Digital Health Is Redefining Patient Expectations of Care
Key Takeaways:
- Consumers used to personalised digital experiences in banking, retail and entertainment now expect the same convenience and personalisation from healthcare, according to a 2024 Deloitte study.
- Patients are already using digital health services widely: in Spain, 72.48% of survey respondents had used online medical appointments, and older patients and caregivers valued patient portals for their convenience.
- A 2026 OECD report found that patients who have to repeat information that should already be in their health records report worse experiences of trust, quality of care and person-centred care.
The overlooked patient perspective
Professional forums are increasingly focusing on how digital health can contribute to clinical workflows, from automating administrative tasks and analysing imaging studies to segmenting patient groups. However, these discussions often overlook the role of patients themselves, and how digital health is changing their expectations and perceptions of their own healthcare needs.
The growing availability of services that combine effectiveness with patient care and convenience may also be influencing what patients expect from the healthcare system as a whole.
Convenience as a competitive advantage
A 2024 Deloitte study indicated that healthcare systems could gain a competitive advantage by ensuring that their virtual health offerings prioritise convenience and respond to consumer preferences.
According to the report, “today’s consumers, accustomed to highly personalized virtual experiences in banking, retail, and entertainment, now expect the same level of convenience and personalization in their interactions with the healthcare system.”
This shift highlights a key challenge for the healthcare sector: aligning organisational strategies with growing consumer demand for accessible digital health solutions.
For healthcare professionals looking to understand how digital and AI-driven tools are entering everyday practice, The College of Contemporary Health’s AI Essentials for Primary Care short course offers a practical starting point for building confidence in this rapidly evolving area.
Private investment in digital health
Private sector companies are also increasing their investment in digital health.
One example is the recent acquisition of Reimagine Care, a US company that provided virtual care to people with cancer outside the hospital setting, by Cureety, a French healthtech company specialising in the remote monitoring of people with cancer. In 2023, Cureety became the first company in France to receive national reimbursement for the remote monitoring of people with cancer.
Among other features, the company offered a patient interface that supported:
- simple, user-friendly symptom reporting
- secure and ongoing communication with healthcare teams
- reminders
- access to educational content selected and adapted to each person’s treatment and stage of disease
The platform also supported the digitisation and automation of care processes, the monitoring of treatment-specific toxicity, patient classification, the generation of real-world data, and the delivery of specialised educational content to both patients and healthcare professionals at the appropriate point in the care process. Taken together, these functions illustrate how digital health services are evolving.
How patients are using digital health
The question, then, is whether these new approaches to healthcare are changing what patients seek. Several studies have highlighted the value that patients place on online health platforms.
A recent article in the Journal of the American Geriatrics Society examined patient and caregiver expectations regarding the use of patient portals in geriatric care. Users considered patient portals beneficial, particularly because they were convenient and allowed them to avoid in-person care.
In Spain, data from the national survey “Supply and Demand for Users of eHealth Services in Spain”, published in the Journal of Medical Internet Research in 2023, showed that online medical appointments were the most widely used eHealth service. Overall, 72.48% of respondents had used online medical appointments at some point.
The question of trust
While convenience is one consideration, trust is another fundamental issue. Greater access to information that is readily available and easier to understand could strengthen patients’ trust in healthcare professionals and in the healthcare system more broadly.
The 2026 report Building People-Centred Digital Health Systems from the Organisation for Economic Co-operation and Development (OECD) concluded that “When patients have to repeat information that should already be available in their health records, they report significantly worse experience across key patient indicators: trust in the healthcare system, experienced quality of care, and person-centered care.”
However, the report also noted: “Truly patient-centered digital health depends not only on electronic systems but also on patient awareness of access rights, practical tools to retrieve and use data, support for health and digital literacy, and policy frameworks that prioritize patient control over the use of their own health information in line with the OECD Health Data Governance Recommendation.”
Redefining quality care
The debate, therefore, centres on whether healthcare systems need to pursue greater resolution of patients’ needs while also developing a new understanding of what constitutes quality care in a digital age.
This article was translated from El Médico Interactivo on Univadis, part of the Medscape Professional Network.
CCH insight
As patient expectations shift towards convenience, personalisation and seamless access to information, healthcare professionals have a growing role in helping digital tools work well for the people they care for. Our AI Essentials for Primary Care CPD short course helps primary care professionals build a practical understanding of AI and digital innovation in everyday practice.
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Source: Medscape
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Adapting the Machine to the Medicine: New Review Shows How to Make Clinical AI Work
Key Takeaways:
- A new review in the Journal of Medical Internet Research analysed 35 recent studies and found that off-the-shelf large language models must be adapted for medical settings before they can be relied upon for diagnosis, triage and treatment planning.
