HealthTech Tag

Telemedicine in Nursing Homes Did Not Cut Hospital Admissions, German Trial Finds

 Key Takeaways: 

• A large cluster-randomised trial across 24 nursing homes in western Germany found no statistically significant reduction in hospital admissions or total days spent in care following an intersectoral, telemedicine-based intervention.
  
• Researchers tested 1,260 separate model specifications; around two-thirds pointed to a numerical trend towards fewer admissions, but none reached statistical significance (P < .05).
  
• The authors attribute the null result partly to external shocks, staffing pressures, limited GP engagement and short adaptation windows, and recommend narrower outcome measures and simpler, automated technology in future.
  
  
  

What the study set out to test

A prospective, multicentre cluster-randomised trial has concluded that introducing an intersectoral, telemedicine-based model of care into nursing homes did not produce a statistically significant reduction in hospital admissions among residents.

The research was led by John Grosser, Sophie Pauge, Birthe Aufenberg and Prof. Dr. Wolfgang Greiner of the Department of Health Economics and Health Care Management at Bielefeld University. They worked alongside Miriam Hertwig, Dr. med. Jenny Unterkofler, Dr. med. Christian Hübel and Prof. Dr. med. Jörg Christian Brokmann from the Department for Acute and Emergency Medicine at University Hospital RWTH Aachen, in collaboration with Dr. med. David Brücken of Rhine-Meuse Hospital Würselen and the Optimal@NRW Research Group.

The findings, published in JMIR Aging, evaluate the Optimal@NRW project as it was rolled out across 24 nursing homes in western Germany between May 2021 and April 2023.

The problem the intervention was designed to solve

Optimal@NRW was built around two well-documented pressures in long-term care: non-emergency hospital admissions that could plausibly have been managed in place, and persistent resource shortages across the sector. Transfers to hospital are disruptive for people living in nursing homes, costly for the wider system, and in a meaningful proportion of cases potentially avoidable if clinical assessment and decision support can be brought to the resident rather than the resident to the hospital.

Inside the model of care

The intervention combined four components intended to work as a single, joined-up pathway:

• A 24/7 telemedical consultation centre, giving nursing staff round-the-clock access to remote clinical input.
  
• Mobile non-physician medical assistants, able to attend the home and carry out assessments in person.
  
• A virtual emergency hub, coordinating escalation decisions across settings.
  
• A software-based early warning system for vital signs, designed to flag deterioration before it became acute.

Crucially, the model was intersectoral by design: it was meant to link the nursing home, emergency medicine and primary care rather than sit within any one of them.

A deliberately exhaustive statistical approach

Rather than relying on a single headline model, the research team used specification curve analysis to test how robust any effect was to analytical choices. They evaluated 1,260 distinct mixed-effects regression model specifications, drawing on both primary data collected during the trial and statutory health insurance claims data, with hospitalisation rates and total days spent in care as the outcomes of interest.

The result was consistent across that curve. Roughly two-thirds of the model variations indicated a numerical trend towards reduced hospital admissions, but not one of the 1,260 specifications demonstrated a statistically significant intervention effect at the conventional threshold (P < .05). In other words, the direction of travel was mildly encouraging, but the evidence did not support a claim that the intervention worked.

Why the intervention may not have delivered

The authors are candid that several practical and contextual factors are likely to have compromised the intervention’s chances of success.

External disruptions. The trial period coincided with severe regional flooding in western Germany and with ongoing COVID-19 pandemic restrictions. Both disrupted day-to-day operations in participating facilities and, importantly, distorted baseline hospitalisation rates against which any effect would have been measured.

Staff workload and usability. The model asked a great deal of nursing teams already contending with severe labour shortages. Complex, multi-component tasks – daily manual recording of vital signs among them – added meaningful operational stress rather than relieving it. A digital system that increases the documentation burden on staff is, in practice, competing with the very work it is meant to support.

Physician integration. Engaging general practitioners directly within the telemedical framework proved difficult. Because GP involvement was central to the intersectoral logic of the intervention, that gap limited how far the programme could genuinely connect primary care with the nursing home and the emergency pathway.

Short adaptation windows. Intervention phases ran for between 6 and 15 months per group. The authors suggest this may simply have been too short for staff to embed unfamiliar digital workflows into routine practice, particularly given the competing pressures above.

What the researchers recommend next

The team’s conclusions are constructive rather than dismissive of telemedicine in long-term care. They recommend that future digital health interventions in this setting should:

• Target specific, avoidable admission metrics rather than overall hospitalisations, which are influenced by too many factors outside the intervention’s reach to serve as a sensitive outcome measure.
  
• Allow extended implementation phases, giving teams realistic time to adapt to new digital workflows.
  
• Favour simplified, automated solutions such as wearable monitoring devices, reducing the manual data entry burden on nursing staff.
  
• Pursue deeper structural integration with general practitioners, so that primary care is built into the pathway rather than invited to join it.
  
  
  

What this means for practice

The wider lesson here is one that recurs across digital health evaluation: the technology itself is rarely the binding constraint. Implementation conditions – staffing capacity, workflow design, clinical buy-in and the length of time teams are given to adapt – tend to determine whether a well-conceived tool produces measurable benefit. Practitioners who are asked to assess, adopt or lead on digital tools increasingly need a framework for judging fit and feasibility, not just functionality, which is the ground covered by CPD-accredited training such as CCH’s AI Essentials for Primary Care: Tools, Ethics and Everyday Applications.

For services considering remote monitoring or telemedical support in care homes, the Optimal@NRW findings are a useful corrective. They suggest that ambition should be matched by realism about what frontline teams can absorb, and that outcome measures should be chosen precisely enough to detect an effect if one exists.

CCH insight

Digital tools are arriving in primary and community care faster than most teams can evaluate them. AI Essentials for Primary Care: Tools, Ethics and Everyday Applications is a CPD-accredited short course covering how to appraise digital and AI-enabled tools, work within governance requirements and get reliable results in everyday practice. It carries 3.5 CPD points and counts towards appraisal and revalidation.

Explore AI Essentials for Primary Care →

Source: JMIR Aging

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Telehealth Strengthens Diabetes Self-Care but Delivers Only Modest Glycaemic Gains

Key Takeaways: 

• Comprehensive telehealth produced modest, non-significant improvements in glycaemic control compared with self-monitoring alone.
  
• Diabetes self-care was the only outcome to improve significantly, pointing to better self-management rather than better clinical numbers.
  
• Uptake was low, with relatively few participants completing the intended number of telehealth encounters.
  
  
  

Why self-management sits at the centre of type 2 diabetes care

People living with type 2 diabetes carry much of the day-to-day work of managing their condition. Self-monitoring, medication adherence, lifestyle modification and psychosocial coping all fall largely to the individual, supported at intervals by their clinical team. Comprehensive telehealth has been proposed as a way of closing the gaps between those intervals – enabling more regular contact with health care providers, structured review of patient-generated data, and input from a multidisciplinary team without requiring people to attend in person.

What has remained uncertain is whether that model works in a fee-for-service setting, where reimbursement structures and service design differ markedly from the integrated systems in which much telehealth research has been conducted. A randomised trial published in Annals of Internal Medicine on 23 June 2026 set out to answer that question.

What the trial set out to test

The study, led by Crowley and colleagues, evaluated both the implementation and the effectiveness of a comprehensive telehealth intervention for people with uncontrolled type 2 diabetes and comorbid hypertension, delivered in a fee-for-service context. Participants were randomised either to the comprehensive telehealth programme or to self-monitoring alone, with the comparison designed to isolate the added value of regular provider contact and multidisciplinary review over and above the data-gathering that people were already doing themselves.

