Digital Health

NHS pilots smart scale and app programme to support children living with severe obesity

Key Takeaways:

• A new NHS England pilot introduces smart scales and a connected app in 15 specialist clinics to support weight management among children living with severe obesity.
• The technology provides weight trend data without displaying exact figures, aiming to reduce anxiety and encourage sustainable behavioural change.
• Clinical teams receive data remotely and use the app to offer personalised, ongoing support to families in their own homes.
  

A New Technological Approach to Paediatric Obesity Care

NHS England has launched a pilot scheme involving the use of a smart scale and mobile app to support children living with severe obesity. The initiative is currently underway at 15 Complications from Excess Weight (CEW) clinics and is serving approximately 350 children and their families. A further four clinics are expected to adopt the programme during the summer months.

The tool forms part of a broader strategy to enhance obesity care through technology-enabled behavioural support, allowing clinical teams to monitor progress and communicate with families without requiring frequent in-person visits.

How the Smart Scale Works

At the heart of the pilot is a specially designed set of smart scales for use at home. These scales do not display numerical weight readings. Instead, they connect to a mobile application which indicates the general direction of weight change – whether weight is being maintained, gained, or lost. This aims to reduce anxiety or fixation around specific weight measurements.

The app transmits the data directly to clinical teams, who can then provide regular, tailored feedback to families. This feedback is delivered through the app and forms part of a broader behavioural support strategy led by CEW clinics.

According to NHS England, this model allows clinics to extend their capacity to deliver holistic, multidisciplinary care that addresses not only physical health but also psychological and behavioural dimensions of obesity.

A Focus on Long-Term Behaviour Change

Professor Simon Kenny, NHS England’s National Clinical Director for Children and Young People, emphasised the significance of combining digital innovation with a person-centred, behavioural approach.

“It is fantastic that through cutting-edge technology and a holistic and behaviour change approach to obesity care, our specialist NHS clinics have already transformed the lives of thousands of children and young people – supporting them to lose weight, live healthier lives and improve their mental health.”

He continued by highlighting the added value of the new tool:

“This game-changing tool is helping our specialists support and keep track of children’s weight loss progress without them needing to leave home, while offering regular advice to them and their parents to help build healthy habits.”

A Scalable Model for Community-Based Support

The smart scale and app solution reflects a growing recognition within the NHS that effective obesity care must extend beyond clinical settings. By enabling care teams to maintain close contact with families remotely, the approach supports sustained engagement and long-term lifestyle change.

The NHS anticipates that this model, if successful, could become a blueprint for future technology-supported obesity interventions across the country – particularly in reaching families who may face barriers to regular clinic attendance.

As rates of childhood obesity remain high in the United Kingdom, especially among those from underserved and socioeconomically disadvantaged backgrounds, scalable, accessible solutions such as this one are becoming increasingly vital.

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NHS digital transformation backed by £10bn investment – sector leaders call for joined-up action and long-term strategy

Key Takeaways:

• The UK government has committed up to £10 billion to digitise the NHS by 2028–29, alongside a £29 billion real-terms increase in day-to-day spending, marking what Chancellor Rachel Reeves called a “record” cash investment.
• Plans include transforming the NHS App into the “digital front door”, creating a single patient record, and improving secure access to services and communications.
• Sector leaders broadly welcomed the funding but urged the government to prioritise interoperability, workforce capacity, strategic continuity, and genuine integration with social care.

Government Outlines Historic Funding Package for NHS Reform

In her first major fiscal intervention, Chancellor Rachel Reeves has announced what she described as a “record” investment in the National Health Service, including up to £10 billion earmarked for technology and digital transformation over the next four years. The funding forms part of a broader £29 billion real-terms increase in annual NHS day-to-day spending from 2023–24 to 2028–29. This brings projected total NHS expenditure to £226 billion by 2028–29, representing an average 3% annual real-terms growth.

Addressing the House of Commons on 11 June 2025, Reeves reaffirmed the government’s commitment to preserving the NHS as “our most treasured public service” – one that remains publicly funded and free at the point of use. She stated that the new funding would “bring our analogue health system into the digital age”.

The Spending Review 2025 document outlines several specific priorities for the investment:

• A 50% increase in NHS digital investment from 2025–26 levels.
• The transformation of the NHS App into a comprehensive ‘digital front door’, allowing patients to manage prescriptions, access test bookings and results, and securely receive communications.
• Delivery of a single, unified patient record, enabling two-way communication between patients and clinicians and streamlining the patient journey across care settings.

The Chancellor also confirmed a £2.3 billion real-terms increase in the Department of Health and Social Care’s annual capital budget through to 2029–30, which will support digital infrastructure, new hospitals, and upgrades in primary care.

Reeves noted that this package builds on previous commitments, including £2 billion pledged in the October 2024 Autumn Budget and a further £3.25 billion transformation fund announced in the March 2025 Spring Budget.

Analysis: Can Funding Drive True Transformation?

Sarah Woolnough, Chief Executive of The King’s Fund, welcomed the government’s prioritisation of health despite fiscal constraints:

“A 2.8% average increase in total health department spending – 3% for day-to-day NHS spending – will have been hard-fought for in the spending round negotiations, despite still being lower than the historical average the NHS has received over recent years.

“A key challenge now will be for NHS to decide how it can deliver most value from the money that has been allocated.”

A Health Foundation analysis released in May 2025 estimated that at least £21 billion would be needed over the next five years to fully digitise health and care services—suggesting the current allocation may fall short without careful prioritisation and long-term planning.

Sector Leaders React: Promise, Pressure and Pragmatism

A wide array of healthcare and digital sector leaders shared their perspectives on the funding announcement:

Jennifer Dixon, Chief Executive, Health Foundation

“A real terms funding increase of 3% a year is a good settlement for the NHS. But how far the money stretches – and how much it benefits patients – will depend on how much is needed to fund pay settlements for NHS staff and how well the money is spent.

“The additional funding for technology is welcome as a first down payment on a long-term settlement to digitise the NHS. The focus needs to be on implementation to ensure the NHS realises the benefits and patients get the 21st-century care they have been promised.”

Steve Wightman, Managing Director, Access HSC

“£29bn additional funding per year is unprecedented. However, it’s at the expense of other core public services, notably social care.

“We need investment that empowers providers to scale integrated services that wrap around the individual, regardless of setting. Policymakers must stop looking at the NHS in isolation and start acting in alignment with the Department’s title: health and social care.”

Tom Whicher, CEO and Co-founder, DrDoctor

“This is a welcome step toward real, lasting change. But effective change requires improved interoperability – we already have many digital tools, but they must work together with systems like the NHS App to ensure consistent access to care, streamline services, and enhance patient outcomes.”