- Performance depends on the clinical task: retraining the model on task-specific data suited narrow jobs such as detecting cancer in medical images, while connecting the model to live, trusted databases worked well for reasoning through complex guidelines. Hybrid systems combining both performed best of all.
- Most of the evidence to date comes from historical medical records rather than live clinical use, so prospective, real-world testing remains essential before widespread hospital adoption.
Why raw capability is not enough
Generative artificial intelligence has arrived in health care with considerable fanfare, but a new review suggests that the decisive factor is not the sheer power of the underlying model. It is what happens after the model is built. A review study published in the Journal of Medical Internet Research by JMIR Publications concludes that adapting existing language models to the clinical environment is the key to ensuring they can safely and effectively support diagnosis, patient triage and treatment planning in real-world settings.
The research team, led by Anshum Patel, MD, and Joseph Y Cheung, MD, MS, analysed 35 recent studies to understand how different customisation methods affect AI performance. Their conclusion was that standard large language models (LLMs) are undoubtedly capable, but capability alone does not translate into clinical reliability. To be trusted in a consulting room or on a ward, a model has to be tailored specifically for the medical environment in which it will be used.
What adaptation actually looks like
The review examined the two broad routes clinicians and developers can take. The first is retraining, in which a general-purpose model is further trained on specific clinical data or guidelines so that it internalises the patterns and standards of a particular task. The second is connection, in which the model is linked directly to trusted medical databases and draws on that source material at the point of use rather than relying solely on what it absorbed during training.
Both approaches produced meaningful gains. When models were connected directly to trusted medical databases or retrained on specific clinical guidelines, accuracy greatly improved, with some systems matching the diagnostic performance of human doctors. That is a striking benchmark, and it underlines the central argument of the review: the adaptation step is not a technical afterthought but the point at which a general tool becomes a clinical one.
The right method depends on the task
The most successful approach was found to depend heavily on the specific medical task at hand, and this is arguably the review’s most practical finding for anyone evaluating these tools.
For narrow, focused tasks such as detecting cancer in medical images, retraining the AI on specific data worked best. Where the job is well defined and the data are consistent, teaching the model directly on that material produces the sharpest results.
For tasks that require reasoning through complex guidelines, linking the AI to live databases was highly effective. Clinical guidance changes, and a model that consults an authoritative, current source is far better placed than one working from a fixed snapshot of its training data.
However, the researchers determined that the best performance came from hybrid systems, which combine both methods to manage complicated workflows such as stroke triage and oncology cases. These are precisely the situations in which speed, protocol adherence and nuanced judgement all matter at once, and where people presenting with time-critical or complex conditions stand to gain the most from well-designed decision support.
“There is no single best way to adapt AI for health care. The right approach depends on the clinical task, and the next step is making sure these systems are safe, reliable, and useful in real-world patient care,” says Anshum Patel.
Implications for clinical teams
For healthcare professionals, the message is not that they need to become engineers. It is that the questions worth asking about any AI tool offered to a service are increasingly practical ones: what was this model adapted for, what source material informs its outputs, how current is that material, and has it been tested on a population and a workflow resembling ours?
That kind of informed scrutiny is becoming part of everyday clinical literacy, and it is one reason structured professional development in this area has grown in demand. The College of Contemporary Health’s AI Essentials for Primary Care short course is designed for exactly this purpose, helping clinicians understand how these systems are built, where their limitations lie, and how to appraise them responsibly within their own practice.
The evidence gap that still needs closing
While these findings are promising, the researchers note that the vast majority of studies on these AI systems are based on past medical records rather than live patient testing. Retrospective analysis can demonstrate that a system performs well against a tidy historical dataset; it cannot show how that system behaves when confronted with incomplete notes, atypical presentations, or the ordinary time pressures of a busy department.
Before these advanced tools are widely adopted in hospitals, additional prospective, real-world testing is needed to guarantee patient safety and ensure the technology works reliably across different clinical environments. Until that evidence accumulates, the sensible position is one of informed optimism – recognising the genuine potential of adapted clinical AI while insisting that it earns its place through testing in the settings where people actually receive care.
CCH insight
Artificial intelligence is moving quickly from conference agendas into everyday clinical workflows, and the professionals best placed to use it well are those who understand both its strengths and its blind spots. AI Essentials for Primary Care is a CPD-accredited short course from The College of Contemporary Health, built for clinicians who want a clear, practical grounding in how AI tools work, how to evaluate them critically, and how to apply them safely in patient-facing practice.