Outcomes spanned both the clinical and the behavioural: glycaemic control, blood pressure, weight, diabetes self-care, disease-related distress and self-efficacy, alongside safety monitoring for serious adverse events.

Uptake proved to be the sticking point

One of the clearest findings was not about physiology at all. Uptake of the intervention was limited, with relatively few participants completing the intended number of telehealth encounters. That matters for interpreting everything that follows: a programme that people do not fully engage with is being tested at less than full strength.

Engagement also appeared to shape the results. Improvements in glycaemic control were more evident among participants with greater engagement, suggesting a dose-response relationship that the trial as a whole was not positioned to demonstrate conclusively.

Modest clinical gains that did not reach significance

Compared with self-monitoring alone, comprehensive telehealth was associated with modest improvements in glycaemic control. Those improvements did not reach statistical significance.

The same pattern held across the other clinical measures. The telehealth group showed trends towards better blood pressure and weight outcomes, as well as towards reduced disease-related distress and improved self-efficacy – but again, these differences were not statistically significant. The direction of travel was consistent and favourable; the magnitude simply was not large enough to distinguish the intervention from the comparator.

Self-care was the one clear winner

The exception was diabetes self-care, which was the only outcome that improved significantly with telehealth. It is a result worth dwelling on. It suggests the intervention may strengthen people’s ability to manage their own condition even where clinical measures remain largely unchanged – a benefit that conventional endpoints are poorly designed to capture, and one that may accrue over a longer horizon than the trial allowed.

Whether that improved self-management eventually translates into better glycaemic, blood pressure or weight outcomes is a question the study cannot answer. What it does indicate is that the mechanism telehealth is meant to activate – supported, informed, confident self-management – did in fact activate.

That mechanism depends heavily on the quality of the conversation, not just its frequency. Practitioners looking to strengthen engagement and draw out patients’ own motivation to change are increasingly turning to structured behaviour change training; CCH’s two-hour CPD short course, Behaviour Change Skills: Enhancing Motivation, covers motivational interviewing techniques for exploring readiness, working with ambivalence and recognising change talk in everyday consultations.

Safety and adverse events

Serious adverse events were uncommon and occurred at similar rates in both groups, supporting the safety of the intervention. For a delivery model that reduces face-to-face contact, that reassurance carries weight.

Where the findings stop short

The authors are candid about generalisability. The study population was predominantly low-income with lower educational attainment, and baseline glycaemic control was relatively favourable – leaving less room for improvement than a more poorly controlled cohort would have offered. Digital literacy was not assessed at all, despite being an obvious determinant of who can engage with a telehealth programme and who cannot.

Each of these factors constrains how far the results can be extrapolated to other populations or other service settings.

What this means for practice

The overall picture is one of a promising model that did not, in this setting, demonstrate a clear clinical advantage over self-monitoring alone. Comprehensive telehealth appears capable of enhancing self-management and patient engagement. It did not, here, translate that into measurable clinical benefit.

Further research is needed to establish whether greater uptake of the intervention, or a different patient population, would yield larger benefits. In the meantime, the trial offers a practical reminder for anyone commissioning or delivering remote diabetes care: the technology is only as effective as the engagement it manages to sustain.

CCH insight

This trial found that telehealth improved self-care significantly while clinical measures barely moved – and that engagement was the limiting factor throughout. Motivational interviewing offers an evidence-based way to shift that dynamic, helping people find their own reasons to change rather than pushing against resistance.

Behaviour Change Skills: Enhancing Motivation is a two-hour online CPD course from the College of Contemporary Health, developed with behaviour change specialists at BCT and led by registered dietitians Dympna Pearson and Sam Howard. It carries 2 CPD hours and a Certificate of Completion, and costs £59.

Explore the course →

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AI Is Reshaping How Patients Make Healthcare Decisions, Survey Finds

Key Takeaways:

• A majority of US patients now research their symptoms before deciding whether to book an appointment, with AI and search engines central to that process.
  
• Researchers frame the trend as a global one, driven less by the availability of technology than by mounting frustration with slow, fragmented care.
  
• The report urges providers to redesign care pathways and deploy their own AI tools to extend capacity and keep patients engaged.
  
  
  

Patients are researching before they reach the clinic

Patients are increasingly taking charge of their own healthcare with artificial intelligence and other digital tools, with 58% of US patients researching their symptoms before deciding whether to book an appointment, according to a new report from the ZS Impact Institute.

The report drew on insights from nearly 10,000 healthcare consumers and providers across the US, Germany and China. Researchers note that the shift is consistent across healthcare systems – a pattern they say points to a “broader breakdown in how patients seek and experience care globally.”

Search engines and AI have become first ports of call

The report found that self-directed research is now routine, and that people who turn to online tools tend to rate them highly. Thirty-seven percent of US respondents report using search engines for health information, 94% of whom find it helpful. Eighteen percent report using AI, and 89% of those describe it as helpful.

The behaviour extends well beyond pre-appointment research. Fifty-two percent of US patients report requesting specific medications, and 68% of providers report seeing an increase in patients requesting therapies by name.

For clinicians, that means fielding more informed – and increasingly AI-shaped – questions in the consulting room. Continuing professional development providers have begun to respond to the trend: the College of Contemporary Health, for example, offers a short CPD course, AI Essentials for GPs, developed to help doctors evaluate AI tools critically and respond with confidence when patients raise them.

Frustration, not just availability, is driving the shift

The report is explicit that the move towards AI “cannot be attributed to availability alone.” Instead, it reflects “growing consumer frustration with healthcare” – a frustration the survey data lays out in some detail.

Forty-five percent of US patients avoid seeing a doctor until they are already unwell, and 41% have not had a health check-up in three or more years. Thirty-six percent wait a year or more for a diagnosis. Once treatment begins, adherence falters: 29% do not take treatments as prescribed, and 54% stop treatments prematurely.

Access is part of the picture too. Thirty-eight percent of respondents report lacking a primary care doctor, and 30% say that diagnostic delays led to a worsening of their medical condition.

What the report recommends

Researchers set out a range of recommendations for leaders across pharma, payers, medtech and providers. For providers in particular, the report suggests redesigning care pathways and using AI to extend both capacity and continuity of care.

Jon Roffman, ZS principal and lead contributor, framed the challenge as one of a system struggling to keep pace with the people it serves.

“Patients are changing faster than the system designed to serve them,” Roffman said in a statement. “AI has put medical knowledge directly in patients’ hands, but the healthcare system still assumes patients will come to it first. That model has changed. Patients are more informed and empowered. To keep them engaged, the system must reduce friction with its own set of AI and technology tools to deliver more connected, supportive care.”

CCH insight

Patients are already walking into consultations with AI-shaped questions and specific therapies in mind. AI Essentials for GPs: Tools, Ethics and Everyday Applications is a three-hour, CPD-accredited short course – created by Prof. Mike Bewick and Dr Dipesh Naik – that gives doctors the frameworks to evaluate AI tools safely, meet NHS governance standards and know exactly what to say when a patient asks. It carries 3.5 CPD hours towards appraisal and revalidation.

Explore AI Essentials for GPs →

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When the Diagnosis Arrives by App: Why Most Patients Still Want a Human Voice

Key Takeaways: 

• In a UT Southwestern survey of more than 2,400 people diagnosed with cancer, 75% said they would prefer to receive the news directly from their physician – in person or by telemedicine – rather than through an electronic patient portal.
  
• More than half of those who first learned of their diagnosis via the portal were alone at the time, often without support from a clinician or family member.
  
• Researchers are calling for a more personalised approach, including better portal notification settings, tiered or delayed release of sensitive findings, and plain-language summaries of radiology and pathology reports.
  
