intervention. 

The further application of AI in this area serves to assist the case management and MDT process with maintaining key administrative tasks. The overall benefit would be to enable routine data gathering to inform and enable case management that improves outcomes for the clients. These positive applications of AI are driving analysis of cost-benefit together with increased efficiency and accuracy in the personal injury rehabilitation process.

Impact on longer-term care costs

In the BBC article “Can AI care for your loved ones in their old age”, (BBC 6 May 2025) Dr Caroline Green, Head of the Institute of Ethics, emphasised that that there is no Government policy on the use of AI in Social Care. She went on to say that any investment in AI, in the future needs to be matched to the development of staff who would be using it. The organisational cost of staff development schemes represents a considerable investment on their part and they will rightly expect a return in terms of cost-benefit and enhanced efficiency. Dr Green described the application of AI in improving management of falls and pain management in elderly people as well as early detection of chest infections and similar conditions associated with later life through improved analysis of data.

From a Care Expert perspective, a cost benefit analysis of the use of any AI in individual packages of care requires integration into the case management process. The upfront costs of systems that directly impact on care and those that support care, must be considered in terms of improved efficiency and reduced costs and AI can augment that very process. Undertaking economic impact assessments through cost benefit analysis is becoming a familiar concept in care and would readily apply to measurement of the improvements to be achieved through AI usage.

For those providing care in the sector, as the use of information technology is developing at a rapid rate in the PI sector, AI driven hardware will need to be developed and replaced. The forecasting of future costs and benefits of that process can be significantly assisted by application of AI systems at organisational level. 

With direct care provision, application of AI generated alternatives to hands-on care through robotics or systems of monitoring and observation must result in cost savings and improved efficiency. These will need to be integrated into the planning of future care packages, whilst enhancing care and rehabilitation outcomes. 

Together with Colin Beacock, we note the inevitable challenges with AI in care, which include:

• Ethics & bias: There remains a risk of algorithmic bias which could perpetuate or amplify existing societal biases. 
• Data security:  Questions remain as to who owns the data retrieved and used to run AI. It is anticipated that UK Data Protection laws will need updating.
• Data collection: There is currently a proliferation of data gathering in statutory services and this impacts on all parts of the care sector.  For AI to be an effective tool the data that drives it must be valid (measure the right thing), and reliable (consistently measure the right thing).
• Ghosting:  This is when AI goes wrong.  Either the data used is incorrect or, as AI can sometimes do, entirely made up! 
• Variability:  There are already numerous AI driven systems, and this could result in unacceptable variation of practice especially where non-validated systems and tools are used.
• Too complex: Until AI develops further and is properly embedded, we anticipate that the simple, easy to understand smart devices or mobile apps are more likely to lead to greater success in promoting independence of injured parties. These more straightforward options are of course also less expensive than the high-tech assistive robots.  

On the 3 July 2025 the UK Government published its 10 year Health Plan for England, 10 Year Health Plan for England: fit for the future - GOV.UK, with a chapter dedicated to the transformation of healthcare through the integration of AI. We explore the role of AI in personal injury care claims and whether it may help to curb claims inflation.

The use of AI and automation has exploded in recent years, think of Chat GPT, smart home devices, self-driving cars, etc. The future is likely to bring developments we can barely comprehend at this time, for example will your ear buds be able to detect e.g. seizures, bipolar disorder, early-stage Alzheimers or even a brain tumour - as well as play your favourite tunes? This may not be far off given that a patent was issued in 2023 for an Airpods Sensor which measures the electrical activity of the user’s brain.

AI used to supplement or replace elements of care packages has, however, been slower to progress although AI and technological solutions already implemented in some care settings include:

• sensors that monitor people overnight and provide audio to carers on shift;
• a face recognition app which can detect if a non-verbal patient is in pain; and
• the use of robots in the training of carers and therapists in aspects of physical care.

AI tools in home-based care packages include the use of assistive robots which stay in the service user’s home to remind them when to eat, drink and take their medication. There are, however, reports that robots tended to create additional work since they need to be moved around, maintained, cleaned, updated and their function explained in detail to residents.

AI and robotics will undoubtedly play an increasing role in the future of personal injury claims. Compensators should expect to see claimants seeking damages for these modern technologies which introduce an additional layer of cost and complexity to personal injury claims, especially if bespoke expert evidence is required. The question is whether there will be a reciprocal reduction in the level of contact hours and overall cost of the care package. 