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Source: Journal of Medical Internet Research
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Cough Data Captured During Sleep Could Flag Flu and COVID-19 Surges a Week Early
Key Takeaways:
- The UK Health Security Agency (UKHSA) and AI sleep technology company Sleep Cycle have published research showing that cough data collected passively by a smartphone sleep app closely reflects levels of respiratory illness reported through NHS 111 in England.
- Rises in night-time coughing were often observed around a week before increases in influenza and COVID-19 activity, offering an early signal that is not shaped by healthcare-seeking behaviour, laboratory turnaround times or reporting delays.
- The cough signal was found to be robust and regionally consistent, supporting a role for privacy-preserved consumer digital health data alongside, rather than instead of, established public health surveillance systems.
A new kind of respiratory signal
A study published by the UK Health Security Agency (UKHSA) and the AI sleep technology company Sleep Cycle has evaluated whether cough data gathered by a sleep app can give early indications of rising respiratory illness in England. The full results are available on medRxiv.
The researchers concluded that passively collected data of this kind can provide a robust and regionally consistent indicator of community respiratory illness, while also providing early signals for influenza and COVID-19 activity. Taken together, the authors argue, the findings support a wider role for digital health data in public health surveillance.
Crucially, the cough data was not gathered by asking anyone to report symptoms. It was generated automatically overnight, from the sound analysis that the app already performs as part of its everyday function.
Why existing surveillance has blind spots
Current respiratory surveillance in England depends heavily on people seeking care through the NHS. That dependence introduces a series of variables that have nothing to do with how much illness is actually circulating in a community.
Levels of public awareness can change how quickly people make contact with a service. The availability of that service matters too, as do demographic and socioeconomic differences between the populations being observed. Two areas with similar levels of illness may therefore generate very different surveillance figures.
Timing is a further constraint. Established systems are also affected by reporting and laboratory processing times, which means the picture available to public health teams is always, to some degree, a picture of the recent past rather than the present moment.
How the sleep app signal is generated
Sleep Cycle is a smartphone app designed to help people understand and improve their sleep through AI-powered sound analysis. The app listens during the night and interprets the sounds it detects, including coughing.
Unlike traditional surveillance, Sleep Cycle’s cough signal is generated automatically during normal sleep using privacy-preserved, passively collected data, and it is updated daily. That combination produces a near real-time view of respiratory illness activity, refreshed without anyone needing to complete a form, book an appointment or wait for a test result.
Because the data is produced as a by-product of ordinary app use, it sidesteps several of the behavioural and administrative filters that sit between illness in a community and the figures that reach public health teams.
A week’s head start on flu and COVID-19
The researchers found that cough data collected through the app closely reflected levels of respiratory illness reported through NHS 111. The two measures moved together, which is the first thing any candidate surveillance signal has to demonstrate.
More significantly, the two measures did not move together at the same moment. Increases in coughing were often observed around one week before increases in influenza and COVID-19 activity. The authors highlight this as evidence that passive digital health data can provide earlier situational awareness alongside established surveillance systems.
A week is a meaningful interval in respiratory season planning. It is time that can be used to prepare staffing, communicate with the public, and anticipate pressure on services rather than respond to it.
The findings suggest that, used alongside established surveillance systems, passive sleep monitoring could provide a fuller picture of respiratory surveillance data, helping public health experts better understand seasonal trends sooner.
What the researchers said
Professor Steven Riley, Chief Data Officer at UKHSA, said:
“These findings suggest that combining established surveillance approaches with novel digital health signals could contribute to an earlier, richer and more resilient understanding of population respiratory health.
No single surveillance system provides a complete picture of respiratory disease activity, but this shows that passive nocturnal cough monitoring can complement other surveillance systems to provide a timely population-level signal of upcoming disease trends, without being affected by healthcare-seeking behaviour, laboratory turnaround times, backfilling and reporting delays.”
Dr Emil Carlsson, Research Scientist and Co-lead Author, said:
“This study demonstrates that passively collected nightly cough data captures meaningful changes in community respiratory illness.
Equally important, it shows that consumer-generated health data can be transformed into epidemiologically meaningful surveillance signals using rigorous scientific methods while maintaining strong privacy protections.”
Dr Mikael Kågebäck, Chief Technology Officer and Acting Chief Executive Officer at Sleep Cycle, said:
“This study validates a completely new category of health data.
For the first time, we’ve demonstrated that passively generated smartphone data can produce robust population-level health intelligence at national scale, while also providing earlier signals for influenza and COVID-19 activity.
That creates opportunities to strengthen public health surveillance and enable researchers, healthcare organisations and industry partners to build new services for situational awareness and operational decision support.”