  
  

A digital convenience with an emotional cost

Electronic patient portals have transformed how quickly people can see their own test results. For routine bloodwork or a clear scan, near-instant access is a welcome convenience. But the same speed raises a difficult question for clinicians: how should a new cancer diagnosis be communicated when a patient can open the result on a phone before anyone has had the chance to talk it through?

That tension has grown sharper since 2021, when a provision of the 21st Century Cures Act came into force in the United States. The regulation requires that patients have timely, unrestricted access to their electronic health information – which, in practice, means a growing number of people are discovering a new or recurrent cancer diagnosis through their portal, sometimes with no clinician present to interpret it or answer the questions that immediately follow.

What the UT Southwestern survey found

A new survey carried out at UT Southwestern Medical Center suggests that, for most people facing a cancer diagnosis, faster is not better. The findings, published in JAMA Network Open, show that 75% of respondents would prefer to learn about a cancer diagnosis directly from their physician, whether in person or through a telemedicine appointment.

The 2025 survey gathered responses from more than 2,400 people who were diagnosed with cancer at the Harold C. Simmons Comprehensive Cancer Center between 2019 and 2023, giving the researchers a substantial real-world picture of how patients want sensitive results delivered.

According to the study’s lead author, Sheena Bhalla, M.D., Assistant Professor of Internal Medicine in the Division of Hematology and Oncology and a medical oncologist at the Simmons Cancer Center, the broad enthusiasm for digital access does not extend neatly to oncology. “While most patients in the general population appreciate rapid electronic access to test results, the situation for patients with cancer is much more nuanced,” she said. “Learning about a cancer diagnosis without the ability to immediately ask questions or discuss next steps with a trusted clinician can add to the significant stress, uncertainty, and fear that patients experience.”

Preferences are not one-size-fits-all

The survey also revealed that there is no single right way to share a result. Preferences varied according to people’s prior experiences, how frequently they used their portal, and their demographic characteristics. Men, for instance, were more likely than women to prefer learning of a diagnosis through the portal.

For senior author David Gerber, M.D., Professor of Internal Medicine in the Division of Hematology and Oncology and of Epidemiology in the Peter O’Donnell Jr. School of Public Health, and co-Director of the Simmons Cancer Center Office of Education and Training, that variation is precisely the point. “These findings highlight the need for a more personalized, tailored approach to communicating sensitive and life-changing results,” he said. “Moving beyond a one-size-fits-all approach can help clinicians provide a more thoughtful, compassionate patient experience.”

The hidden consequence: facing the news alone

Perhaps the most striking insight concerns the circumstances in which people are receiving these results. Among those who learned of their diagnosis through the portal, more than half reported that they were alone when they read it.

Dr Bhalla described this as one of the most troubling side effects of real-time access. “That’s one of the most unintended consequences of real-time access,” she said. “Patients are often alone without support from their physician or family at one of their most vulnerable moments.”

Possible solutions for clinicians and health systems

The researchers are clear that the answer is not to roll back access, but to design around it more thoughtfully. They point to several potential measures, including raising awareness among both clinicians and patients of the portal notification settings already available; developing tiered or delayed-release approaches for particularly sensitive findings; and integrating supportive digital tools such as plain-language summaries for radiology and pathology reports.

Policy is beginning to catch up. Since the Cures Act took effect, three states – including Texas – have enacted laws permitting the delayed portal release of cancer-related and other sensitive results, giving care teams a window to reach out before a patient is left to interpret difficult news on their own.

Looking ahead

For the study’s authors, the work is a starting point rather than a conclusion. “Further study and increased interdisciplinary collaboration among oncology clinicians, health services researchers, and digital health experts can help us better understand how patients receive and react to cancer diagnoses,” Dr Bhalla said. “Our goal is to increase awareness of this issue and help drive innovative approaches to patient-centered communication.”

Source: UT Southwestern Medical Center

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Half a Million NHS Staff to Receive AI Tools to Free Up More Time for Patients

Key Takeaways: 

• NHS England is rolling out Microsoft 365 Copilot to 505,000 clinicians and support staff, with the full rollout expected to be complete by October 2026.
  
• The world’s largest healthcare AI trial of its kind found that the tool could save staff an average of 43 minutes a day – the equivalent of around five weeks a year, or roughly two days of administrative time every month.
  
• Copilot is expected to support a wide range of roles, including clinical administration, ward clerks, medical secretaries, core services such as HR and finance, and management.
  
  
  

A major step forward in NHS AI adoption 

More than half a million NHS staff are being given access to new artificial intelligence (AI) tools that could free up an average of two days every month from administrative duties, creating more time for the work that matters most to patients and staff.

NHS England announced today that it is significantly accelerating the adoption of AI across healthcare services by providing 505,000 clinicians and support staff with access to Microsoft 365 Copilot.

The AI personal assistant is designed to help clinicians draft documents and analyse data more efficiently, so they can devote more of their time to caring for patients.

What the world’s largest healthcare AI trial found

The agreement follows the largest AI trial of its kind anywhere in the world in healthcare, which gave more than 30,000 NHS workers across 90 NHS organisations access to Microsoft 365 Copilot.

The trial found that AI-powered administrative support could save an average of 43 minutes per staff member each day, or more – the equivalent of five weeks of time per person every year.

Results from the trial indicated that a full rollout of Microsoft 365 Copilot could save millions of hours of staff time per month.

NHS England and government leaders welcome the rollout 

Rob Thompson, Chief Digital, Data and Technology Officer at NHS England, said: “The NHS wants to embrace cutting-edge technology and this Microsoft partnership will mean staff can be freed from admin so they can focus more of their time on what matters most – improving care for patients.

“Innovations like this will help drive NHS productivity so patients can get the treatment they need sooner and there is better value for taxpayers.

“The potential to save NHS staff around 2 days of admin time every month could be a gamechanger for patients.

“As part of our 10 Year Health Plan, we’re making sure every pound is spent on cutting waiting times and boosting care”.

Health Innovation and Safety Minister Preet Kaur Gill said: “Technology should support our NHS staff, not slow them down.

“Every day, doctors, nurses and other healthcare professionals spend valuable time on administrative tasks that take them away from patients. By rolling out Microsoft Copilot across the NHS, we can reduce that burden, free up clinicians’ time and help staff focus on what they do best caring for patients.

“This government is putting innovation to work for patients: helping staff work more efficiently, improving productivity and supporting a modern NHS that delivers better care, faster access to treatment and better value for taxpayers”.

Darren Hardman, CEO, Microsoft UK and Ireland, said: “By rolling out Microsoft 365 Copilot at scale, NHS teams can cut through everyday admin and spend more time where it matters most.

“Bringing AI safely into the flow of healthcare will help ease pressures, improve productivity and support better decision-making across the health service.

“We’re proud to work with NHS England to help tackle some of its biggest challenges and accelerate digital transformation for the benefit of staff and patients alike”.

How Copilot will be used across the health service

Copilot is designed to help users create, analyse and complete work more quickly. NHS England anticipates that it will be harnessed in a variety of ways across all aspects of the healthcare service, including:

• Clinical administration: supporting clinicians in drafting letters and in registrar training.
• Ward clerks: assisting with patient discharge processes, service data analysis, rota building and bed management.
• Medical secretaries: helping to draft patient letters and meeting minutes, and creating templates to maintain consistency.
• Core services: supporting human resources, finance and procurement functions.
• Management: assisting with drafting board papers, briefings and organisational analysis.
  
  
  

Licensing and timeline for the rollout

Each NHS trust will receive a central allocation of licences based on its organisational headcount, typically starting at around 2,000 Microsoft 365 Copilot licences.

The rollout to more than 500,000 staff across the NHS is expected to be complete by October 2026.