We have consulted with Colin Beacock, Registered Nurse and Senior Associate of TG Associates, who has been monitoring AI developments in the care landscape.  Colin draws our attention to the following: 

Person-centred care and rehabilitation planning

In the Personal Injury sector, Rehabilitation Case Managers primarily have backgrounds in either Nursing or Occupational Therapy. The use of AI systems is driving an acceptance that the need for person-centred data within these professions must shift away from being a perceived burden that encumbers practitioners, to an essential component of high-quality care and rehabilitation. The professional bodies responsible for these areas of practice are extending their guidance on the use of AI in health and social care practice accordingly. 

In June 2025 the Nursing Times produced an article on the integration of AI into clinical practice in which they emphasised the “overwhelming documentation burden faced by nurses, and how AI could be harnessed to free up time to care”. The Nursing and Midwifery Council (NMC) programme of work for 2025/6 includes a review of the NMC Code of Conduct, NMC standards and NMC revalidation criteria, each of these will include standards for professional practice in the use of AI. The Health Care Professionals Council (HCPC) have mapped AI usage to its existing practice standards and included guidance for education providers on how AI should be integrated into curricula for Occupational Therapists. 

As yet, the principal area of application of AI within the Personal Injury sector appears to be within rehabilitation case management. Co-ordinating and maintaining multi-disciplinary rehabilitation and care management of people with complex needs is a demanding process in which maintenance of person-focussed data is a key area of function for Case Managers. Maintaining direction and attainment of goal-driven rehabilitation outcomes across various areas of practice is crucial in these services and relies heavily upon consistent measurement of outcomes and modification of planned interventions. 

The application of AI has significant potential in this area of practice, particularly in privately funded packages as part of ongoing litigation. Using person-focussed data enables the Case Manager to plan and organise care and activities that are tailored to the individual needs and lifestyle preferences of the client, whilst alleviating waste and driving growth toward their optimum independent function. AI will assist this process through its ability to analyse vast amounts of medical, therapy and care information and recommend future options for modified forms of intervention. 

The further application of AI in this area serves to assist the case management and MDT process with maintaining key administrative tasks. The overall benefit would be to enable routine data gathering to inform and enable case management that improves outcomes for the clients. These positive applications of AI are driving analysis of cost-benefit together with increased efficiency and accuracy in the personal injury rehabilitation process.

Impact on longer-term care costs

In the BBC article “Can AI care for your loved ones in their old age”, (BBC 6 May 2025) Dr Caroline Green, Head of the Institute of Ethics, emphasised that that there is no Government policy on the use of AI in Social Care. She went on to say that any investment in AI, in the future needs to be matched to the development of staff who would be using it. The organisational cost of staff development schemes represents a considerable investment on their part and they will rightly expect a return in terms of cost-benefit and enhanced efficiency. Dr Green described the application of AI in improving management of falls and pain management in elderly people as well as early detection of chest infections and similar conditions associated with later life through improved analysis of data.

From a Care Expert perspective, a cost benefit analysis of the use of any AI in individual packages of care requires integration into the case management process. The upfront costs of systems that directly impact on care and those that support care, must be considered in terms of improved efficiency and reduced costs and AI can augment that very process. Undertaking economic impact assessments through cost benefit analysis is becoming a familiar concept in care and would readily apply to measurement of the improvements to be achieved through AI usage.

For those providing care in the sector, as the use of information technology is developing at a rapid rate in the PI sector, AI driven hardware will need to be developed and replaced. The forecasting of future costs and benefits of that process can be significantly assisted by application of AI systems at organisational level. 

With direct care provision, application of AI generated alternatives to hands-on care through robotics or systems of monitoring and observation must result in cost savings and improved efficiency. These will need to be integrated into the planning of future care packages, whilst enhancing care and rehabilitation outcomes. 

Together with Colin Beacock, we note the inevitable challenges with AI in care, which include:

• Ethics & bias: There remains a risk of algorithmic bias which could perpetuate or amplify existing societal biases. 
• Data security:  Questions remain as to who owns the data retrieved and used to run AI. It is anticipated that UK Data Protection laws will need updating.
• Data collection: There is currently a proliferation of data gathering in statutory services and this impacts on all parts of the care sector.  For AI to be an effective tool the data that drives it must be valid (measure the right thing), and reliable (consistently measure the right thing).
• Ghosting:  This is when AI goes wrong.  Either the data used is incorrect or, as AI can sometimes do, entirely made up! 
• Variability:  There are already numerous AI driven systems, and this could result in unacceptable variation of practice especially where non-validated systems and tools are used.
• Too complex: Until AI develops further and is properly embedded, we anticipate that the simple, easy to understand smart devices or mobile apps are more likely to lead to greater success in promoting independence of injured parties. These more straightforward options are of course also less expensive than the high-tech assistive robots.