Complement, not replacement
Both organisations frame the work in the same way. The cough signal is not presented as a substitute for laboratory confirmation, clinical reporting or NHS 111 data. It is presented as an additional layer that behaves differently from the others, and whose value lies precisely in that difference.
As Professor Riley notes above, no single surveillance system provides a complete picture. A signal that is unaffected by whether people choose to seek care, and unaffected by how long a laboratory takes to process a sample, fails in different ways from the systems already in place. Combining sources with different weaknesses is a well-established way of building a more resilient overall view.
What it means for clinicians and health organisations
For practitioners, the study is a practical illustration of a broader shift. Data generated by consumer devices is increasingly being assessed against the standards applied to conventional health data, and in this case it held up well enough to warrant serious attention from a national public health body.
That shift raises questions clinicians and managers are likely to encounter more often: how such signals are validated, what privacy protections are in place, how to judge the reliability of a consumer-generated dataset, and where these tools genuinely add value to decision-making. Professionals looking to build confidence in this area may find structured CPD useful, and The College of Contemporary Health’s short course AI Essentials for Primary Care is designed to help healthcare professionals understand how AI-driven tools are being applied in practice and how to appraise them critically.
A note on the status of this research
This was reported in medRxiv, The Preprint Server for Health Sciences. The following caution applies, as it does to all preprints:
Preprints are preliminary reports of work that have not been certified by peer review. They should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
The findings described here should therefore be read as promising and carefully conducted early-stage research rather than as settled evidence. Peer review may yet refine the conclusions, the size of the reported lead time, or the conditions under which the signal performs best.
CCH insight
Digital health signals are moving quickly from novelty to everyday practice, and healthcare professionals are increasingly expected to interpret them with the same rigour they apply to any other source of clinical or population data.
AI Essentials for Primary Care is a CPD short course from The College of Contemporary Health, developed for healthcare professionals who want a clear, practical grounding in how artificial intelligence is being used across healthcare settings and how to evaluate it responsibly.
Explore AI Essentials for Primary Care →
Source: GOV.UK
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AI Blood Test Could Detect Early Eye Nerve Damage in People with Type 2 Diabetes
Key Takeaways:
- An AI tool called Pro-DRN uses a blood sample to flag people with type 2 diabetes at high risk of diabetic retinal neurodegeneration (DRN), before any damage shows on the retina.
- It was trained on 1,218 participants and validated in 502 people from UK Biobank, identifying 71 proteins linked to DRN – with ACTA2, COL6A3 and HSPG2 the strongest predictors.
- As retinal nerves are among the first tissues affected by diabetes, the test could also hint at wider nerve damage and help target earlier monitoring and future treatments.
A simple blood test to catch nerve damage early
Scientists have developed an AI-assisted prediction tool that can identify people with type 2 diabetes who are at high risk of developing diabetic retinal neurodegeneration (DRN) before symptoms appear. The findings were published in the journal PLOS Medicine.
The work was led by Wei Wang, MD, PhD, associate professor at the Guangdong Provincial Clinical Research Center for Ocular Diseases. According to the authors, the damage that diabetes inflicts on the delicate nerves of the eye appears to leave a detectable molecular trail in the bloodstream long before it becomes visible in the eye itself.
“Our study suggests that early retinal nerve damage in diabetes leaves measurable signals in the blood,” write the authors. “These findings suggest that a simple blood test analyzed with artificial intelligence may help identify people with diabetes who are at highest risk of early retinal nerve damage, well before visible damage appears on the retina.”
Why the retinal nerves matter in diabetes
Type 2 diabetes affects more than half a billion people worldwide, and it carries an increased risk of long-term complications, including progressive neurodegeneration – the gradual deterioration of nerve tissue over time.
The nerves of the retina are among the earliest tissues to be affected. As this damage advances, it can eventually lead to severe visual impairment and the loss of sight. The difficulty for clinicians is one of timing: current diagnostic methods can only detect DRN once the retina has already sustained irreversible damage. By the time the problem is visible, the window for early, protective intervention has often closed.
How the Pro-DRN tool was built
To address this, Wang and colleagues developed a machine learning algorithm called Pro-DRN. They drew on data from 1,218 participants in the Guangzhou Diabetic Eye Study, all of whom had been diagnosed with type 2 diabetes but had not yet developed DRN at the point of enrolment.
The model combined two distinct streams of information. The first was proteomics data – a detailed read-out of the proteins circulating in participants’ blood samples. The second was a series of yearly retinal images, capturing the state of the eye over a six-year follow-up period. By matching the molecular signals in the blood against how each person’s retina changed year on year, the algorithm learned which blood-borne patterns preceded the onset of nerve damage.