Source: NHS England

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Mobile App May Help Prevent Excess Weight Gain in Pregnancy

Key Takeaways:

• A digital intervention combining self-monitoring and personalised feedback helped reduce excess weight gain during pregnancy among women with overweight or obesity
  
• Participants using the programme gained weight more slowly and were less likely to exceed recommended guidelines compared with standard care
  
• Greater engagement, particularly consistent self-weighing, was linked to better outcomes
  
  
  

Addressing a common challenge in pregnancy

Weight gain during pregnancy is both expected and necessary. However, gaining more than recommended levels is associated with increased health risks for both the pregnant person and the baby. These risks are particularly pronounced among individuals who are already living with overweight or obesity at the start of pregnancy.

Excess gestational weight gain has been linked to complications such as gestational diabetes and preeclampsia. It also increases the likelihood of pre-term birth and larger birthweight, both of which can complicate delivery and raise the child’s long-term risk of obesity.

In the United States, around half of pregnant individuals with overweight or obesity exceed national guidelines for weight gain, highlighting a persistent challenge in routine prenatal care.

The LEAP programme – a technology-enabled approach

To address this issue, researchers at Kaiser Permanente developed the Lifestyle, Eating, and Activity in Pregnancy (LEAP) programme. The intervention was designed to provide scalable, technology-driven support integrated into everyday clinical care.

The study, published in JAMA Network Open on 20 April, evaluated whether this digital approach could help individuals maintain healthier weight gain trajectories during pregnancy.

“Our results are especially exciting because we intentionally designed the LEAP program to be feasible for widespread implementation,” said first author Monique Hedderson, PhD, principal investigator with the Kaiser Permanente Division of Research (DOR). “This study represents the culmination of years of research on what works well for this high-risk population and how to leverage technology to make those benefits achievable across a health system.”

How the intervention worked

Participants in the LEAP group were provided with a digital scale for home use and a wearable activity tracker, either a Fitbit or their own Apple Watch. These devices were connected to a mobile application that delivered automated, personalised feedback on both weight and physical activity.

The programme also included weekly educational sessions delivered through the app, focusing on realistic and achievable goals related to diet and exercise. In parallel, clinicians received guidance on how to discuss gestational weight gain with patients more effectively.

A key feature of the programme was its adaptive design. Individuals whose weight gain began to accelerate received targeted support, including personalised chat messages from a lifestyle coach. If necessary, this support escalated to telephone consultations.

“About half of patients in the LEAP intervention group managed weight well on their own, but for those inching towards the upper limit of recommendations, LEAP provided extra, targeted support,” said Hedderson. “This conserves resources for patients who need help the most.”

Study design and participant outcomes

The effectiveness of the LEAP programme was assessed across 58 clinicians within Kaiser Permanente Northern California. All pregnant patients with overweight or obesity under their care were invited to participate.

Clinicians were randomly assigned to one of two groups:

• Standard care plus the LEAP intervention
• Standard care alone

In total, 1,256 participants were included in the study. Of these, 677 received the LEAP programme, while 588 received standard care.

Standard care involved providing patients with a newsletter at their first prenatal visit outlining healthy weight gain targets and lifestyle advice. At around 20 weeks, clinicians reviewed weight gain against recommended guidelines.

The results showed clear differences between the groups. Participants in the LEAP programme experienced:

• Lower weekly rates of weight gain
• Lower total weight gain during pregnancy
• Reduced likelihood of exceeding recommended guidelines

In addition, fewer individuals in the LEAP group gave birth to infants classified as large for gestational age.

“This study demonstrates that combining wireless self-monitoring tools with an adaptive digital intervention can make a meaningful difference for patients with overweight or obesity at risk of excess weight gain during pregnancy,” said Kari Carlson, MD, director of women’s health for The Permanente Medical Group. “Importantly, it was tested in routine care, making the findings highly relevant for health systems looking for practical solutions.”

Engagement matters

Although all participants assigned to the LEAP group were included in the analysis, only around half actively engaged with the programme. This reflects real-world conditions, where not all individuals fully participate in digital health interventions.

However, among those who did engage, outcomes were notably stronger. Higher levels of interaction with the programme, particularly consistent self-weighing, were associated with lower overall weight gain.

“When we zoomed in on participants who actually engaged, we found that the more they engaged, especially by weighing themselves consistently, the less weight they gained,” Hedderson said.

Participants could view their weight trends in relation to Institute of Medicine guidelines within the app, a feature that appeared to play a particularly important role in supporting behaviour change.

“This seems to be a particularly effective component of the program,” she said.

Implications for clinical practice

The findings suggest that digital, scalable interventions such as LEAP could offer a practical solution to a longstanding challenge in prenatal care. By integrating self-monitoring tools, automated feedback, and targeted coaching into routine workflows, the programme demonstrates how technology can support both patients and clinicians without increasing burden.

“Excess gestational weight gain among these high-risk patients is a common and challenging issue in routine prenatal care,” said senior author Assiamira Ferrara, MD, PhD, a DOR senior research scientist. “What’s compelling about the LEAP study is that it shows we can support healthier weight gain using scalable, technology-enabled tools that fit within real-world clinical workflows, without adding burden for patients or clinicians.”

Looking ahead

The research team continues to follow participants to assess longer-term outcomes for both parents and children. There is optimism that the LEAP programme could be integrated into routine care in the near future.

“Bottom line, using technology to help patients manage their own health can be effective, and patients tend to like it, too,” Hedderson said.

Funding and research team

The study was funded by the National Institutes of Health.

Additional co-authors included Susan D. Brown, PhD; Charles P. Quesenberry, PhD; Fei Xu, MS; Emily Liu, MPH; Karen L. Li, MPH; Sneha B. Sridhar, MPH; Tali Sedgwick, RDN; Page Kissel, BA; and Hillary D. Serrato Bandera, BA, all from the Division of Research. Mibhali M. Bhalala, MD, contributed from Kaiser Permanente Northern California, and Cheryl Albright, PhD, MPH, contributed from the University of Hawaii at Manoa School of Nursing and Dental Hygiene.

About the Kaiser Permanente Division of Research

The Kaiser Permanente Division of Research conducts epidemiological and health services research aimed at improving health outcomes and healthcare delivery. With more than 720 staff and approximately 630 active research projects, the division focuses on understanding the determinants of illness and advancing the quality and cost-effectiveness of care.

Source: Kaiser Permanente Division of Research

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Growing Use of Emojis in Electronic Health Records Raises Safety Questions

Key Takeaways:

• Emoji use in electronic health records has increased steadily between 2020 and 2025, appearing in thousands of clinical notes.
  
• While emojis may offer a quicker, more expressive way to communicate, they introduce risks of misinterpretation across clinicians and patients.
  
• Experts suggest that clearer governance and guidance may be needed to ensure safe and consistent use in clinical documentation.
  
  
  

Study reveals growing presence of emojis in clinical records

A recent study from Michigan Medicine, published on 14 January, examined 218.1 million clinical notes from the electronic health records of 1.6 million people receiving care. The findings revealed a notable rise in emoji usage by both healthcare professionals and patients between January 2020 and September 2025.

Across all records analysed, researchers identified 372 unique emojis appearing in 4,162 notes. While this represents a small proportion of total documentation, the upward trend signals a shift in how digital communication is entering clinical environments.

David Hanauer, clinical associate professor of paediatrics and learning health science at Michigan Medicine, explained that the study was initially driven by simple curiosity.

“It was mostly out of interest, just trying to explore if anything was there at all,” Hanauer said. “Our understanding had been that emojis and other symbols are actually not supposed to be used in a medical record, so we were wondering: Were there any there at all, and how often were they being used, and which ones?”

Concerns around clarity and misinterpretation

Despite their increasing use, emojis raise important concerns about clarity in clinical communication. Hanauer highlighted that the ambiguity of many emojis could lead to misunderstanding.