Nick Lansman, CEO and Founder, Health Tech Alliance

“This is a watershed moment for health tech adoption. The doubling of the technology budget and £10 billion fund set a foundation for faster innovation uptake. Combined with life sciences investment and the forthcoming 10 year health plan, the government has given us reason to be optimistic.”

Dr Katherine Halliday, President, Royal College of Radiologists

“Investment in technology and diagnostic infrastructure is vital. But success will depend on addressing the chronic workforce shortages in cancer and diagnostic services. We need not only equipment but also the trained professionals to use it effectively.”

Dr Rachael Grimaldi, CEO, CardMedic

“One-off injections are not enough. True transformation requires sustained support beyond pilot phases. Procurement reform, delivery capacity, and strategic planning must also be tackled—without that, we’re building on shaky ground.”

Ram Rajaraman, Industry Lead, Quantexa

“A single patient record could be a game changer. It would save 140,000 staff hours annually by reducing duplication and record-chasing. Integrating broader health determinants can also enable more effective AI deployment to tackle service gaps and improve outcomes.”

John Ramsay, MD, Social-Ability

“Once again, social care is left in the shadows. Fair pay is a start, but it’s no substitute for structural reform. Without adequate support for care homes and staff, NHS investment alone cannot ease system-wide pressure.”

Alison Gardiner, CEO, Sleepstation

“We must prioritise scalable, evidence-based interventions, particularly for long-term conditions like insomnia. Mental health needs are rising fast, and technology investment should reflect this reality.”

Kevin Shah, Head of Sales EMEA, Annalise.ai

“With radiologist shortages projected to reach 40% by 2028 and outsourcing costs climbing, AI imaging tools offer an essential solution. It’s encouraging to see the government embracing AI not just for innovation’s sake, but to deliver practical clinical value.”

Julian Coe, MD, X-on Health

“Transformation needs to happen from the ground up. The NHS cannot rely on national funding alone—change must be operationally embedded at local levels through smarter tech integration and hands-on support.”

Rich Pugmire, CEO, Answer Digital

“The money is vital, but progress hinges on how wisely it is used. The upcoming 10 year health plan must prioritise partnerships and interoperability—legacy tech must not be a barrier to AI adoption and service reform.”

Nick Wilson, CEO, System C

“Innovation must be built into core NHS workflows. Standalone tools save time, but integrated AI systems can double that benefit. Clinicians didn’t enter the profession to do admin—AI should give them back time to care.”

Looking Ahead

As the NHS awaits further detail in the forthcoming 10 year health plan and NHS Long Term Workforce Plan, sector leaders are calling for not just investment but alignment – across technologies, departments, and between health and social care. The government’s pledge is bold, but without structural reform, implementation support, and workforce planning, it risks becoming another missed opportunity.

The coming years will reveal whether this digital transformation truly marks a turning point for the NHS—or simply another cycle of ambition constrained by complexity.

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UVA’s artificial pancreas incorporates digital twin technology to enhance personalised diabetes management

Key Takeaways: 

• Digital twin simulations allow personal experimentation: Individuals with Type 1 diabetes can safely trial adjustments to their artificial pancreas system using virtual models based on their own data.
• Improved blood glucose control: Use of the new system led to an increase in time spent within a healthy blood glucose range, from 72% to 77%, over six months.
• Biobehavioural co-adaptation: The approach supports better synchronisation between user behaviours and automated insulin delivery, enhancing real-world efficacy.
  

Introduction: A New Frontier in Personalised Diabetes Care

Researchers at the University of Virginia (UVA) have developed a novel approach to managing Type 1 diabetes using a form of artificial intelligence known as a digital twin. This innovation has been embedded into UVA’s artificial pancreas system and enables it to respond more effectively to each individual’s changing physiological needs. In a six-month clinical study, this adaptive model resulted in improved glucose control among participants.

The technology, termed “adaptive biobehavioural control”, facilitates a dynamic feedback loop between the person using the system and the device itself. It is the first study of its kind to allow individuals to test potential adjustments in a virtual space that reflects their own physiology before applying those changes in real life.

Adaptive Biobehavioural Control: How It Works

The core of the system lies in its digital twin architecture. This is a cloud-based computational model that mimics how a particular person’s body responds to insulin and processes glucose. The model is updated every two weeks based on recent user data, such as glucose readings, insulin usage, meals, and activity levels.

Users can interact with this virtual twin to test out modifications to the artificial pancreas system. For instance, they may simulate changes to insulin delivery overnight or during periods of physical activity, then observe how their digital twin would respond. This safe testing ground helps users make informed decisions and adapt their device settings accordingly.

Dr Boris Kovatchev, Director of the UVA Center for Diabetes Technology, explained:

“Artificial pancreas systems require adjustments by those who use them to adapt to a person’s changing insulin demands. This is the first study that maps each person to their ‘digital twin’ in the cloud and enables people with diabetes to experiment with their own data to learn how their artificial pancreas system would react to changes, in a safe simulation environment, before adjusting their system.”

Real-World Results: Measurable Improvements in Glucose Control

The study involved individuals living with Type 1 diabetes who used the adaptive system over a six-month period. Key outcomes included:

• Increased time in target glucose range: Participants increased the proportion of time their blood glucose levels remained within the recommended range from 72% to 77%.
• Lower average blood glucose: Although the reduction was modest, it was clinically meaningful in terms of reducing long-term complications.

The most notable gains occurred during the day, when glucose variability is greatest due to food intake and physical movement. Traditional artificial pancreas systems are generally more effective at night, when external variables are limited. The adaptive system’s ability to improve performance during waking hours represents a major advancement.

Human-Machine Co-Adaptation: The Next Step in Automated Care

A key strength of this approach is that it does not treat the individual as a passive recipient of care. Instead, it invites active engagement. The digital twin empowers users to co-manage their diabetes alongside the automated system, with each adapting to the other over time.

As Dr Kovatchev notes:

“Human-machine co-adaptation is critical for conditions like Type 1 diabetes, where treatment decisions are made both by the artificial pancreas algorithm and the person who wears it. Digital-twin technology is very helpful in facilitating this co-adaptation.”

This concept of shared decision-making between human and machine may well represent the future of chronic disease management—especially in conditions requiring continuous data monitoring and nuanced adjustments.

Conclusion

UVA’s integration of digital twin technology into its artificial pancreas system marks a significant stride in the pursuit of tailored diabetes care. By enabling people with Type 1 diabetes to model and refine their glucose management strategies in a safe and responsive environment, the system not only improves physiological outcomes but also strengthens user confidence and autonomy.

As the field of digital health continues to evolve, this hybrid model—blending machine learning, human insight, and patient-specific data—illustrates the promise of more intelligent, adaptive, and person-centred care.

