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Why technology-enabled care is essential to reduce the care gap



Andrew Davies, CEO of RWG Mobile, discusses how preventing long-term conditions through technology-enabled care is essential to reducing the care gap.

The COVID-19 pandemic changed not only the way we live our day-to-day lives, but the way we view and use healthcare.

The belief that the best care was provided face-to-face and involved a mandatory walk to the doctor’s surgery had to be reevaluated – care could now be provided online, in our homes, and it did not need us to go anywhere.

The advance and, above all, the acceptance of digital has naturally led to the question: what now? What path must healthcare and social care take to remain sustainable, practical, and compassionate for both short-term patients and people with chronic illnesses?

Tech advancement has been profound on so many levels, especially when it comes to sensors – home blood pressure monitors and pulse oximeters, single-lead home ECG devices – used to check sinus rhythm and atrial fibrillation – glucose monitoring, and of course – smartwatches.

Medical wearables are all around us facilitating, to an extent, remote monitoring. But what is the missing link between advanced sensor tech and true digital care?

The answer is integration. It is essential for caring organisations to implement technology-assisted care to address the care gap and reduce the likelihood of preventable long-term chronic and debilitating conditions – especially within the population of older people.

Elderly woman talking with a doctor while holding hands at home and wearing face protective mask.

Despite the proliferation of tech sensors that facilitate monitoring, the lack of an integrated care platform is a setback to achieving digital care that extends older people’s independence.

Single point applications are useful in gathering data. However, they fail to pull it all together and support medical professionals in drawing a holistic picture of the service user’s health.

Technology can notice and measure a change in a person’s behaviour but can’t account for other factors that may be affecting their daily routine: for example if they are ill, or just feeling a bit low or lonely.

That’s why there is a need for an integrated platform that can contribute to monitoring, analysing, and improving the well-being and independent living of the ageing population.

In order to achieve that connected network, that integration, healthcare and social care need to embrace a new kind of digital platform: one that brings together all the information from sensor tech and generates insights that in turn help power new workflows and interventions.

Not only will it provide masses of specialised data, it will connect the cared-for, the carers and the kinship carers (family and friends) – everyone gets to sit at the table and improve communication between all parties.

The generic term for this is Health-Platform-as-a-Service (HPaaS) – and this is the direction innovative healthcare must head for.

Falls and fall-related injuries class as a common and often serious problem for older people.

In England alone in 2021, there were more than 216,000 emergency hospital admissions in the 65+ age group. The annual costs for addressing these falls and resulting injuries for the NHS is estimated at £2.3b.

Older woman using laptop in an online consultation with her doctor.

Let’s play through some scenarios to see how an integrated platform can enable reactive and proactive treatments.

Imagine that a vulnerable person has a fall at home, a sensor can detect the fall and the platform can automate action.

A first step might be to initiate a video call with a carer, popping up a familiar face on a standard TV set. The next might be a call to a paramedic or securing physical help from friends and neighbours. Then, the professional carers can analyse the data collected before, during, and after the fall to find the cause for the fall – maybe low blood sugar or abnormal blood pressure?

Our conversations with medical professionals have also indicated the need for proactive technology – one that anticipates and prevents crises.

Movement sensors, locations awareness technology, collection of patterns, such as understanding heating and lighting behaviours, supplementary information from wearables including heart rate, oxygen levels and blood sugar levels and pop-up quizzes about mood and medication can be collected and analysed with the help of AI to portray a detailed wellness picture of the cared-for individual.

Actions can then occur – prompt a video call from a professional or family member, for example. That could not only help individuals with their medical conditions, but will provide a way to combat loneliness – a serious and challenging problem for elderly people who live independently.

Health platforms provide a unique service: it’s a connected care community, where people are taken care of digitally, but are simultaneously provided with means to communicate with kinship carers and participate in social events organised by professional carers.

It is vital that remote patient management technology must enable support for people in the community to self-manage their long-term health conditions to achieve the best outcomes for their health and well-being and to reduce the care gap.

Health platforms are a valuable toolbox that brings remote care to another level.


The role of AI in early detection of dementia



By Mike Battista, Director, Science and Research, Creyos

Dementia is a debilitating condition affecting millions all over the world, characterised by a decline in cognitive functions such as memory, reasoning, and communication.

Early detection is advantageous in planning for and managing diseases that cause dementia, as early detection can identify mild issues with cognition while pathology in the brain is still minimal, and potential interventions have the greatest chance of slowing decline.

Early modification of lifestyle and medical risk have been estimated to prevent 40 per cent of dementia cases.