“Most of the concerns that people have is that it’s hard to understand from an emoji what is being conveyed,” Hanauer said. “Maybe a smiley face is pretty obvious to most people, but there’s a lot of different faces with nuances and other symbols. I think there can be a lot of miscommunication, misinterpretation.”

The issue becomes more complex when considering variation in interpretation across different groups of people receiving care and healthcare professionals.

Kim Ford, a health information business systems analyst lead at Michigan Medicine, emphasised how generational differences may influence understanding.

“If you have older patients who may not be familiar with emojis, it’s almost like a foreign language to them,” Ford said. “(For) our younger generation – or those people that have grown up with technology – it’s a second language for them that they understand very well. That’s my biggest concern.”

Accessibility challenges for some people receiving care

Beyond interpretation, accessibility presents another potential barrier. Hanauer noted that small visual symbols may be difficult for some individuals to distinguish clearly, particularly those with visual impairments.

“For older people, having small emojis might actually be hard for them to see and make out, so they might see its face but they can’t tell what the specific expression is,” Hanauer said. “I think we found over 300 different kinds of emojis being used. That’s a lot of different symbols that people would have to understand what they mean.”

This highlights a broader concern that even seemingly simple visual cues may not be universally interpretable or accessible.

Potential implications for patient care

A key concern raised by the study is whether emoji misinterpretation could affect clinical outcomes. While there is currently no direct evidence linking emoji use to adverse outcomes, the possibility remains.

“We hope that doesn’t happen, but I think because of that concern, there’s probably going to be a little bit more oversight,” Hanauer said. “I don’t think we would easily be able to find a circumstance in which there was actually some sort of better or negative outcome from an emoji being misinterpreted.”

The absence of clear evidence does not eliminate the risk, particularly in high-stakes environments where precise communication is essential.

Balancing efficiency with professionalism

Some healthcare professionals recognise potential benefits in using emojis, particularly in reducing communication burden within electronic systems. However, concerns remain about maintaining professionalism and objectivity.

Leah Beel, a medical assistant at American Family Care in Ann Arbor, expressed reservations about their place in formal documentation.

“From my experience, EHRs are used to get quick information and try to communicate with each other in a fast and reliable way,” Beel said. “The only thing I would use is an exclamation point, which, even then, is kind of out there. It’s a good thing that emojis can show enthusiasm or certain reactions, but I also think to a degree – it’s not unprofessional but just someone might take it the wrong way. My perspective on EHR is that you write very objectively.”

In contrast, Elizabeth Rossmann Beel, a paediatric anaesthesiologist at Texas Children’s Hospital, noted that emojis may offer a more efficient way to communicate in certain contexts.

“It’s a way to react to something without putting as much effort into it, or into making that person who’s reading it feel like they need to reply,” Rossmann Beel said. “I think it can cut down a little bit on the burden of replying to and responding to messages in the EHR, which is nice. However, it’s definitely more casual, and so sometimes that’s not the best tone to be setting in a medical record.”

The case for governance and standardisation

Given the growing use of emojis, there is increasing interest in whether formal guidance or regulation should be introduced.

Ford suggested that healthcare organisations may need to consider structured governance around emoji use.

“Maybe emojis are an acceptable means of communication,” Ford said. “The other piece is, should there be a governance process around what emojis can be used? And in what situations? I need to think a little bit about what their structure might look like – what department should be involved in reviewing and approving those, what should be the process to submit an emoji for consideration for use? There’s a lot of pieces to the governance process that need to be figured out there.”

A shift in digital communication within healthcare

The findings from this study reflect a broader evolution in digital communication, where informal elements are beginning to intersect with traditionally formal systems such as electronic health records.

While emojis may offer efficiency and emotional nuance, their integration into clinical documentation raises important questions about clarity, accessibility, professionalism, and patient safety. As their use continues to grow, healthcare systems may need to balance flexibility with standardisation to ensure communication remains precise, inclusive, and clinically appropriate.

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Algorithm-Guided Insulin Dosing Improves Blood Sugar Control in Type 2 Diabetes

Key Takeaways:

• An algorithm paired with continuous glucose monitoring significantly increased time in target glucose range compared with standard self-monitoring approaches
  
• The tool provides personalised weekly insulin dose recommendations based on recent glucose data, helping to simplify titration
  
• Early findings suggest strong patient acceptability and potential to enhance diabetes management at scale, though larger trials are needed
  
  
  

A data-driven approach to insulin adjustment

A novel algorithm developed by researchers at the University of Virginia Center for Diabetes Technology has demonstrated encouraging results in supporting people living with Type 2 Diabetes to better manage their blood glucose levels.

The system works in combination with a continuous glucose monitor and provides tailored recommendations for insulin dose adjustments. Rather than relying solely on manual interpretation of glucose readings, the algorithm analyses patterns over time and offers structured, data-informed guidance.

In a clinical trial involving 30 participants, individuals were randomly assigned to one of two approaches over a 16-week period:

• Algorithm-guided insulin adjustment using continuous glucose monitoring data
  
• Traditional self-monitoring of blood glucose with independent dose adjustment
  

The results showed a marked improvement in glycaemic control among those using the algorithm. Participants in this group increased their average time spent within a safe blood glucose range from 54.1% to 75.3%. By contrast, those relying on self-monitoring alone saw a more modest increase from 50.2% to 55.3%.

Moving beyond traditional insulin management

The findings highlight the growing role of digital health tools in diabetes care. According to Marc D. Breton, the study’s lead author:

“These results clearly show that diabetes technology and advanced algorithms can be leveraged to great effects, well beyond the classical paradigm of automated insulin delivery. As continuous glucose monitoring and connected medical devices become ubiquitous, we have the opportunity to provide highly personalized advice and monitoring to people with diabetes and guide their use of insulin and medications. Showing the impact of these technologies in early insulin therapy (only one dose a day) opens the door to helping the vast majority of people using insulin, well beyond what we were able to achieve with automated insulin delivery.”

This perspective reflects a broader shift towards personalised, technology-enabled care. Rather than fully automated systems alone, there is increasing interest in decision-support tools that augment clinical judgement and patient self-management.

Addressing the challenges of insulin titration

For many people living with type 2 diabetes, treatment often begins with oral or non-insulin therapies. However, as the condition progresses, insulin may become necessary to maintain adequate glycaemic control.

Adjusting insulin doses – a process known as titration – can be complex and burdensome. It typically requires frequent monitoring, interpretation of glucose patterns, and iterative dose changes. Importantly, there is no universally standardised titration protocol, which can create variability in care and outcomes.

To address this, Anas El Fathi developed the algorithm with the aim of streamlining and improving this process. The system evaluates two weeks of continuous glucose monitoring data and generates weekly recommendations for insulin dose adjustments, offering a structured and personalised approach.

Strong acceptance and clinical potential

The study also explored how well the technology was received by participants. According to Ralf Nass:

“From a medical point of view, it was fascinating to see that the algorithm was not only better than the standardized insulin titration recommendations, but also how well the technology was accepted by the participants with type 2 diabetes. This type of technology has the potential to help physicians enable their patients to achieve better glycemic control faster by using a personalized approach.”

This combination of improved outcomes and user acceptability is particularly important, as adherence and engagement remain key challenges in long-term diabetes management.

Future directions – towards more personalised diabetes care

While the results are promising, the researchers emphasise that further validation is required. Larger and longer clinical trials will be needed to confirm the effectiveness of the algorithm across more diverse populations.

Looking ahead, the integration of more advanced data-driven approaches may further enhance personalisation. Breton noted:

“It is only the very beginning of these efforts. With early demonstration behind us, we can focus on robust approaches that will be effective with more varied populations. Integrating recently developed data-driven methodologies, especially digital twins, to further improve our capacity to tailor diabetes managements to individuals is likely to once more revolutionize diabetes care.”