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From paper to push notifications: NHS app set for major expansion

Key Takeaways:

• Digital-by-default shift: The NHS app will become the primary communication tool for millions of patients in England, aiming to streamline messaging and reduce reliance on traditional post.
• Significant cost savings: The government anticipates savings of £200 million over the next three years through the reduction of printed letters and missed appointments.
• Equity in access remains a concern: While the initiative promotes convenience, medical leaders stress that care must be taken to ensure people without access to digital devices are not left behind.
  

Introduction: A Shift Towards Digital Healthcare

The NHS app is set to become the default method of communication for a vastly expanded number of patients in England, under a new government initiative aimed at modernising patient services and reducing unnecessary healthcare spending. As part of a £50 million investment into the digital platform, the Department of Health and Social Care has announced plans to deliver more test results, screening invitations, and appointment reminders directly to patients’ smartphones.

According to the department, the expanded use of the app could save the NHS up to £200 million over the next three years by reducing its reliance on printed letters and manual communications.

Reducing Paper: Towards a More Efficient NHS

Currently, the NHS sends approximately 50 million letters to patients each year. Under the new plan, this number will be significantly reduced, as electronic notifications become the norm for patient communication. Push notifications sent through the NHS app will now be used to remind patients of upcoming appointments, a move intended to help curb the high rate of non-attendance. In 2023/24, the NHS recorded around eight million missed elective care appointments, a figure the government hopes to reduce through more timely digital reminders.

Additional features are in development, including functionality that will allow patients to add appointments directly to their mobile phone calendars and request assistance from their local GP surgeries through the app.

Safeguards for People Without Digital Access

While the shift to digital communication is intended to streamline services, concerns remain around accessibility for people who are not digitally connected. The Department of Health and Social Care stated that individuals unable to access messages via the NHS app—particularly older people—will instead receive text messages. If that is not possible, letters will still be sent as a final measure. It is hoped that these measures will ensure no patient is excluded from critical healthcare information, while also freeing up NHS phone lines for other purposes.

Professor Phil Banfield, Chair of the British Medical Association (BMA) Council, welcomed the drive towards modernisation but warned against overlooking people who are digitally excluded:

“We must guard against creating a situation in which patients who are vulnerable, elderly and possibly without access to digital communication are forgotten and left behind – as they already are in society.”

Engagement with the NHS App Continues to Grow

Figures released by the department reveal strong engagement with the NHS app. More than 11 million people currently log into the app each month, and nearly 20 million users have opted in to receive messages through the platform. In the current financial year, an estimated 270 million messages are expected to be delivered via the app—up 70 million from the previous year.

The NHS app, which was first launched in December 2018, is now in use in 87% of hospitals across England, signifying widespread adoption across secondary care.

Leadership Perspectives: Convenience and Modernisation

Health Secretary Wes Streeting expressed strong support for the expansion, positioning it as a leap forward for digital healthcare:

“Further investment in the app will bring the NHS into the digital age so that being a patient is as convenient as online banking or ordering a takeaway.”
He also noted that shifting away from paper-based communications would free up funding for front-line NHS services.

Rachel Power, Chief Executive of the Patients Association, described the development as:

“A significant step in modernising how patients receive information.”

Next Steps in NHS Digital Strategy

This announcement follows several recent digital innovations announced by NHS England. In January, the government unveiled plans to allow more patients to book treatments and appointments through the app. Last month, it introduced “Amazon-style” prescription tracking, enabling patients to check via the app whether their medications are ready to collect or have been dispatched for delivery.

These developments reflect a broader ambition to empower people to manage their own healthcare more actively, with greater ease and autonomy, while simultaneously improving efficiency across the health service.

Conclusion: A Balancing Act of Innovation and Inclusion

The expansion of the NHS app marks a significant milestone in the UK’s digital health transformation. While the promise of greater convenience, improved attendance rates, and substantial cost savings is clear, ensuring that the move does not disadvantage people without digital access will be critical to the success and equity of this strategy. As the NHS moves forward with its digital-first approach, maintaining an inclusive framework will be essential to ensure that all patients, regardless of technological capability, remain supported and informed.

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Generative artificial intelligence in healthcare: Enthusiasm outpaces readiness, report warns

Key Takeaways:

• Despite enthusiasm for generative AI among health professionals, fewer than one in five organisations have established formal policies or required training in its use.
• Clinicians identify key GenAI applications in alleviating workforce shortages, clinician burnout, and administrative burden — yet organisational structures lag behind these ambitions.
• Survey respondents urge a strategic approach, calling for governance frameworks addressing data use, ethical responsibilities, and workflow integration.
  

Introduction: Hope Meets Hesitation in Healthcare AI Adoption

Many healthcare professionals across the United States are hopeful that generative artificial intelligence (GenAI) can help address some of the sector’s most entrenched challenges — from persistent workforce shortages and clinician burnout to the administrative overload tied to electronic health records. However, a new report reveals a troubling gap between professional enthusiasm and institutional readiness.

The 2025 Future Ready Healthcare Survey Report, jointly released by Wolters Kluwer Health and Ipsos, highlights a sector on the cusp of change but burdened by systemic inertia. Drawing insights from 312 respondents — including physicians, nurses, administrators, and pharmacists — the findings underscore both the appetite for GenAI and the foundational barriers preventing its full-scale adoption.

“GenAI has the potential to be a powerful tool for supporting sustainability in healthcare organisations right now, as well as preparing them for a more efficient future,” said Greg Samios, CEO of Wolters Kluwer Health. “Right now, organisations are at risk of falling behind unless they take a more cohesive approach to making GenAI standardised, scalable and impactful.”

Workforce Pressures Drive Demand for GenAI Integration

Among surveyed professionals, the top drivers for GenAI implementation centred on supporting a depleted and overstretched workforce:

• 85% highlighted recruiting and retaining nursing staff as a critical priority.
• 76% pointed to reducing clinician burnout.
• 67% aimed to ease the administrative burden of tasks such as prior authorisations.
• 62% emphasised streamlining electronic health record (EHR) management.

There was also optimism about more advanced applications of GenAI, including:

• Ambient listening tools for automated note-taking,
• AI-driven clinical decision support (CDS), and
• Communication and documentation automation.

However, while aspirations are high, the necessary infrastructure and governance to support these use cases remain largely absent.

Governance and Training: The Missing Foundations

Despite the proliferation of GenAI technologies, most healthcare organisations have yet to develop robust frameworks to guide their implementation. Only:

• 18% of respondents said their organisation had issued formal policies on GenAI use.
• 20% reported that structured training was required.