The proteins behind the predictions
The analysis surfaced 71 proteins associated with the development of DRN. Of these, three stood out as the most consistent drivers of accurate prediction: ACTA2, COL6A3 and HSPG2. These are key structural components involved in maintaining the integrity of the nerve and muscle tissue in the eye, which helps explain why disturbances in their levels might signal nerve tissue under strain.
Crucially, the team did not rely on a single dataset. The results were validated in an independent cohort of 502 people from UK Biobank, where the core effects and protein signals were reproduced – an important check that the findings were not simply a quirk of the original group.
From research tool to clinical aid
Pro-DRN has been made available as an interactive, web-based risk assessment tool that clinicians can use to support early DRN screening and to monitor how a person’s risk evolves over time. People identified as being at high risk could then benefit from more frequent check-ups and from early interventions aimed at preventing or slowing progressive neurodegeneration, rather than waiting for damage to become apparent.
A window into the wider nervous system
The potential significance of the test reaches beyond the eye. Because DRN is one of the first signs of nerve degeneration brought on by diabetes, detecting it early could also signal the onset of nerve injury elsewhere in the body.
Such damage can contribute to cognitive impairment, dementia and peripheral neuropathy – the latter causing loss of sensation and motor control in the hands, feet and other extremities. Viewed this way, a single eye-focused test could offer valuable insight into the overall health of a person’s nervous system.
New possibilities for treatment and trials
The discoveries also open up two further avenues. The proteins identified as being involved in DRN progression could be investigated as potential targets for the development of new therapies. In addition, the AI-based tool could prove useful for selecting and stratifying participants in clinical trials that are evaluating neuroprotective strategies designed to prevent or delay nerve damage – helping ensure such studies enrol the people most likely to show a measurable benefit.
Looking ahead
For the researchers, the broader ambition is a shift in how diabetic eye care is approached – from reacting to damage that has already occurred towards anticipating who is most vulnerable.
“Pro-DRN may help move diabetic eye care from detecting established damage toward earlier, molecularly informed risk stratification, so that closer monitoring and future neuroprotective interventions can be directed to the people most likely to benefit,” Wang and colleagues write.
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AI Models Identify Hidden Cardiac Arrest Risk in Routine Patient Data
Key Takeaways:
- Researchers have built AI models that analyse electronic health records and electrocardiograms to identify people at elevated risk of sudden cardiac arrest, which kills more than 400,000 Americans each year.
- In a real-world group of nearly 40,000 patients, the combined model correctly flagged 153 of 228 high-risk people who later experienced cardiac arrest, narrowing risk prediction from 1 in 1,000 to 1 in 100.
- The models also surfaced modifiable contributors such as electrolyte disorders, substance use and medication interactions, pointing to practical opportunities for clinicians to intervene.
A new approach to an unpredictable emergency
Researchers have developed artificial intelligence (AI) models capable of analysing electronic health records (EHR) and electrocardiograms to pinpoint people in the general population who face a heightened risk of sudden cardiac arrest. The condition is responsible for more than 400,000 deaths each year in the United States and carries a survival rate of just 10%, making any tool capable of forecasting it a meaningful step forward.
The work represents a notable advance in anticipating an event that is widely considered difficult, if not impossible, to predict, and which often strikes people who have no previously known heart disease.
“Using artificial intelligence applications and health records data, the prediction of cardiac arrest in the general population is feasible,” said Dr Neal Chatterjee, the study’s lead investigator and a cardiologist at the University of Washington School of Medicine.
The paper was published on 11 May in JACC: Advances, a journal of the American College of Cardiology. Additional co-senior authors are affiliated with Massachusetts General Hospital and the Broad Institute of MIT and Harvard.
How the models were built
The investigation drew on a test population of roughly 1.7 million patients enrolled in a large healthcare system in the United States. The team built three separate AI models, each trained on a distinct dataset. The first, referred to as “EKG-only,” relied solely on electrocardiogram readings. The second, “EHR-only,” weighed 156 clinical features drawn from patients’ health records. The third combined both EKG and EHR data into a single integrated model.
The researchers developed and validated their models across three distinct patient groups.
Training cohort
The models were initially trained using data from 993 people who had experienced out-of-hospital cardiac arrest between 2013 and 2021, alongside 5,479 control patients matched for age and sex who had not. This stage allowed the AI to learn which patterns in EHR entries and EKG readings were linked to a higher risk of cardiac arrest.
