The UK population is ageing, and the statistics clearly reflect this trend. One of the most significant figures that reflects this is that by 2039, the number of people aged 75 and over is expected to double from five million to nearly ten million, which raises important questions about how the nation will respond to the consequences of such a demographic shift. Enhancing caretaking facilities and caretaking in general through the use of technology is undoubtedly one important area to consider.
In this article we want to support those efforts by exploring some of the ways in which caretaking services can be improved through the power of artificial intelligence (AI) and machine learning (ML).
A national approach to improving caretaking
Given the statistics outlined above, it is no surprise that both government authorities and care providers face growing pressure to deliver high-quality, personalised care at scale. Artificial intelligence (AI) and machine learning (ML) play and will further play a role in addressing this challenge. These technologies can in many ways help in solving the current and future challenges by offering predictive capabilities, automation, and intelligent insights that support better decision-making, enabling care professionals to provide safer, more responsive, and more efficient services.
Recognising this potential, the integration of AI into healthcare has been backed by the 2019 NHS Long Term Plan. As part of this commitment, the NHS established the AI Lab with an investment of £250 million to support the development and implementation of AI technologies across the NHS and wider care system. One of the Lab’s key initiatives is the AI in Health and Care Award, which provides £140 million in funding to test and evaluate promising AI solutions, ensuring they are both effective and safe for real-world use.
Also, important to mention in this context are the most recent national policies related to elderly care which falls under the Plan for Change. The plan includes initiatives to train care leaders in the effective use of technology across care homes and similar settings. Among the technologies already being introduced are familiar tools such as motion sensors, which can detect when a resident has fallen and automatically alert staff, enabling a quicker response.
Personalised care plans and decision support
Let’s now explore how emerging technologies can support and enhance the policies outlined above. One good example is the ability of AI to create highly individualised care plans that adapt in real time. By analysing a resident’s health patterns, preferences, and responses to treatment, AI can recommend tailored dietary plans, medication schedules, and exercise routines.
In addition, these systems can offer valuable clinical decision support. For example, if a caregiver is unsure about the best course of action for a patient exhibiting new symptoms, AI can provide evidence-based recommendations drawn from thousands of similar cases. This not only improves the accuracy of care but also boosts caregiver confidence and reduces mental strain.
Predictive analytics for proactive care
One of the most promising uses of AI and machine learning in caretaking facilities is predictive analytics. By processing large volumes of historical health data, these technologies can anticipate potential health issues before they develop further.
For example, AI can detect early signs of dementia by analysing subtle changes in speech patterns, walking gait, or daily routines. Similarly, machine learning models can assess the risk of a fall by evaluating an individual’s movement data and medical history. This enables care teams to take early action by adjusting daily routines, modifying living environments, or introducing targeted physical therapy, which helps reduce the likelihood of emergencies.
In addition to supporting individual care, predictive analytics can assist with operational planning. It can forecast staffing needs, anticipate medication shortages, and identify patients who may be more vulnerable during overnight hours when staffing levels are lower. This type of foresight allows for better resource planning and contributes to more responsive and effective care delivery.
Improving communication and monitoring
Natural Language Processing (NLP), a subset of AI, is changing how caretakers communicate and document care. Voice-activated tools can allow caregivers to record updates hands-free, speeding up documentation and allowing more time for actual care.
Additionally, AI-powered monitoring systems can track residents’ vital signs, movements, and even emotional states using sensors, cameras, or wearable devices. These systems send real-time alerts if unusual patterns are detected, such as a spike in heart rate or prolonged inactivity. Such constant, intelligent surveillance enhances resident safety without being intrusive.
For families, this real-time monitoring also provides reassurance. Many platforms now offer family portals where authorised relatives can receive updates, view progress, or communicate with care staff. This level of transparency builds trust and keeps families connected with the care process.
Fall detection
Artificial intelligence and machine learning are playing a big role in developing systems for fall detection. Modern fall detection systems combine sensor data such as motion detectors, pressure pads, and wearable devices with AI algorithms that can distinguish between normal movements and a potential fall. Unlike traditional alarms or manual checks, these intelligent systems monitor activity in real time and can instantly alert staff when a fall is suspected.
Some advanced solutions even incorporate video analytics and behavioural modelling. These systems learn typical movement patterns of residents and are able to flag anomalies that may indicate instability or increased fall risk. For example, slower gait, sudden hesitations, or erratic movement may signal the need for a closer assessment by care staff.
This not only enables faster response times when a fall occurs, but also supports preventative action. When combined with predictive analytics, fall detection systems can highlight residents who are at a higher risk, prompting care teams to adapt physical environments or implement interventions such as mobility aids or tailored physiotherapy.
At Roedan, one of our ongoing projects focuses on developing a passive fall detection system tailored to healthcare environments. Drawing inspiration from the simplicity and effectiveness of passive infrared (PIR) sensors, this solution is designed to detect falls in real time without requiring any active input from patients or staff. By leveraging AI and machine learning algorithms, the system can accurately identify fall incidents, trigger immediate alerts for caregivers, and help prevent serious injuries—all while minimising the need for additional resources or disruption to daily routines.
What is next
The integration of AI and ML into caretaking facilities signals a new era, one where technology enhances rather than casting a shadow over the human element of care. As these tools continue to evolve, they will become more intuitive, more accessible, and more integrated into everyday care routines, helping caregivers and staff to improve the care for patients and loved ones.
To fully harness this potential, many facilities are already investing in both the technology itself and the training required to support its effective use. This trend is expected to become even more prominent in the near future. It is also anticipated that national policy and technological innovation will become increasingly intertwined, working together to maximise the benefits for both care providers and those receiving care.