This lack of preparation is compounded by a broader issue — GenAI solutions are often added as standalone features, rather than being embedded into care workflows.

“GenAI delivers durable value only when it is welded to a mapped workflow rather than bolted on as another point solution,” explained Dr Matthew Crowson, Director of Digital Innovation, Health Research at Wolters Kluwer Health. “The first job is old-fashioned problem-solving.”

Risks of Inadequate Implementation

With so few safeguards in place, healthcare professionals expressed strong concerns about premature or unstructured GenAI deployment. Key worries included:

• 57% feared that overreliance on GenAI could impair clinical judgement.
• 55% cited concerns about transparency, data privacy, and the absence of industry-wide standards.

Pharmacists and allied health professionals were particularly wary. Nearly three-quarters in these roles warned that excessive trust in GenAI could lead to the erosion of essential clinical skills.

Personal Adoption Outpaces Professional Use

Despite the lack of institutional infrastructure, GenAI is already making inroads into the personal lives of health professionals:

• 51% said they use GenAI in their personal life at least once a week.
• Yet only 42% used GenAI at work weekly.
• 40% reported never having used GenAI in a professional context.

This discrepancy underscores a recurring theme in healthcare: the sector is often slow to adopt and integrate emerging technologies, even when those technologies are actively shaping other industries.

Opportunities for Early Adoption: Nurses and Pharmacists Leading the Way

The report identified nursing and pharmacy departments as potential proving grounds for GenAI integration. Respondents from these groups were among the most optimistic:

• 52% of pharmacists and 45% of nurses believed GenAI could significantly reduce burnout.
• Many also saw benefits in automating routine tasks such as scheduling and data entry.

There was limited concern that GenAI would displace core roles. Instead, professionals viewed it as a tool to enhance — not replace — clinical capacity.

A Sector-Wide Call for Strategic Leadership

To harness GenAI’s potential without introducing new risks, healthcare leaders are being urged to move beyond one-off pilot schemes. The report highlights strong support for:

• Clearer policies on responsible data use (64%),
• Greater transparency in addressing algorithmic bias (55%), and
• Legal and ethical clarity regarding GenAI’s role in decision-making.

“It is imperative organisations deploy GenAI strategically and methodically, establishing clear, understood, well-communicated guidelines and applicable training,” said Denise Anderson, President and CEO of the Health Information Sharing and Analysis Center.

Some respondents went further, calling on leaders to shift their mindset entirely — from incremental improvements to systemic transformation.

“Too many healthcare orgs are duct-taping AI onto crumbling workflows, hoping for efficiency while ignoring reinvention,” said Tatyana Kanzaveli, CEO of Open Health Network. “GenAI is not here to optimise the past — it’s here to provoke a redesign of care itself. Until leaders shift from pilot projects to system-level provocations, we’ll keep solving yesterday’s problems with tomorrow’s tools.”

Conclusion: Realising the Potential of GenAI Requires Bold, Informed Action

The report leaves little doubt: generative AI holds promise for a more efficient, sustainable healthcare system — but only if implemented with foresight, structure, and purpose. Professionals are ready. Organisations must now catch up.

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The future of virtual care: 30% of U.S. medical visits could be remote by 2026 – but policy uncertainty looms

Key Takeaways:

• Telehealth could account for up to 30% of all U.S. medical appointments by 2026 — but only if federal policy supports long-term adoption.
• Mental health leads telehealth usage, with 38.3% of encounters conducted virtually in 2023, highlighting both patient demand and clinical adaptability.
• Despite robust infrastructure and patient interest, policy gaps around reimbursement, cross-state licensing, and audio-only visits threaten progress.

Virtual Care’s Potential — and the Policy Threat That Shadows It

Telehealth is poised to transform the delivery of healthcare across the United States, with a projected 25% to 30% of all medical visits occurring virtually by 2026, according to a forecast by healthcare IT consultancy ScienceSoft. Yet, this future hinges less on technology or patient demand than on a more uncertain force: federal legislation.

Although key Medicare telehealth flexibilities were extended earlier in the year, they are set to expire on 30 September, placing healthcare providers in a precarious position. The temporary nature of these extensions — and the exclusion of several widely-used pandemic-era programmes — has resulted in confusion and strategic hesitation for health systems attempting to plan for the future.

“Key obstacles to telemedicine adoption are related to regulations rather than technological barriers or lack of demand,” wrote Dr Gala Batsishcha, a healthcare IT consultant at ScienceSoft, in the company’s February forecast.

Mental Health: A Stronghold of Virtual Engagement

Among the various medical specialties, mental health services have emerged as a leader in telehealth adoption. In 2023, 38.3% of all mental health encounters in the U.S. were conducted via telehealth platforms. This sustained demand, even as general telehealth use has declined post-COVID, points to strong alignment between patient needs and clinical service delivery.

“The resilient utilisation in mental health shows that the necessary technology and demand are already in place,” said Batsishcha in the report. “The future of its adoption — whether it continues to increase or returns to pre-pandemic levels — largely depends on government decision-makers.”

This sustained virtual engagement reflects both patients’ comfort with remote mental health care and clinicians’ growing proficiency with digital platforms in this context.

Hospitals and Patients Are Ready — But Laws Lag Behind

According to Definitive Healthcare data, by early 2024, nearly 79% of U.S. hospitals had implemented some form of telehealth infrastructure. Patients, especially those in rural communities or with physical, mobility, or time constraints, continue to embrace virtual appointments for their convenience and accessibility.

The main obstacle, however, is not the readiness of hospitals or the enthusiasm of patients. It is the lack of durable public policy.

While Congress extended several Medicare flexibilities — such as enabling virtual visits from home and widening the types of healthcare professionals eligible to offer telehealth — many important provisions were left out. These include:

• Reimbursement for audio-only visits, which many patients with limited internet access rely on;
• Cross-state licensing reforms, which would ease access to specialists and alleviate regional disparities;
• Telehealth allowances within high-deductible health plans, which would increase affordability and uptake.

These omissions have curtailed telehealth’s broader potential and complicated planning for healthcare providers.

Clinician Perspectives: Balancing Efficiency and Caution

For clinicians, telehealth presents notable benefits, including reduced no-show rates, greater scheduling flexibility, and efficiency in handling low-acuity cases. Models such as remote patient monitoring (RPM) and digital triage tools offer scalable solutions without the need for additional physical infrastructure — a vital consideration for resource-constrained practices.

Yet, reservations remain.

“It’s important to strike a balance here,” Batsishcha cautioned. “For routine follow-ups, medication refills, mental health consultations or minor ailments like colds and flu, virtual visits can be an excellent way to save time and reduce strain on the healthcare system. However, for more complex or urgent issues … an in-person visit is crucial for an accurate diagnosis and immediate intervention.”