Artificial Intelligence (AI) is emerging as a ubiquitous tool in healthcare, offering new hope for early dementia detection through advanced data analysis and pattern recognition.

Dementia consists of a range of symptoms that can include memory loss, confusion, atypical behaviour, and difficulty completing everyday activities.

Mike Battista

Diagnosing dementia early is challenging with traditional methods, as so many of the most common diagnostic tools rely on significant cognitive decline.

However, early detection can lead to better management of dementia slowing the progression of symptoms and improving the overall quality of life for patients.

With these benefits in mind, innovative uses of AI are needed to create more sensitive tools that can detect the most subtle signs of dementia earlier than ever before.

AI in Identifying Dementia Markers

In relation to dementia, AI can search extensive datasets to find markers indicative of the condition.

Then, by analysing massive amounts of data, AI is advancing healthcare capabilities by identifying patterns indicative of the condition that clinicians might miss.

Understanding these data markers is imperative for accurate detection.

For instance, AI can analyse cognitive performance, language use, and even facial expressions to differentiate between healthy individuals and those showing early signs of dementia.

A strong understanding of these markers comes from reviewing scientific literature and adopting a data-driven approach.

For researchers, AI systems can be trained on large datasets to help discover the subtle differences in performance and behaviour associated with early dementia.

Clinicians can make use of the models trained by researchers to accurately identify patients with early signs of dementia.

Data from clinicians can also be used to further train AI models, improving the accuracy of early detection over time.

The Importance of High-Quality Data

High-quality, clean data are essential for AI to function effectively in detecting dementia.

These data must be accurate and comprehensive, containing the “truth” about individuals’ health status.

One of the biggest challenges in healthcare AI is obtaining this level of data, as it involves building a repository of information that includes both individuals with dementia and healthy individuals.

To overcome this challenge, there are various initiatives to gather diverse data.

These include gathering diagnostic information from patients and creating data partnerships between healthcare practices and research organisations.

These data partnerships are not only interesting for research purposes but also help gather high-quality real-world data from patient populations that can be used to train AI systems for practical clinical application.

Longitudinal datasets are also key to achieving early diagnosis.

They are required to answer research questions about which early signs progress into dementia, which lifestyle factors slow progression over time, and more.

However, gathering these datasets is a complex process.

It requires collecting data from a large group of healthy individuals at a specific time and tracking who develops conditions like mild cognitive impairment or dementia over time.

This involves at least two time points with good follow-up, minimal dropout, and accurate diagnoses, making it a complicated dataset to get right.

Additionally, there is a great deal of selection bias in the patients who are repeatedly tested.

That is because these patients are likely more proactive about addressing their health, and/or more likely to already have issues and seek medical attention because of concerns about their cognition.

What is needed are large scale, randomised controlled studies where people are recruited ahead of time and followed throughout their health journey with state-of-the-art tools to assist in the process.

For example, the Maintain Your Brain trial followed thousands of older participants over three years to examine the effectiveness of lifestyle interventions meant to slow cognitive decline.

They used online cognitive testing from Creyos to ensure that testing was easy for participants, but provided high-quality data for the researchers.

normative dataset from healthy individuals allows researchers to compare individuals with a known condition to the general population, ensuring AI models can accurately distinguish the two groups.

Building and maintaining these datasets is a complex process but is critical for the success of AI applications.

An example of successful implementation of machine learning is the Creyos dementia screener, which used data from thousands of neurology clinic patients and a health normative database to train a model that accurately detects individuals with subtle cognitive impairments.

Current Success and Future Potential

Several success stories illustrate AI’s potential in early dementia detection.

For instance, pilot programmes using AI have shown promising results in identifying early cognitive decline through routine health assessments and patient interactions.

Examples of how AI recorded observations include:

  • How the patient moved the computer mouse while performing assessments
  • The time taken by the patient to read the tutorial
  • Passive data gathering such as device motion data (noting a person’s gait) handwriting, virtual reality integration (to test for driving ability), facial expression and speech pattern analysis.

These pilot programmes demonstrate novel ways to exploit AI’s capability to provide real-time monitoring and personalised treatment plans, tailored to individual patients’ needs.

It’s clear that the future potential of AI in dementia care is immense.

As technology advances, AI could enable even more precise and individualised healthcare, offering the earliest interventions and continuous monitoring that could further slow the progression of dementia.

This proactive approach could revolutionise how we manage and treat dementia, improving patient outcomes.

Ethical Considerations and Challenges

While AI offers great potential, it also raises ethical considerations and challenges.

Privacy concerns regarding patient data must be addressed, ensuring that data is securely handled, and patients’ confidentiality is maintained.