Such developments could represent a significant step forward in precision medicine for people living with diabetes.

Study publication and funding

The findings have been published in the peer-reviewed journal Diabetes Technology & Therapeutics, with the article available as open access.

The research team included El Fathi, Nass, Carol J. Levy, Camilla Levister, Grenye O’Malley, Nirali A. Shah, Shaziah Hassan, Cheryl Quainoo, Chaitanya L.K. Koravi, Taylor N. Nguyen, Giulio Matteo Santini, Emma Emory, Carlene Alix, Dillon K. Flanagan, David Fulkerson, Mary Clancy Oliveri, Christian Laugesen, Jonas K. Lineolov, Peter W. Hansen and Breton.

The clinical trial was supported by a grant from Novo Nordisk.

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AI in Healthcare: Promise, Pitfalls, and the Risk of Misguided Medical Advice

Key Takeaways:

• People using AI for health advice often struggle to interpret and communicate symptoms effectively, leading to incorrect conclusions in many cases.
  
• Even when AI identifies a condition correctly, it may fail to recommend appropriate urgency, particularly in time-sensitive or complex scenarios.
  
• Clinicians see value in AI as a supportive tool, but stress that it should complement, not replace, professional medical care.
  
  
  

AI becomes a common source of health information

As technology companies continue to develop platforms tailored for healthcare consultation, artificial intelligence is becoming an increasingly influential part of how people make decisions about their health. According to OpenAI, more than 40 million people use ChatGPT each day to seek health-related information.

However, emerging research suggests that while these tools offer unprecedented access to medical knowledge, they may also mislead users in certain contexts.

Challenges in how people use AI for medical queries

One of the central issues identified by researchers is not only the capability of AI systems, but how individuals interact with them. Many people lack the knowledge required to communicate symptoms accurately or comprehensively.

A recent study published in Nature Medicine attempted to replicate real-world use of AI chatbots. Participants were given medical scenarios and asked to consult AI tools. The results highlighted notable limitations:

• Participants correctly identified the condition only about one-third of the time.
  
• Just 43% made the correct decision regarding next steps, such as whether to seek emergency care or remain at home.
  

“People don’t know what they are supposed to be telling the model,” says Andrew Bean, who studies AI systems at Oxford University and was one of the authors on this study.

Bean explains that effective use of AI often depends on precise wording. “Doctors are trained to ask you questions about symptoms you might not have realised you should have mentioned,” says Bean.

Small differences in input can lead to dangerous outcomes

The study demonstrated how subtle differences in language can significantly alter the advice provided by AI systems.

In one example, two individuals described the same clinical scenario slightly differently. One described experiencing “the worst headache I’ve ever had” and was advised to go to the emergency room immediately. The other, who did not include that specific phrasing, was advised to take aspirin and remain at home.

“Turns out this was actually a life-threatening condition,” says Bean.

This highlights a critical limitation: AI systems rely heavily on the information they are given, and may not prompt for missing but clinically important details in the way a trained clinician would.

When AI gets the diagnosis right but the advice wrong

Even when AI tools successfully identify a medical condition, they may still provide inappropriate guidance regarding urgency or next steps.

In a separate study, researchers evaluated how AI systems responded to a range of medical scenarios. They found that in 52% of emergency cases, the tools “under-triaged” – treating conditions as less serious than they actually were.

In one case, the AI failed to direct a hypothetical patient experiencing diabetic ketoacidosis and impending respiratory failure – both life-threatening conditions – to seek emergency care.

“When there was a textbook medical emergency, ChatGPT got it right,” said Girish Nadkarni, a doctor and AI researcher at Mount Sinai who is an author on the study. However, he noted that performance declined in more complex situations, particularly where timing was critical. In such cases, the system often misjudged how urgently care was required.

An OpenAI spokesperson responded by stating that the study did not reflect typical real-world usage and that it evaluated an older version of ChatGPT, which the company says has since been improved to address some of these concerns.

The role of AI in supporting patient understanding

Despite these concerns, many clinicians believe that AI tools can still play a constructive role in healthcare, particularly in improving patient understanding and engagement.

“I encourage patients to use these tools,” says Robert Wachter, a doctor at UC San Francisco and author of the recently published book, A Giant Leap: How AI Is Transforming Health Care and What That Means for Our Future.

Wachter points out that barriers to accessing healthcare – including cost and availability – mean that AI can sometimes provide a useful alternative source of information. “The advice you get from the tools is substantially better than nothing and better than what you would get from your second cousin,” says Wachter.

However, he emphasises that AI should never be viewed as a substitute for professional medical care.

Enhancing, not replacing, the doctor–patient relationship

Experts suggest that AI is most valuable when used alongside traditional healthcare, rather than in place of it.

Adam Rodman, a hospitalist and researcher at Harvard Medical School, advises against using AI tools to assess emergency situations. Instead, he sees their greatest benefit in preparing for or reflecting on medical consultations.

“A good time to use a large language model is when you’re about to go see a doctor – or after you see your doctor,” says Rodman.

He explains that AI can help people better understand their condition, ask more informed questions, and make more effective use of time during appointments. This can support a more collaborative relationship between patients and clinicians.

“There are no downsides to better understanding your health,” says Rodman.

The future of AI in healthcare

Healthcare professionals broadly agree that AI is now firmly embedded within modern medicine and will continue to evolve alongside clinical practice.

“ My hope is that you might see AI as an extension of a human relationship,” says Rodman. He envisions a future in which both clinicians and patients work with AI to improve communication and navigate healthcare systems more efficiently.

However, he also raises concerns about potential unintended consequences. One particular risk is the possibility that people may receive serious or distressing diagnoses – such as cancer – directly from an AI system, rather than from a clinician.

Research suggests that when healthcare becomes more transactional or resembles a marketplace, trust in clinicians may decline.

 ”What I hope is that this technology can be used in a way that enhances humanity in medicine,” says Rodman “and not in a way that cuts out the doctor-patient relationship.”

Conclusion

Artificial intelligence is rapidly transforming access to health information, offering both opportunities and risks. While these tools can enhance understanding and support more informed decision-making, their limitations – particularly in how they interpret incomplete or imprecise input – mean they must be used with caution.

Ultimately, AI has the potential to strengthen healthcare delivery, but only if it is integrated in a way that supports, rather than replaces, the human relationships at the heart of medicine.

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‘Shadow AI’ on the Rise in Healthcare as Clinicians Turn to Unauthorised Tools to Improve Workflows

Key Takeaways: 

• A survey of healthcare professionals found that 57% have encountered or used unauthorised artificial intelligence tools in their organisations, highlighting the growing presence of so-called “shadow AI” in healthcare settings.
  
• Many clinicians and administrators report using these tools to improve efficiency, analyse data, and manage administrative tasks, particularly when approved solutions or clear guidance are lacking.
  
• While most respondents believe AI will significantly improve healthcare within five years, concerns about patient safety, data privacy, and security risks remain widespread.
  
  
  

Unauthorised AI tools emerging in healthcare workplaces

A new survey suggests that artificial intelligence tools are already being used in healthcare organisations in ways that fall outside formal governance structures. According to the findings, a significant proportion of healthcare professionals have either encountered or used AI tools that have not been authorised by their employer.

The survey, conducted by Wolters Kluwer Health, gathered responses from 518 healthcare professionals, including both clinical providers and administrators. The research was carried out in December 2025 and was released publicly last week.

Overall, the findings indicate that four in ten healthcare professionals reported encountering unauthorised AI tools within their organisation, while 17% acknowledged personally using such tools.