Clinicians also express concern that critical symptoms may be overlooked without physical examinations and that relational trust between patients and providers could diminish if virtual care is overused or impersonal.

The Road Ahead: Policy Will Determine Progress

The trajectory of virtual care in the United States will ultimately be shaped by what Congress decides in the coming months. Following a continuing resolution in March, which extended current flexibilities through September, there have been calls — notably from the American Telemedicine Association (ATA) — urging lawmakers to make these changes permanent and to reinstate previously expired programmes.

Without decisive policy action, telehealth risks stagnating just as its infrastructure and user base reach maturity.

ScienceSoft remains optimistic, projecting that one-quarter to one-third of all medical visits in the U.S. could be conducted virtually by 2026. But the report stresses that this potential will only be realised if the federal government removes regulatory barriers and offers long-term clarity.

As Batsishcha summarised, “The necessary technology and demand are already in place. Key obstacles to telemedicine adoption are related to the regulations rather than technological barriers or lack of demand.”

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AI tool accurately predicts cancer patient outcomes based on pre-treatment data

Key Takeaways:

• A machine learning platform developed at Weill Cornell Medicine accurately grouped lung cancer patients by shared baseline traits and treatment outcomes using health record data.
• The model outperformed all previously published approaches in predicting treatment responses, using a real-world dataset of patients with advanced small-cell lung cancer.
• This tool could enhance both clinical trial design and individualised treatment planning, while offering insight into the biological underpinnings of patient subgroups.

Introduction: A Smarter Way to Predict Cancer Treatment Outcomes

A new artificial intelligence (AI) method developed by researchers at Weill Cornell Medicine and Regeneron Pharmaceuticals can accurately sort people living with cancer into groups with similar baseline characteristics and likely treatment outcomes. The breakthrough, published on 12 May in Nature Communications, could improve both patient selection in clinical trials and the tailoring of treatments to individual patients.

“We’re hopeful that this approach ultimately will be useful for testing and targeting treatments across a wide range of diseases,” said senior author Dr Fei Wang, founding director of the Institute of AI for Digital Health and professor of population health sciences at Weill Cornell Medicine.

Background: Addressing a Longstanding Challenge

Predicting which individuals are most likely to benefit from a particular cancer treatment has proven to be a major challenge for both pharmaceutical companies and healthcare professionals. Machine learning has long been viewed as a promising tool to uncover complex patterns in large datasets, such as those derived from electronic health records. However, traditional algorithms often fail to match these patterns to actual treatment outcomes.

“Groupings don’t always correspond closely to patients’ future treatment responses,” explained Dr Wang. Recognising this limitation, Regeneron scientist Dr Ying Li approached Dr Wang’s team with the aim of developing a more effective system.

“Our goal was to develop a platform that sorts patients with the target disease who are receiving the same treatment into groups sharing similar baseline characteristics and treatment outcomes,” said Dr Li. “We validated this method using a real-world database of advanced small-cell lung cancer patients treated with immune checkpoint inhibitors.”

Methodology: Training on Real-World Data

The machine learning platform was developed by first author Dr Weishen Pan, a postdoctoral research associate in Dr Wang’s lab. It was trained on anonymised health records from 3,225 individuals with lung cancer. Each record included 104 variables—ranging from blood test results and prescriptions to tumour stage and medical history.

The platform grouped patients into three distinct subpopulations:

• The group with the longest average survival time consisted mostly of women (55.5%) and had relatively low rates of co-occurring conditions such as diabetes and heart failure.
• The group with the shortest average survival time had less than half the mean survival of the first group, comprised mostly of men (66.2%), and exhibited more extensive tumour metastases along with blood test abnormalities suggesting liver, kidney, and inflammatory issues.

“Using a metric called the concordance index, we showed that the average performance of this new approach at predicting patient survival times was superior to that of standard statistical and machine learning methods,” said Dr Pan.

Validation and Broader Application

To test the robustness of their platform, the research team applied it to a separate dataset involving 1,441 people with non-small-cell lung cancer. Remarkably, the model generated nearly identical groupings in terms of both baseline traits and survival outcomes.

Dr Wang and Dr Li now plan further development and application of this approach, particularly for improving stratification in clinical trials of new cancer drugs. They also hope to use the tool to support more personalised treatment decisions in routine care.

Towards Understanding Disease Mechanisms

In addition to its immediate clinical utility, the model may also offer a new lens through which to explore cancer biology itself.

“We’ll probably need more than electronic health record data for this, but we do want to understand the biological mechanisms that explain these distinct patient subgroups,” said Dr Wang.

The research marks a significant step forward in the use of AI to enhance precision oncology, offering the promise of more targeted therapies and improved outcomes for people living with cancer.

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AI tool uses face photos to predict health and cancer outcomes

Key Takeaways:

• FaceAge, an AI tool developed at Mass General Brigham, uses facial photographs to predict biological age and survival outcomes in people with cancer.
• The algorithm outperformed clinicians in predicting short-term life expectancy in individuals undergoing palliative radiotherapy.
• FaceAge may become a novel, non-invasive biomarker to support personalised care decisions across cancer and potentially broader healthcare contexts.

Introduction: A New Frontier in Biomarkers

A team of researchers at Mass General Brigham has developed a deep learning tool known as FaceAge that estimates a person’s biological age and predicts survival outcomes using only a photograph of their face. The innovation demonstrates that even a simple facial image—such as a selfie—can reveal clinically significant insights about an individual’s health status and future prognosis, particularly in the context of cancer care.

The findings, published in The Lancet Digital Health, reveal that cancer patients generally appear biologically older than their actual chronological age, and that this discrepancy is strongly associated with poorer clinical outcomes.

“We can use artificial intelligence (AI) to estimate a person’s biological age from face pictures, and our study shows that information can be clinically meaningful,” said Dr Hugo Aerts, PhD, co-senior author and director of the Artificial Intelligence in Medicine (AIM) programme at Mass General Brigham.

The Link Between Appearance and Survival

The study found that individuals with cancer had an average FaceAge approximately five years older than their chronological age. Those whose predicted biological age appeared particularly elevated—especially beyond the age of 85—tended to have worse survival outcomes, even after adjusting for known factors such as sex, cancer type, and actual age.

“How old someone looks compared to their chronological age really matters—individuals with FaceAges that are younger than their chronological ages do significantly better after cancer therapy,” added Dr Aerts.

This observation supports longstanding clinical intuition: a patient’s appearance can often offer subtle indicators of their underlying health. However, human assessment is fallible and subject to unconscious bias. FaceAge provides a more objective, scalable, and potentially more accurate measure of biological ageing.