Testing cohort
To confirm that the models could reliably distinguish between high- and low-risk indicators, the researchers applied them to a separate group consisting of 463 cardiac arrest cases from 2022 to 2023 and 2,979 control patients. The risk associations identified in this testing group closely mirrored those established during training.
Real-world cohort
The final stage involved 39,911 people who had received EKGs during 2021, regardless of their health status. The researchers examined the records of those within this group who went on to experience cardiac arrest over the following two years, assessing how closely their profiles aligned with the risk patterns identified by the models.
Within this real-world group, the combined EHR-EKG model accurately predicted 153 of 228 people who were classified as high-risk and who later went on to experience a cardiac arrest.
Bringing theoretical risk into focus
The shift in predictive precision is one of the study’s most striking outcomes.
“With these models, we’re able to enrich risk prediction from about 1 in 1,000 down to 1 in 100,” Chatterjee said. “If your doctor were to tell you that your risk of cardiac arrest is 1 in 100, that would catch your attention. We’re bringing a theoretical risk into focus.”
Another encouraging finding concerned the performance of the EKG-based model on its own. AI-enhanced analysis of electrocardiograms alone demonstrated strong predictive ability, only modestly behind the two models that drew on EHR data.
“The 12-lead EKG is a low-cost tool that might stratify patients’ risk for cardiac arrest in any community around the world,” Chatterjee said.
Risk factors beyond traditional cardiology
The study also surfaced risk factors that lie outside the conventional cardiovascular picture. Contributors flagged by the models included electrolyte disorders, substance use and interactions between medications, all of which are often addressable through clinical attention.
“We show some relatively low hanging fruit … modifiable risk factors,” Chatterjee noted. “A model that flags a patient as high-risk might prompt somebody taking care of a patient to review their medical history and their medications.”
Open questions for clinical practice
While the results demonstrate that predicting cardiac arrest risk at the population level is achievable, Chatterjee was careful to note that the next stage of inquiry involves working out what clinicians should actually do once a patient is flagged.
“We need to figure out which follow-on studies to pursue to understand what we do with this patient information. What screening, what surveillance, what intervention is warranted?”
Limitations of the study
Several constraints temper the findings. All of the data was drawn from a single healthcare system, leaving open the question of whether the models would perform similarly across populations with different demographic profiles or patterns of care. The real-world group was also restricted to people who had received an EKG, and these individuals may differ in important ways from those who had not undergone such testing. In addition, the AI-enhanced interpretations of EKGs could reflect biases tied to demographics or to the way care is delivered.
Funding and support
The research received support from the National Institutes of Health (K23HL169839, R01 HL160003, R01 HL168889, K24 HL153669, R01HL092577, R01HL157635), the American Heart Association (23CDA1050571, 961045), the European Union (MAESTRIA 965286) and the Foundation Leducq (24CVD01). Chatterjee is supported through a philanthropic donation from Kevin and Ann Harrang and through the John and Cookie Laughlin Endowed Professorship.
Source: UW Medicine
AI Analysis of Health Records May Help Identify ADHD Risk Years Earlier
Key Takeaways:
- Artificial intelligence can analyse routine electronic health records to estimate a child’s risk of developing ADHD years before diagnosis.
- The model demonstrated strong accuracy across diverse populations, using data from more than 140,000 children.
- The tool is designed to support earlier evaluation and intervention, not to replace clinical diagnosis.
AI and the challenge of delayed ADHD diagnosis
Attention-deficit/hyperactivity disorder (ADHD) affects millions of children worldwide. Despite its prevalence, many children experience significant delays before receiving a formal diagnosis. This can limit access to timely support, even when early signs are present.
New research from Duke Health suggests that artificial intelligence may help address this gap. By analysing routinely collected electronic health records, researchers have developed a tool capable of identifying patterns that indicate a higher likelihood of future ADHD diagnosis, potentially years in advance.
Unlocking insights from routine healthcare data
The study, published in Nature Mental Health on April 27, demonstrates how existing healthcare data can be used to support earlier clinical decision-making.
“We have this incredibly rich source of information sitting in electronic health records,” said Elliot Hill, lead author of the study and data scientist in the Department of Biostatistics & Bioinformatics at Duke University School of Medicine. “The idea was to see whether patterns hidden in that data could help us predict which children might later be diagnosed with ADHD, well before that diagnosis usually happens.”
Rather than relying on new or specialised testing, the approach draws on information already collected during standard healthcare visits. This includes developmental milestones, behavioural observations, and clinical events recorded from birth through early childhood.
How the AI model was developed
To build and test the model, researchers analysed electronic health records from more than 140,000 children, including both those diagnosed with ADHD and those without the condition.