When responses were analysed by professional role, 15% of physicians admitted to using an unauthorised AI tool, compared with 19% of administrators. In addition, one in ten respondents reported using an unauthorised AI tool in connection with direct patient care.

The report refers to the unauthorised adoption of artificial intelligence tools in professional environments as “shadow AI.”

Why healthcare staff turn to unauthorised AI

The survey findings suggest that healthcare professionals are often motivated by practical needs rather than deliberate attempts to bypass organisational policies.

According to the report:

“Clinical and administrative teams want to adhere to rules surrounding AI usage, but if the organization hasn’t provided guidance or approved solutions, they’ll experiment with generic tools to improve their workflows.”

Many respondents indicated that the absence of formal guidance or approved AI platforms has encouraged individuals to explore publicly available tools on their own.

The most frequently cited motivation for using unauthorised AI tools was the need to accelerate workflows and improve efficiency. Approximately half of respondents identified faster workflows as the primary reason for using these tools.

However, the survey also revealed differences in how clinical and administrative staff tend to use AI technologies.

Administrators were more likely to employ AI tools for operational or analytical tasks such as:

• Data analysis
  
• Predictive analytics
  
• Administrative processes
  

Healthcare providers, meanwhile, reported using AI for activities such as:

• Data analysis
  
• Patient scheduling
  
• Patient engagement tasks
  

The findings also indicate that clinicians were more likely than administrators to experiment with AI tools out of curiosity.

Governance and policy development remain uneven

The survey results highlight a notable imbalance in how different professional groups participate in the development of AI policies within healthcare organisations.

According to the report, administrators were three times more likely than clinical providers to be actively involved in developing AI governance policies.

Specifically:

• 30% of administrators reported involvement in AI policy development
  
• Only 9% of providers said they had participated in such efforts
  

This difference suggests that policy ownership around AI adoption may currently be concentrated within administrative leadership rather than clinical teams.

Administrators also reported greater familiarity with their organisation’s AI policies compared with providers, although awareness varied across both groups.

Security and privacy risks associated with “shadow AI”

The use of unauthorised AI tools raises important concerns about data security, privacy protection, and governance oversight.

The Wolters Kluwer report notes that inconsistent or unsanctioned AI usage can expose organisations to potential vulnerabilities. Without clear oversight, the integration of external AI tools may lead to data privacy violations, security breaches, or inappropriate handling of sensitive information.

To illustrate these risks, the report references a 2025 study by IBM, which found that 97% of organisations that experienced an AI-related security incident lacked adequate AI access controls.

Security incidents involving AI systems can have significant consequences, including financial losses, operational disruption, and damage to public trust.

Healthcare professionals remain optimistic about AI’s future

Despite concerns about governance and security, the survey indicates that most healthcare professionals remain broadly optimistic about the long-term role of artificial intelligence in healthcare.

Nearly 90% of respondents said they believe AI will significantly improve healthcare within the next five years. Administrators were found to be slightly more optimistic than clinical providers about the potential benefits of the technology.

At the same time, respondents recognised that AI implementation carries important risks that must be addressed.

Patient safety was identified by around half of respondents as the most significant risk associated with AI adoption.

Meanwhile, nearly half of respondents also expressed concerns about data privacy risks.

These findings suggest that healthcare professionals recognise both the transformative potential of artificial intelligence and the need for careful governance, clear guidance, and secure systems.

Addressing the rise of “shadow AI”

The report concludes that addressing the growth of shadow AI requires organisations to understand why staff are turning to unauthorised tools rather than focusing solely on restricting access.

According to the report:

“Ultimately, addressing shadow AI is not about restricting access to productivity tools. Leaders must understand why teams are using unsanctioned tools and which challenges they’re trying to solve, and then identify enterprise-level tools that can accomplish these goals safely and securely.”

As artificial intelligence becomes increasingly embedded in healthcare workflows, organisations may need to develop clearer policies, provide approved tools, and involve both clinical and administrative staff in governance decisions.

Such measures may help ensure that the benefits of AI can be realised while protecting patient safety, safeguarding sensitive data, and maintaining organisational trust.

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New Self-Aware Biosensor System Could Improve Reliability of Wearable Medical Devices

Key Takeaways:

• Researchers have developed a new biosensor monitoring system that can rapidly detect when electrodes in wearable medical devices begin to detach from the skin.
  
• The technology evaluates the quality of digital signals transmitted between electrodes through the body, allowing direct monitoring of electrode contact.
  
• Early testing shows the system can identify early signs of electrode failure that conventional monitoring approaches often miss, potentially improving the reliability of digital health monitoring.
  
  
  

Advances in wearable biosensing for modern healthcare

Smart biomedical technologies are increasingly shaping the future of healthcare. A growing number of digital health tools rely on skin-mounted biosensors that collect detailed physiological data directly from the human body. These devices are commonly used in applications such as heart rhythm monitoring, remote patient monitoring, and long-term health tracking.

As these technologies become more widely adopted in clinical practice and home-based healthcare settings, the accuracy and reliability of the signals they collect become critically important. If the sensors or electrodes attached to the skin begin to loosen or detach, the data captured by the device can become unreliable.

To address this challenge, a research team at King Abdullah University of Science and Technology (KAUST) has developed a new system designed to detect electrode detachment in real time. The technology enables medical devices to identify when electrodes begin to lose proper contact with the skin, allowing clinicians and users to maintain accurate physiological monitoring.

The study describing the system was published in the journal Results in Engineering.

Limitations of traditional electrode monitoring methods

Many wearable medical devices rely on electrodes placed on the skin to detect electrical signals produced by the body, such as those generated by the heart. However, ensuring that these electrodes remain properly attached throughout monitoring can be difficult.

Conventional systems typically rely on indirect methods to determine electrode integrity, such as measuring electrical impedance or using other proxy indicators. These techniques were developed many years ago and often assume stable monitoring conditions.

According to the researchers, these assumptions do not always reflect real-world use.

“Traditional methods for checking whether medical electrodes are properly attached, based on impedance or indirect monitoring, were developed many years ago and assume relatively stable conditions,” explains Rajat Kumar, a student working in the laboratory of Ahmed Eltawil, who led the research.

In everyday situations, however, people move, perspire, and change position. These normal activities can cause electrodes to loosen slightly or temporarily lose contact with the skin.

Such intermittent disruptions can be difficult for conventional monitoring approaches to detect.

“This is especially problematic for home-based wearable medical devices, where poor electrode contact may go unnoticed for long periods, leading to inaccurate data being recorded and relied upon,” says Abdelhay Ali, a postdoctoral researcher in Eltawil’s research group.

Rethinking the body as part of the monitoring system

To overcome these limitations, the KAUST team reconsidered how electrodes interact with the body during monitoring.

Instead of viewing the human body purely as a source of interference in electrical measurements, the researchers explored whether it could become part of the detection mechanism itself.

Eltawil describes this shift in perspective:
“Instead of treating the body as something that interferes with measurements, we considered whether it could be part of the solution.”

Previous research has shown that very small electrical signals can safely travel through the body. The researchers realised that this property could be used to evaluate the condition of electrode attachments.

“We realized that if electrodes could exchange digital signals through the body, then the quality of that communication would directly reflect how well the electrodes were attached,” Kumar says.

If the electrodes remain firmly attached, the signals between them would be transmitted clearly. If the electrodes begin to loosen, the signal quality would deteriorate.

How the self-aware monitoring system operates

To test this concept, the team developed a monitoring system built around a custom-designed microchip created at KAUST.

The system works by sending very small digital signals between electrodes positioned at different locations on the body. These signals pass through the body and are then received by other electrodes.

A small processing unit analyses how well the signals are received.

According to Ali, the signal quality provides a direct indication of electrode contact:

“Clear signals indicate good electrode skin contact; small errors indicate weakening contact; and missing signals indicate disconnection.”