Testing the Algorithm: Cancer and Radiotherapy Cohorts

To build the FaceAge tool, researchers trained the algorithm on 58,851 images of presumed healthy individuals drawn from public datasets. They then tested the algorithm on 6,196 patients with cancer at two medical centres, using routine photographs taken at the beginning of radiotherapy treatment.

These evaluations showed a consistent pattern: cancer patients not only had higher FaceAges, but those with greater discrepancies between biological and chronological age had markedly poorer survival rates.

Outperforming Clinicians in Prognostic Judgement

One of the study’s most compelling insights emerged from a sub-study involving 100 patients receiving palliative radiotherapy. The researchers invited 10 clinicians and researchers to estimate short-term life expectancy using facial photographs and clinical context, including chronological age and cancer status.

Despite their experience, clinicians’ predictions were only marginally better than chance. However, when provided with the FaceAge data, their accuracy improved significantly.

“This work demonstrates that a photo like a simple selfie contains important information that could help to inform clinical decision-making and care plans for patients and clinicians,” said Dr Aerts.

A Glimpse Into the Future of AI-Driven Health Tools

While the results are promising, the research team cautions that FaceAge is not yet ready for clinical deployment. Ongoing and future research aims to:

• Validate FaceAge across different hospitals and populations
• Assess its utility in various stages of cancer
• Monitor how FaceAge changes over time
• Investigate its robustness against potential confounders, such as cosmetic procedures

“This opens the door to a whole new realm of biomarker discovery from photographs, and its potential goes far beyond cancer care or predicting age,” said Dr Ray Mak, co-senior author and faculty member in the AIM programme.

“As we increasingly think of different chronic diseases as diseases of ageing, it becomes even more important to be able to accurately predict an individual’s ageing trajectory. I hope we can ultimately use this technology as an early detection system in a variety of applications, within a strong regulatory and ethical framework, to help save lives.”

Contributors, Conflicts of Interest, and Funding

Study Authors:
The research was conducted by a multidisciplinary team from Mass General Brigham including Dennis Bontempi, Osbert Zalay, Danielle S. Bitterman, Fridolin Haugg, Jack M. Qian, Hannah Roberts, Subha Perni, Vasco Prudente, Suraj Pai, Christian Guthier, Tracy Balboni, Laura Warren, Monica Krishan, and Benjamin H. Kann.

Intellectual Property:
Mass General Brigham has filed provisional patents on two next-generation facial health algorithms developed in conjunction with this research.

Funding:
This study received funding from the following sources:

• National Institutes of Health (NIH-USA grants: U24CA194354, U01CA190234, U01CA209414, R35CA22052, and K08DE030216-01)
• European Union – European Research Council (Grant 866504)

Conclusion

FaceAge marks a significant advance in the intersection of AI and personalised medicine. By turning a simple image into a clinically valuable biomarker, this technology has the potential to support decision-making, improve prognostic accuracy, and ultimately enhance outcomes for people living with cancer and other ageing-related conditions.

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Google’s enhanced AI chatbot surpasses human doctors in diagnosing skin rashes and analysing medical images

Key Takeaways:

• Google’s experimental AI chatbot, AMIE, has demonstrated superior diagnostic accuracy compared to human physicians in simulated clinical consultations involving images and medical records.
• The updated system incorporates Google’s image-processing large language model Gemini 2.0 Flash, adapted for clinical reasoning without requiring extensive retraining.
• Experts acknowledge the chatbot’s promise but caution that real-world deployment faces significant challenges, and the system’s reproducibility remains limited due to missing technical details. 

AI-Powered Diagnosis Using Images

An advanced version of Google’s medical chatbot is showing promising capabilities in interpreting images to diagnose health conditions, including skin rashes from smartphone photographs. The system can now process a broader range of medical imagery—such as electrocardiograms and laboratory reports in PDF format—enhancing its diagnostic abilities across a wider clinical spectrum.

Previously, an earlier version of the artificial intelligence (AI) model had already demonstrated superior diagnostic performance and more empathetic communication than human physicians. The recent upgrade continues this trend, outperforming doctors in interpreting both visual data and patient-reported symptoms.

Introducing AMIE: Articulate Medical Intelligence Explorer

The upgraded system is known as the Articulate Medical Intelligence Explorer (AMIE). It remains experimental and has not yet undergone peer review, with the initial findings published on 6 May on the arXiv preprint server. According to Dr Eleni Linos, Director of the Stanford University Centre for Digital Health, who was not involved in the study, the integration of visual and clinical data “brings us closer to an AI assistant that mirrors how a clinician actually thinks”.

Testing Through Simulated Consultations

To evaluate AMIE’s clinical potential, researchers organised a series of simulated consultations involving 25 trained actors portraying patients. These individuals participated in virtual consultations with both AMIE and human primary-care physicians, covering 105 medical scenarios encompassing diverse symptoms, medical histories, and accompanying images relevant to each case.

After each consultation, both AMIE and the doctors were tasked with generating a diagnosis and treatment plan. Their responses were assessed by a panel of 18 specialists across dermatology, cardiology, and internal medicine, who reviewed transcripts and written summaries.

The outcome was striking: AMIE produced more accurate diagnoses overall, and its performance was notably resilient to challenges such as poor image quality.

Built on Gemini 2.0 Flash: A Shift in Training Methods

The latest iteration of AMIE is based on Google’s Gemini 2.0 Flash, a large language model (LLM) capable of processing both text and images. Rather than retraining the model using a traditional, labour-intensive method with domain-specific datasets, researchers introduced a more efficient adaptation process. They incorporated an algorithm designed to enhance clinical dialogue and reasoning, enabling the model to better replicate the structure of a diagnostic consultation.

To refine the model’s conversational behaviour, developers prompted it to simulate complete diagnostic interactions—alternating between the roles of patient, physician, and independent evaluator. According to Dr Ryutaro Tanno, a researcher at Google DeepMind and co-author of the study, this approach allows the AI to “sort of imbue it with the right, desirable behaviours when conducting a diagnostic conversation”.

“This is much cheaper and potentially more accessible,” Tanno added, highlighting a key advantage of the method compared to previous retraining approaches.

Expert Reactions and Limitations

Simulated patient scenarios are commonly used in medical education to assess human clinical skills. However, Dr Linos cautioned that such simulations cannot fully replicate the nuances of real-world clinical care. “Physicians bring experience, intuition and the ability to physically examine a patient—elements that are hard to replicate in a simulated script,” she said.

Dr Dan Zeltzer, a digital-health researcher at Tel Aviv University, recognised the AI’s potential but expressed concerns about transparency. He noted that the research paper lacks details about the specific code and prompt configurations used, which hinders the ability of others in the field to reproduce the results or build upon them.