The AI system was trained to identify combinations of factors that tend to appear before an ADHD diagnosis is made. Over time, it learned to detect subtle patterns across large datasets that may not be easily recognised through conventional clinical assessment alone.
The model showed strong performance in estimating ADHD risk in children aged 5 years and older. Notably, its accuracy remained consistent across different population groups, including variations in sex, race, ethnicity, and insurance status.
A support tool, not a diagnostic replacement
The researchers emphasise that the AI system is not intended to diagnose ADHD. Instead, it serves as a clinical support tool that can help identify children who may benefit from closer monitoring or earlier referral for specialist assessment.
“This is not an AI doctor,” said Matthew Engelhard, M.D., Ph.D., in Duke’s Department of Biostatistics & Bioinformatics, and senior author of the study. “It’s a tool to help clinicians focus their time and resources, so kids who need help don’t fall through the cracks or wait years for answers.”
By highlighting children who may be at higher risk, the tool could enable healthcare professionals to prioritise evaluation and initiate discussions with families sooner.
Potential benefits of earlier identification
Earlier identification of children at risk of ADHD could have meaningful implications for long-term outcomes. Research consistently shows that timely diagnosis and intervention are associated with improved academic performance, social development, and overall health.
“Children with ADHD can really struggle when their needs aren’t understood and adequate supports are not in place,” said study author Naomi Davis, Ph.D., associate professor in the Department of Psychiatry and Behavioral Sciences. “Connecting families with timely, evidence-based interventions is essential for helping them achieve their goals and laying a foundation for future success.”
The ability to flag potential concerns earlier may also help reduce the delays that many families face when seeking answers and support.
Next steps and ongoing research
While the findings are promising, the researchers stress that further validation is needed before such tools are implemented in routine clinical practice. Additional studies will be required to confirm effectiveness, assess real-world impact, and ensure safe integration into healthcare systems.
Hill and Engelhard have also explored the broader use of AI models in identifying risks and contributing factors for mental health conditions in adolescents, signalling a growing interest in predictive tools within this field.
Study authors and funding
In addition to Elliot Hill, Matthew Engelhard, and Naomi Davis, the study authors include De Rong Loh, Benjamin A. Goldstein, and Geraldine Dawson.
The research was supported by grants from the National Institute of Mental Health (K01-MH127309, UL1 TR002553) and the National Center for Advancing Translational Sciences.
Source: EurekAlert!
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Digital and AI Strategies Emerge as Central to Expanding Health System Capacity, Survey Finds
Key Takeaways:
- Health system leaders increasingly view AI and digital health as essential to expanding capacity without adding buildings or clinical staff.
- Surveyed executives highlight persistent system pressures, including unaffordable care, limited access to primary care, and insufficient management of people’s health and wellbeing.
- Most leaders believe that fundamental operational change, underpinned by AI and digital tools, will be necessary to create sustainable, proactive models of care.
Introduction
A new report from the healthcare advisory firm Chartis suggests that digital health and artificial intelligence are now central pillars in health system leaders’ strategies to expand capacity, improve access, and operate more sustainably. The findings come from the firm’s fifth annual digital transformation survey, conducted in September 2025, which examined the perspectives of 150 health system executives on their progress and priorities in digital transformation.
Persistent pressures on healthcare delivery
The survey underscores the mounting pressures facing health systems today. Executives identified several entrenched challenges that continue to shape healthcare delivery:
- Unaffordable care was cited by 61 per cent of respondents as a major concern.
- Insufficient management of people’s long-term health and wellness was highlighted by 52 per cent.
- Limited timely access to primary care was reported by 49 per cent of leaders.
More than half of surveyed leaders believe that the sustainability of current care delivery models will decline further over the coming five years.
A shift from reactive to proactive care
In response to these pressures, there is widespread agreement that health systems must undergo fundamental change. According to the survey, nine in ten executives feel that organisations need to move away from reactive care and adopt more proactive, anticipatory models.
AI and digital health solutions are now widely considered critical to achieving this shift. The report notes that 90 per cent of leaders are already prioritising investments in digital and AI capabilities to support operational transformation.
AI and digital tools to expand capacity
Executives emphasised the importance of AI and digital health in increasing capacity while avoiding costly infrastructure or workforce expansion. Over the next five years, leaders expect these capabilities to be essential for serving more people without increasing physical space or clinical headcount.
Key priorities include:
- Freeing clinicians’ time for direct care through the use of AI (reported as very important by 52 per cent).
- Maximising access to clinical expertise using digital tools (51 per cent).
- Developing digitally enabled referral channels (45 per cent).
