By Leon Neal,Saskia Koopman
Copyright cityam
The NHS and government have saved more than £1bn to date thanks to an AI-driven approach to home care.
HealthTech innovator Cera, which employs 10,000 carers and nurses delivering 2.5 million home visits per month, has developed predictive AI tools aimed at reducing avoidable hospital admissions, A&E attendances, and costly residential care referrals.
Dr Ben Maruthappu, Cera’s founder and chief executive, told City AM: “These figures show social care’s potential to act as the frontline of prevention for high-risk over-65s – easing NHS pressures at a time when the health service is stretched to breaking point.”
“Critically, they also show the power of technology and AI to save money, increase productivity and help transform public services – delivering better care, for less, at a time when every pound of Government spending is under pressure,” he added.
Cera’s AI tools, including falls prevention AI and the hospitalisation predict-prevent tool, use data gathered during home visits to spot health risks up to a week in advance, alerting staff to intervene in community settings.
“Just as banks use AI to spot fraudulent transactions quickly – we use AI to spot health risks early and take preventative action in the community”, Maruthappu explained.
The system reportedly allows carers to spend up to 25 per cent more time directly with patients than industry averages.
Faculty’s analysis estimates that Cera saves the NHS and government over £1.5m daily, translating to the equivalent cost of 25,000 nurse salaries for a year or 40m GP appointments.
Hugh Neylan, Head of Health at the Faculty, added that “these savings show the role AI and digitally-powered prevention can play in improving public finances, easing NHS pressures, and giving older and vulnerable people greater independence”.
Balancing promise with caution
While the figures are compelling, questions remain about scalability and real-world adoption across the public sector.
Funding and procurement structures for social care remain primarily oriented around time and volume, rather than outcomes.
Maruthappu acknowledged this, telling City AM: “Funding frameworks in care are currently set up to reward providers based on volume and time – that is, to reward quantity of care provided, over quality”.
“These frameworks need to change, to align financial incentives with innovation, prevention and patient health outcomes”.
There is also the question of AI’s reliability in such a sensitive sector as healthcare.
Cera reports over 80 per cent accuracy in predicting falls and hospitalisations up to seven days in advance, but safeguards depend heavily on staff intervention.
“Our frontline staff are trained not to rely solely on tech”, said Maruthappu. Still, integrating predictive AI into diverse, sometimes fragmented care systems may introduce operational and ethical challenges.
The UK government has signalled interest in AI and digital transformation across the NHS, but investment and procurement processes remain slow-moving.
A recent report from techUK revealed that current approaches favour short-term, low-cost solutions over scalable, innovative models.
The research highlighted several key challenges hindering AI adoption in the public sector, including outdated IT systems, a shortage of AI-skilled personnel, and procurement practices that may limit innovation.
It also offered recommendations for overcoming these barriers, such as embedding senior digital leadership within departments, promoting transparency in AI usage, and investing in digital infrastructure and skills development.
Without systemic support, even high-performing initiatives like Cera may struggle to achieve national impact.
Despite these challenges, Maruthappu sees AI-driven home care as central to the future of healthcare.
“AI-powered home healthcare is the future, not just of social care, or care in the community, but of our whole approach to healthcare… AI-powered home healthcare will stand at the centre of our health service: playing a key role in patient monitoring, empowerment, wellbeing and prevention,” he explained.
The debate over AI in social care is now shifting from theoretical potential to practical implementation.
Cera’s model offers a glimpse of what could be achieved, but realising these benefits across the NHS will depend on careful oversight, regulatory frameworks, and sustainable funding.