In addition to the chip and signal-processing unit, the system includes a control component that manages the electrode-checking sequence. This allows the device to automatically evaluate multiple electrodes in sequence without interrupting the primary medical measurements being performed by the device.

Testing the system on human skin

To evaluate the system’s effectiveness, the researchers conducted experiments using electrodes placed on human skin.

The testing showed that the system could reliably distinguish between several different conditions of electrode attachment, including:

• Firmly attached electrodes
  
• Partially loosened electrodes
  
• Electrodes that intermittently lose contact with the skin
  
• Completely disconnected electrodes
  

Importantly, the system demonstrated the ability to detect early stages of contact degradation before full disconnection occurs.

“Importantly, the system detected the early signs of contact degradation that traditional methods often miss,” Kumar says.

This early detection could be particularly valuable in wearable health monitoring devices that operate continuously over long periods.

Potential benefits for long-term wearable monitoring

A key feature of the new system is its very low power consumption, which makes it suitable for wearable technologies that must operate continuously for hours or days at a time.

Ali explains that this efficiency could make the technology practical for real-world use.

“The system’s very low power consumption should enable practical integration with wearable medical devices that need to run continuously for long periods,” he says.

He also notes that the design could be incorporated into existing devices with minimal modifications.

“These components form a compact and efficient solution that can be added to existing medical devices with minimal changes.”

Towards fully integrated wearable medical devices

Although the current system has been demonstrated in laboratory testing, the research team is now working to advance the technology further.

Their next goal is to develop a fully integrated single-chip system capable of monitoring many electrodes simultaneously.

Such a system could be used in a range of clinical monitoring devices, including multi-lead electrocardiogram (ECG) monitors and other wearable biosensing platforms used in both hospital and home environments.

Eltawil emphasises the broader aim of translating the technology into practical healthcare solutions.

“Ultimately, our goal is to translate this KAUST-developed technology into practical medical devices that are more reliable, more trustworthy, and better suited for continuous health monitoring in the clinic and at home,” he says.

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Camera Glasses and Biomarkers Aim to Transform How Diets Are Measured in Real Life

Key Takeaways:

• A UK-wide study is testing camera glasses alongside blood and urine biomarkers to produce more accurate dietary data than self-reported food diaries.
  
• Researchers say existing methods often fail to capture snacking, portion sizes, and mindless eating, limiting confidence in nutrition research.
  
• While the technology could improve objectivity, experts caution it may not be suitable for everyone, particularly people vulnerable to food anxiety or disordered eating.
  
  
  

A new approach to measuring what people really eat

A new UK trial is exploring whether wearable camera glasses, combined with biological markers, can provide a more reliable picture of what people eat and drink in their everyday lives. The study is led by the University of Reading and aims to address long-standing challenges in nutrition research, particularly the limitations of self-reported dietary data.

Registered nutritionist Christine Bailey explained that camera glasses build on existing clinical tools, such as food photographs used to support dietary assessment, by capturing intake automatically and in real time. According to the research team, this approach could significantly reduce reliance on memory and self-reporting, which are known to be imperfect.

Professor Julie Lovegrove, who is leading the trial, said: “Humans are not very reliable, especially when asked to remember snacking or portion sizes.”

The study is taking place against a backdrop of rising rates of excess weight in the UK. Analysis from the Health Foundation in 2025 indicated that more than 60 percent of UK adults are now classified as living with overweight or obesity, including around 28 percent living with obesity.

How the SODIAT-2 study works

The study, known as SODIAT-2, will recruit 133 adults from across the UK to take part in a five-week programme conducted entirely from their own homes.

For up to 12 days, participants will wear camera glasses that automatically take photographs of everything they eat and drink. During this period, they will also collect small blood and urine samples using easy-to-use kits that are returned by post for laboratory analysis. In addition, participants will complete short online questionnaires to report what they have eaten over recent days.

All participants will then follow a standardised test diet, consuming identical foods and drinks for three days. By combining wearable imagery, biological data, and self-reported information, the research team aims to identify the most accurate and practical way to study diets in real-world settings.

Why current dietary assessment methods fall short

One of the central problems in nutrition research is obtaining an accurate account of people’s habitual eating patterns. Dr Manfred Beckmann, lead principal investigator from the Department of Life Sciences at the Aberystwyth University, said: “One of the problems facing nutrition researchers is getting a true picture of people’s eating habits.”

Professor Lovegrove noted that current approaches typically rely on food diaries, questionnaires, and 24-hour dietary recalls. She described these tools as “not very reliable or accurate”, largely because they depend on memory and honest reporting.

Christine Bailey added: “Research consistently shows that self-reported food diaries are prone to recall bias, with people often misremembering what they ate, when they ate it, and portion sizes.”

Dr Michelle Weech, research fellow at the University of Reading and trial manager, said: “By automatically photographing everything they eat and drink and measuring substances the body makes from food in their blood and urine – we will have dietary data we can really rely on.”

Potential benefits for nutrition and public health research

Researchers believe that combining camera glasses with biomarkers could mark a step change in how diets are measured. More accurate dietary data would allow scientists to explore links between diet, health, and disease with greater confidence, including conditions such as type 2 diabetes, cardiovascular disease, and some cancers.

Bailey said wearable camera technology may “improve objectivity and offer valuable insight into eating behaviours and patterns” that are not always captured through written food records alone.

Wearable devices and AI-driven analysis like this are part of a much wider digital transformation of healthcare – a shift that professional training such as the College of Contemporary Health’s digital health CPD courses aims to help practitioners keep pace with.

Registered nutritional therapist Gemma Westfold, based in Windsor, highlighted how the technology could also shed light on behavioural aspects of eating. She said: “Humans can eat mindlessly on occasion, whilst scrolling on social media or while watching TV. When we are absorbed in other activities whilst eating, we can risk overeating, but more importantly, we can also switch off our ability to adequately digest and absorb the nutrients.”

Westfold added that using camera glasses for a short period could help identify behavioural patterns and support mindful eating, which she described as “key for all health conditions”.

Ethical considerations and concerns about over-monitoring

Despite the potential benefits, experts emphasise that such tools will not be appropriate for everyone. Bailey cautioned that increased monitoring could be counterproductive for some people: “For a small proportion of individuals, particularly those vulnerable to food anxiety or disordered eating, increased focus on food monitoring or quantity can become counterproductive and heighten preoccupation around eating.”

Westfold also raised concerns about how constant monitoring might affect therapeutic relationships. She said: “My line of work is based on relationships and making someone feel comfortable. Camera glasses could imply that as a nutritionist I do not trust my client, or believe what they are telling me.”

“It could make us feel like a food nanny, policing our clients and damage that relationship because they are under constant surveillance,” she added.

A collaborative UK research effort

The SODIAT-2 project brings together expertise from several UK institutions. Alongside the University of Reading and Aberystwyth University, partners include the University of Cambridge, which is leading blood sample analysis, and Imperial College London, which developed the camera glasses and is using artificial intelligence to analyse the images captured by the wearable devices.

Funded by the UK Medical Research Council and the Biotechnology and Biological Sciences Research Council, the project aims to improve how dietary intake is measured, providing stronger evidence to inform future nutrition guidance and public health policy.

CCH insight

Camera glasses and AI-analysed dietary data are a vivid example of how digital health tools – wearables, connected devices and machine learning – are moving into the heart of research and care. As these technologies become part of everyday practice, being equipped to understand and use them well is increasingly imperative for healthcare professionals. CCH’s CPD-accredited digital health short courses are designed to help practitioners build the practical skills and judgement to keep pace, quickly and flexibly around a clinical schedule.

Explore CCH’s Digital Health courses →

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