Additionally, the bias that can get baked into some AI systems must be scrutinised to maintain accuracy, and avoid false positives and negatives, which could lead to misdiagnosis and inappropriate treatments.

Ethical use of AI in healthcare involves balancing technological advancements with patient rights.

Furthermore, AI cannot replace human decision-making. Machine learning models and AI software can enhance human judgment, but a physicians must always make the final call when it comes to diagnosing dementia

AI holds tremendous promise in the early detection of dementia, offering tools that can analyse complex data and identify subtle signs of disruptions to cognition.

To fully realise this potential, continued research and investment in AI technologies and high-quality data collection are essential.

About the Author – Mike Battista, Director, Science and Research, Creyos 

Mike Battista is the Director of Science and Research at Creyos.

His interests and science communication focus on brain health, cognition, and neuropsychological testing.

He received his PhD in personality and measurement psychology at Western University in 2010 and has been exploring the intersection of science and technology ever since.

For more information about Creyos Health visit 

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Opinion: How robots could help to ease the social care crisis



Robear is an animatronic bear that lifts elders with mobility problems, Paro is a fuzzy robotic seal intended to provide a futuristic form of animal therapy, and Pepper is a humanoid with remote-monitoring capabilities and learning tools.

Pet robots have revolutionised and enhanced the standard of care, improving the wellbeing of care home residents. Here Stephen Hayes, managing director at automation tech firm Beckhoff UK, explores the benefits of integrating robots in social care homes.

There is a wide variety of care robots on the market. Some are aimed at physical care, including machines that can assist with mobility and exercise, feed their owner and help them with hygiene tasks. These could greatly benefit caregivers, freeing their time and preventing them from suffering from long-term health conditions or disability due to the physical effort associated with giving care.

Others play the role of a companion, engaging older people emotionally to reduce and even prevent cognitive decline, providing companionship for lonely older people, and making those with cognitive conditions easier for care staff to manage.

Research by the University of Plymouth, conducted in care homes using these pets found decreased neuropsychiatric symptoms such as delusions, depression, anxiety, apathy and occupational disruptiveness because they provided a sense of responsibility and purpose.

Social care vacancies are higher than before the COVID-19 pandemic, and data shows one in ten social care posts are unfilled in a staffing crisis that could have harmful results for residents. In England, 152,000 social care posts remain empty, according to a report released by Skills for Care.

Steve Barclay, Secretary of State for Health and Social Care commented for The Telegraph earlier this year, stating that robots and AI are key to better supporting patients and reducing demand on social care staff. He said that there was a need to adopt an innovative approach to health and attempt to cut NHS waiting times while improving care for the elderly.

However, these robots present limitations, such as superficiality and lack of personalisation. Also, the content of their conversations can be very limited, making them less entertaining with the pass of time.

This was the case of the humanoid Pepper, for which production ceased in 2021 due to a weak demand as care homes did not see the long-term benefit of his interactions. Nevertheless, robots like Paro, which move and respond to touch, have had a positive impact on the wellbeing of care home residents. However, with a cost of £5,000, care homes are looking for more affordable options.

Japan is a pioneer in developing this kind of technology. The nation is facing a ‘greying’ crisis due to the aging of its population, so the country has invested heavily in developing caretech able to serve and provide emotional support.

In the UK, there are currently almost 12 million people who are aged 65 or over, and the number of people coping with illnesses such as arthritis or dementia is expected to increase. In fact, a recent machine learning study by the Journal of Medical Systems suggests that 135 million people might be affected by dementia by 2050.

To allow for continuous innovation in this field, the UK Government announced its commitment to invest at least 2.4 per cent of GDP in R&D by 2027. The programme supports the UK Government’s Ageing Society Grand Challenge and Future of Mobility Grand Challenge, which will ensure we meet the needs of an ageing society.

With this in mind, there are certain technologies that we are likely to see more of in care homes over the coming years, including robots that can connect to each other and other devices.

This includes devices such as oximeters, thermometers, or even thermal cameras, enabling the elderly to have consultations any time of the day, from home, and send out emergency notices to staff or hospitals. However, for care robots to be a success, state-of-the-art control technology is required.

Beckhoff’s Ethernet-based fieldbus system, EtherCAT, has extension modules that are compatible with third party hardware for integration. This platform process data and transports it directly, has a flexible topology and simple line or tree structure that requires no expensive infrastructure components and includes the environments for programming, diagnostics and configuration.

This global standard for real-time Ethernet communication provides workers with real-time information about elders like location, health condition, or learning progress. This data also allows carers focus their time on other urgent tasks, optimise resources and personalise treatment.