- Building hospital-at-home models as an alternative to inpatient care (36 per cent).
Expanding reach and access to care
Leaders also highlighted a strong need to extend the reach of healthcare services. More than half (53 per cent) stated that expanding delivery through initiatives such as care-at-home or mobile clinics is very important to improving access.
Several digital approaches were identified as particularly valuable for enhancing timely and convenient access:
- AI coaches to answer people’s questions (44 per cent).
- Connected devices and remote diagnostics to gather real-time health data (43 per cent).
- AI-enabled risk prediction to identify emerging health issues (43 per cent).
Supporting personalised patient journeys
Personalisation is another priority area, with leaders recognising the potential of digital platforms and AI to tailor the patient journey. The survey found:
- 52 per cent view offering multiple digital communication channels as very important for personalising the experience.
- 48 per cent believe that enhanced data collection and AI-supported analytics will be key to developing personalised care plans.
Call to action from Chartis
Tom Kiesau, co-author of the report and chief AI and digital officer at Chartis, emphasised the urgency of acting on these insights. He stated in the press release:
“Organisations need to capitalise on the momentum in this moment – and ensure that they are truly realising the potential presented by AI and digital capabilities to drive needed business transformation at scale.”
AI Model Forecasts Risk of More Than 1,000 Diseases Decades in Advance
Key Takeaways:
- Delphi-2M, a generative AI model, can predict susceptibility to over 1,000 diseases using anonymised medical records.
- The system has been tested successfully across large-scale UK and Danish datasets, showing remarkable accuracy and transferability.
- Experts say the model could transform population health forecasting within years and may eventually be adapted for personalised clinical use.
European scientists build AI model to predict long-term disease risk
European researchers have unveiled a powerful new artificial intelligence model that can predict a person’s susceptibility to more than 1,000 diseases decades before symptoms arise.
The system, known as Delphi-2M, was developed by scientists at the European Molecular Biology Laboratory (EMBL) in Cambridge. “Delphi uses a similar architecture to large language models but with key innovations to work with healthcare data,” explained Tom Fitzgerald of EMBL.
Delphi was trained on anonymised health records from 400,000 participants in the UK Biobank, a major long-term biomedical study. The researchers then validated its performance using records from 1.9 million patients in the Danish National Patient Registry.
Matching and exceeding existing prediction tools
The predictions made by Delphi-2M spanned more than 1,000 diseases and were generally comparable in accuracy to existing clinical tools that focus on specific conditions, such as the QRisk score used for cardiovascular disease risk. Results from the study were published in Nature on Wednesday.
“Our model is a proof of concept, showing that it’s possible for AI to learn many of our long-term health patterns and use this information to generate meaningful predictions,” said Ewan Birney, EMBL’s interim executive director. “We were surprised at how well the model transferred from the UK to Denmark though it had never seen a single bit of Danish data.”
Birney emphasised that turning Delphi into a clinically deployable forecasting tool could take five to ten years, but he noted that it could be used much sooner to inform public health strategies.
Population-level insights and healthcare planning
While Delphi generates predictions at the level of individual patients, its most immediate value may be in population health planning. “Although it makes predictions for each individual, it can be very useful at the population level to forecast collective healthcare needs, how many people will suffer from particular diseases such heart attacks, cancers or diabetes and what sort of treatment they need,” said Moritz Gerstung, head of AI at the German Cancer Research Center in Heidelberg and a member of the Delphi team.
The model performed best for diseases with well-understood and consistent progression patterns, such as cardiovascular disease, diabetes and sepsis (blood poisoning). It was less effective for conditions triggered by unpredictable environmental factors or for very rare congenital disorders.
Expanding to genomics and biological data
Researchers are now working to enhance Delphi by incorporating biological information, such as genomic and proteomic data. Despite this, Birney said they were “very pleasantly surprised” at how well the model performed using healthcare records alone, achieving results comparable to or better than some models that rely on genetic and protein-level data.
“I want to stress the power of the straightforward medical record,” Birney added.
The team has patented key aspects of Delphi’s approach to predicting disease risk and timing. “We are exploring whether there are commercialisation possibilities and how to do that with our respective institutions,” Birney confirmed.
Towards ethical and scalable predictive medicine
Independent experts have praised the work as an important step forward for responsible AI in medicine. “This research looks to be a significant step towards scalable, interpretable, and — most importantly — ethically responsible form of predictive modelling in medicine,” said Gustavo Sudre, professor of genomic neuroimaging and AI at King’s College London, who was not involved in the study.
He added that while the current model relies solely on anonymised health records, its architecture has been designed to handle richer data types in the future, including biomarkers, imaging and genomics.
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