Deploying this type of system in everyday clinical practice remains a considerable hurdle, according to Dr Xueyan Mei, an AI scientist at the Icahn School of Medicine at Mount Sinai in New York City. “That being said, we do think large language models for diagnosis would be the way to go in the future,” she remarked.

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Mayo Clinic and Phenomix study uses AI to predict GLP-1 side effects and advance personalised obesity treatment

Key Takeaways:

• A new study shows Phenomix Sciences’ machine learning algorithm can predict which individuals are more likely to experience nausea from GLP-1 agonist therapies, allowing for more tailored treatment.
• Participants with a high genetic risk score (GRS) were over twice as likely to develop nausea when treated with liraglutide, a common GLP-1 medication, compared with those with a low GRS (68% vs 30%).
• The findings highlight the potential for precision medicine to improve both patient outcomes and clinical trial efficiency, paving the way for more effective, personalised obesity care.

Detailed Findings from Digestive Disease Week 2025

Phenomix Sciences, a leader in precision obesity medicine and the first commercial biotechnology company of its kind, has unveiled new research in partnership with Mayo Clinic scientists that could reshape the future of obesity treatment. Presented at Digestive Disease Week (DDW) 2025, the study demonstrates how Phenomix’s proprietary machine learning algorithms can predict which individuals are more likely to experience side effects—particularly nausea—when undergoing GLP-1 receptor agonist therapy.

The study was led by Dr Andres Acosta, a globally recognised expert in obesity research and co-founder of Phenomix, and was presented by Dr Thomas Fredrick. Their research represents a pivotal step towards personalised obesity treatment and smarter clinical trial design.

Machine Learning Unlocks Predictive Insights

Titled “A Genetic Risk Score Associated with Nausea Resulting from GLP-1 Agonist Treatment: A Post-Hoc Analysis of a Randomised Controlled Trial of Liraglutide,” the study analysed genetic data from 110 participants enrolled in a previous trial. Researchers used a Genetic Risk Score (GRS) powered by machine learning to examine the relationship between individual genetic markers and adverse effects, focusing on nausea—a common and sometimes treatment-limiting side effect of GLP-1 therapies.

The results were striking. Individuals with a high GRS were more than twice as likely to experience nausea from liraglutide, a widely used GLP-1 receptor agonist, compared to those with a low GRS (68% versus 30%).

Nausea affects approximately 40% of people prescribed GLP-1 medications, and up to 6.4% may discontinue treatment as a result. By identifying likely adverse reactions before treatment begins, clinicians can minimise unnecessary hospital visits, reduce healthcare costs, and better align treatment plans to individual tolerability.

Implications for Personalised Care and Drug Development

“These findings represent a meaningful advancement in how we approach obesity treatment at an individual level,” stated Dr Acosta. “By identifying which patients are more likely to experience side effects before starting therapy, we can improve tolerability, support long-term adherence, and better match the right treatment to the right patient. This is a critical step toward delivering on the promise of truly personalised obesity care.”

Dr Fredrick added, “Our team’s research builds on previous findings by showing we can now predict not just who will benefit from GLP-1s, but who is more likely to struggle with side effects. That allows for more balanced, individualised treatment planning. It is an important advancement in the clinical application of precision obesity medicine.”

Phenomix’s MyPhenome® test, a simple saliva swab introduced previously at DDW 2024, identifies biological contributors to obesity, enabling physicians to personalise treatment strategies. Last year’s research demonstrated that MyPhenome could identify likely responders to semaglutide. This new study refines that approach by predicting who might encounter tolerability challenges, further enhancing the precision of obesity care.

A Boost for Clinical Trials and Beyond

Mark Bagnall, CEO of Phenomix Sciences, highlighted the broader implications: “This study underscores the power of predictive tools like MyPhenome to transform how we approach obesity treatment—not just in the clinic, but in the drug development pipeline. By identifying patients at risk for side effects before treatment begins, we can match the right patient to the right therapy, increase real-world adherence, and dramatically improve clinical trial efficiency through smarter patient selection. Our strategic partnership with Mayo Clinic, and its dedicated research team including Drs Acosta and Fredrick, have been critical in validating this precision medicine approach.”

The study was one of 17 presented by Mayo Clinic researchers at DDW 2025, with eight studies integrating Phenomix’s machine learning algorithms. Collectively, this research underscores the increasing importance of precision medicine in tackling obesity and accelerating drug development.

Collaborators and Acknowledgements

The study was co-authored by a multidisciplinary team: Dr Thomas Fredrick; Dr Jessica Atieh; Dr Daniel B. Maselli; Dr Diego Anazco; Dr Lizeth Cifuentes; Dr Maria A. Espinosa; Dr Jose Villamarin; Deborah Eckert BSN; Dr Serban Ciotlos; Dr Timothy O’Connor; Dr Michael Camilleri; and Dr Andres Acosta.

For further details about Phenomix Sciences and its research, please visit phenomixsciences.com. A full list of studies presented at DDW 2025 can be accessed here.

About Phenomix Sciences

Phenomix Sciences is a pioneering precision obesity biotechnology company focused on transforming obesity care by putting individuals at the centre of therapeutic innovation. Through advanced genetic testing, sophisticated analytics, and exclusive technology licensed from Mayo Clinic, Phenomix delivers personalised insights that empower physicians to optimise obesity treatments. These insights also assist pharmaceutical and medical device companies in refining trials, identifying high-responder groups, and accelerating the development of more targeted therapies. Backed by Health2047, the innovation arm of the American Medical Association, Phenomix is committed to shaping a more personalised and impactful future for obesity treatment.

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AI-powered heart scans speed up diagnosis and save millions

Key Takeaways:

• AI-powered HeartFlow analysis has reduced unnecessary invasive heart tests by up to 16% and cut follow-up testing by 12%, enhancing patient care and NHS efficiency.
• Since 2021, the tool has been used across 56 hospitals in England, saving the NHS an estimated £9.5 million and benefiting over 24,300 patients.
• The technology transforms CT scans into personalised 3D heart images, enabling faster, more accurate diagnoses and allowing many patients to avoid invasive procedures.

A ground-breaking artificial intelligence (AI) tool that streamlines the diagnosis of coronary heart disease (CHD) has saved the NHS millions of pounds, while also reducing the need for invasive procedures, according to new research.

How the Technology Works

The HeartFlow technology, introduced in 56 hospitals across England since 2021, uses AI to convert a standard computed tomography (CT) scan into a personalised three-dimensional image of a person’s coronary arteries. This detailed model allows clinicians to diagnose and manage suspected heart disease more quickly and with greater precision.