The advancement and implementation of robot pets could improve awareness of preventative care, reduce anxiety on disease and enhance stakeholder relationship.

Further research on caretech would tackle functional problems, making these devices an essential asset for any caregiver. By investing in the right control technology now, social care homes will be better prepared to take care of their residents.

Beckhoff provides PC-based control and EtherCAT to connect caretech systems. See more here.

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How AI speech analysis could predict dementia onset



AI could be used to predict whether a person will develop Alzheimer’s-associated dementia by simply analysing their speech, scientists believe.

Researchers at Boston University are exploring how analysis of speech patterns via a machine learning model could detect with a high degree of accuracy whether someone with mild cognitive impairment will develop Alzheimer’s-associated dementia within six years

They say their model can predict, with an accuracy rate of 78.5 percent, whether someone with mild cognitive impairment is likely to remain stable over the next six years—or fall into the dementia associated with Alzheimer’s disease.

While allowing clinicians to make earlier diagnoses, the researchers say their work could also help make cognitive impairment screening more accessible by automating parts of the process;  with no expensive lab tests, imaging exams, or office visits required.

Ioannis (Yannis) Paschalidis, director of the Boston University Rafik B. Hariri Institute for Computing and Computational Science & Engineering, says: “We wanted to predict what would happen in the next six years—and we found we can reasonably make that prediction with relatively good confidence and accuracy.

“We hope, as everyone does, that there will be more and more Alzheimer’s treatments made available.

“If you can predict what will happen, you have more of an opportunity and time window to intervene with drugs, and at least try to maintain the stability of the condition and prevent the transition to more severe forms of dementia.”

The project involves a multidisciplinary team of engineers, neurobiologists, and computer and data scientists.

“We hope, as everyone does, that there will be more and more Alzheimer’s treatments made available,” says Paschalidis.

“If you can predict what will happen, you have more of an opportunity and time window to intervene with drugs, and at least try to maintain the stability of the condition and prevent the transition to more severe forms of dementia.”

To train and build their new model, the researchers turned to data from one of the oldest and longest-running studies in the US —the BU-led Framingham Heart Study.

Although the Framingham study is focused on cardiovascular health, participants showing signs of cognitive decline undergo regular neuropsychological tests and interviews, producing a wealth of longitudinal information on their cognitive well-being.

Paschalidis and his colleagues were given audio recordings of 166 initial interviews with people, between ages 63 and 97, diagnosed with mild cognitive impairment—76 who would remain stable for the next six years and 90 whose cognitive function would progressively decline.

They then used a combination of speech recognition tools—similar to the programs powering your smart speaker—and machine learning to train a model to spot connections between speech, demographics, diagnosis, and disease progression.

After training it on a subset of the study population, they tested its predictive prowess on the rest of the participants.

“We combine the information we extract from the audio recordings with some very basic demographics—age, gender, and so on—and we get the final score,” says Paschalidis. “You can think of the score as the likelihood, the probability, that someone will remain stable or transition to dementia. It had significant predictive ability.”

Rather than using acoustic features of speech, like enunciation or speed, the model is just pulling from the content of the interview—the words spoken, how they’re structured.

And Paschalidis says the information they put into the machine learning program is rough around the edges: the recordings, for example, are messy—low-quality and filled with background noise.

“It’s a very casual recording,” he says. “And still, with this dirty data, the model is able to make something out of it.”

That’s important, because the project was partly about testing AI’s ability to make the process of dementia diagnosis more efficient and automated, with little human involvement.

In the future, the researchers say, models like theirs could be used to bring care to patients who aren’t near medical centers or to provide routine monitoring through interaction with an at-home app, drastically increasing the number of people who get screened.

According to Alzheimer’s Disease International, the majority of people with dementia worldwide never receive a formal diagnosis, leaving them shut off from treatment and care.

Rhoda Au, a coauthor on the paper, says AI has the power to create “equal opportunity science and healthcare.”

The study builds on the same team’s previous work, where they found AI could accurately detect cognitive impairment using voice recordings.

In future research, Paschalidis would like to explore using data not just from formal clinician-patient interviews—with their scripted questions and predictable back-and-forth—but also from more natural, everyday conversations.

He’s already looking ahead to a project on if AI can help diagnose dementia via a smartphone app, as well as expanding the current study beyond speech analysis—the Framingham tests also include patient drawings and data on daily life patterns—to boost the model’s predictive accuracy.

“Digital is the new blood,” says Au. “You can collect it, analyse it for what is known today, store it, and reanalyse it for whatever new emerges tomorrow.”

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