Key Findings from the Study

A major study published in Nature Medicine on 4 April 2025 has demonstrated the tool’s substantial impact. Researchers found that the use of HeartFlow reduced the need for invasive coronary angiograms by 16% in instances where no further treatment was necessary, and by 7% overall. Additionally, the number of follow-up heart tests required within two years fell by 12%.

Dr Vin Diwakar, National Director of Transformation at NHS England, praised the findings:

“It is fantastic to see that these revolutionary AI-driven 3D heart scans, supported by NHSE, are transforming cardiac care by significantly reducing the need for invasive tests, speeding up diagnoses, conserving NHS resources, and enabling clinicians to advise patients on the best treatment for their condition.”

Study Scope and Impact

The research, funded by the Medical Research Council, tracked the outcomes of 90,000 NHS patients between 2017 and 2020. Nearly 8,000 of these patients underwent HeartFlow analysis. The results indicated that personalised imaging not only reduced unnecessary and potentially risky procedures, but also improved diagnostic accuracy, thereby increasing the number of people receiving timely treatment for heart disease.

Dr Timothy Fairbairn, lead clinician on the study and Consultant Cardiologist at the Liverpool Heart and Chest Hospital, explained:

“These results show that this technology reduces the need for tests so that patients only undergo necessary treatments, demonstrating how AI technology can both improve care as well as increase efficiency in the NHS.

The nationwide study, funded by the Medical Research Council, also showed that the huge benefits of this tool can be felt by all patients equally, no matter where they live.”

Financial and Clinical Benefits

The NHS estimates that more than 24,300 patients have benefited from the technology since its rollout, saving approximately £9.5 million to date—an average saving of around £390 per patient.

Coronary heart disease remains the most prevalent form of cardiovascular disease, affecting approximately 2.3 million people in England. Traditionally, individuals with suspected CHD would undergo a CT scan, and if narrowing or blockage of a coronary artery was suspected, they were often referred for an invasive coronary angiogram to confirm the diagnosis.

Improving Patient Care and Personalisation

HeartFlow allows many individuals to be managed through medication and lifestyle interventions, with invasive procedures recommended only if surgery or stent placement may be necessary. The AI tool can also advise on the optimal size and positioning of a stent for each person, further personalising treatment.

Supporting Equitable Access to Innovation

The deployment of HeartFlow was supported by NHS England through the MedTech Funding Mandate programme. This initiative is designed to fast-track the adoption of clinically effective and cost-saving medical technologies, ensuring that both patients and the healthcare system benefit swiftly and equitably.

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NHS Cancer 360 tool aims to reduce administrative burden and speed up treatment

Key Takeaways:

• Cancer 360, integrated within the NHS Federated Data Platform (FDP), offers clinicians a comprehensive dashboard to track cancer patient progress, identify delays, and support personalised care plans.
• The tool has already shown success in pilot sites, reducing administrative workload and expediting patient care coordination.
• Cancer 360 is part of a wider NHS digital transformation drive, backed by significant government investment, aiming to improve outcomes and operational efficiency across the healthcare system.

The Department of Health and Social Care (DHSC) has announced the national rollout of a new cancer diagnostic tool, Cancer 360, embedded within the NHS’s Federated Data Platform (FDP). This technology aims to accelerate diagnosis and treatment, reduce delays, and ultimately improve survival rates for people living with cancer across England.

Cancer 360 operates as a centralised dashboard, providing clinicians with a unified view of each patient’s journey. It draws together key information such as diagnostic test results, upcoming appointments, and treatment records. By consolidating data streams, the tool is designed to streamline clinical decision-making and enhance the quality of care provided.

Dr Vin Diwakar, NHS National Clinical Transformation Director, stated:
“Every cancer patient deserves swift, effective care, and our new Cancer 360 solution harnesses data to ensure exactly that. By giving clinicians a comprehensive view of patient pathways, we can identify and address delays immediately. The NHS FDP is already showing its value in transforming cancer care, helping our hard-working staff deliver better outcomes while reducing administrative burden.”

According to the DHSC’s official press release on 4 May 2025, Cancer 360 is expected to benefit millions of people with cancer over the next five to ten years. Its core purpose is to allow care teams to track patient progress efficiently, flag potential delays early, and design personalised treatment strategies tailored to each individual’s needs. Additionally, it promises to cut down on time-consuming paperwork, ensuring that critical clinical warning signs are promptly identified and acted upon.

Pilot implementations of the tool have already taken place at Chelsea and Westminster Hospital NHS Foundation Trust and Royal United Hospitals Bath NHS Foundation Trust.

Suraiya Abdi, Consultant Obstetrician and Gynaecologist at Chelsea and Westminster Foundation Trust, shared her experience:
“The implementation of Cancer 360 has enabled my team to monitor and safely carry our patients through their cancer pathway. The tool enables us to have in-depth conversations at our weekly meetings regarding a patient’s next step as well as allowing us to escalate queries directly to other teams for faster turnaround. The tool has reduced the amount of admin time spent by our cancer team therefore enabling them to focus on the patient journey.”

This innovation forms part of a wider £1 billion investment in digital transformation projects from the Autumn Budget, which also allocated £121 million specifically for the FDP. The DHSC has reported that hospitals using the FDP have delivered impressive results since April 2024, including performing approximately 70,000 additional procedures and reducing unnecessary hospital stays by nearly 19%.

The rollout of Cancer 360 is just one component of the broader FDP initiative. In February 2025, NHS England announced the launch of three additional FDP-powered services slated for 2025:

• Patient Led Validation, designed to empower people by enabling them to confirm their own waiting list status, improving both transparency and patient experience.
• Diagnostics Imaging Scheduler, which will streamline diagnostic workflows and optimise appointment scheduling.
• Timely Care Hub, a platform offering a real-time, centralised view of ward and site activities to support better-informed decisions and more coordinated care.

Moreover, Chelsea and Westminster Hospital NHS Foundation Trust is currently conducting user acceptance testing for an Referral to Treatment (RTT) clinical letters validation tool within the FDP, as noted in the April 2025 NHS England FDP bulletin. According to the bulletin, 108 hospital trusts and 41 integrated care boards have now signed up to the FDP, demonstrating broad national commitment to this digital transformation effort.

In line with this, the NHS England 2025/26 Priorities and Operational Planning Guidance, published in January 2025, mandates that all systems must adhere to an ‘FDP first’ policy, ensuring that their digital and data infrastructure is integrated with the FDP.

By embedding Cancer 360 within the NHS’s digital framework, the government and NHS leadership aim not only to improve immediate clinical outcomes but also to lay the groundwork for a more agile, data-driven healthcare system. This initiative underscores a clear commitment to enhancing patient-centred cancer care through modern, technology-enabled solutions.

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