Digital Health

One in five GPs already incorporate AI tools into clinical practice, reveals BMJ survey

A recent online survey conducted by the BMJ has disclosed that 20% of UK general practitioners (GPs) are incorporating generative artificial intelligence (AI) tools, such as ChatGPT, into their clinical practice, despite the widespread lack of formal training on how to properly utilise these technologies.

The results, which were published on 17 September 2024, highlight that out of 1,006 GP respondents, 205 reported using generative AI tools in their work. Of those practitioners, 47 (29%) revealed that they utilised AI to create clinical documentation following patient consultations, while 45 (28%) used AI to suggest potential differential diagnoses.

According to the survey, ChatGPT is the most popular large language model (LLM)-based chatbot currently being used among UK GPs.

Dr Charlotte Blease, an associate professor at Uppsala University in Sweden and the study’s lead author, suggests that GPs could be finding value in these tools, particularly for assisting with administrative burdens and supporting clinical reasoning. She explained, “GPs may derive value from these tools, particularly with administrative tasks and to support clinical reasoning.”

However, Dr Blease also stressed the limitations of AI, noting that such tools can be prone to subtle errors and biases that could impact clinical decision-making. “These tools have limitations since they can embed subtle errors and biases,” she stated.

The study raised concerns over the lack of official guidance or policies concerning the use of AI in clinical practice. “Despite a lack of guidance about these tools and unclear work policies, GPs report using them to assist with their job,” Blease observed.

She further argued that the medical community needs to strike a balance by both educating current and future practitioners about the benefits of AI—such as summarising complex information—and highlighting the risks, including hallucinations, biases, and the potential compromise of patient privacy.

One of the most pressing risks she emphasised is the potential breach of patient confidentiality. “Our doctors may unintentionally be gifting patients’ highly sensitive information to these tech companies,” Dr Blease warned, underscoring the uncertainty around how companies behind generative AI models may use the data they collect.

The BMJ survey was conducted in February 2024 and distributed via Doctors.net.uk, a clinician marketing service, to a non-probability sample of GPs. This monthly ‘omnibus survey’ requires responses from 1,000 participants, all of whom are asked to complete closed-ended questions.

While GPs’ use of AI is becoming more common, another study commissioned by the Health Foundation, published in July 2024, showed broad support for AI in healthcare among both NHS staff and the general public. According to the Health Foundation’s research, 76% of NHS staff and 54% of the public are in favour of using AI for patient care.

Despite the enthusiasm, NHS England’s director of AI, imaging, and deployment, Dom Cushnan, has urged for a cautious and evidence-based approach. Speaking at the Digital Health AI and Data event in October 2023, Cushnan underscored the need for health systems to ensure that AI tools are suitable and rigorously tested before being fully integrated into clinical settings.

While Cushnan acknowledged the excitement surrounding AI’s potential, he also emphasised that such technology must pass stringent reviews to ensure its efficacy and safety for patient care. “AI tools must undergo a rigorous process before they can be integrated into clinical practice,” Cushnan said, reinforcing the importance of evidence-based approaches in healthcare innovations.

This growing integration of AI into the medical profession highlights a significant shift in healthcare, yet it also raises critical questions about training, patient safety, and ethical considerations as the technology continues to evolve.

NHS Providers urges government to prioritise digital investment to boost productivity

NHS Providers has called for urgent government investment in digital technology as a means to enhance productivity, following the release of a survey involving leaders of NHS trusts. This call comes amid growing concerns about financial pressures and operational challenges facing the health service.

The survey, whose results were published on 8 September 2024, painted a stark picture of the financial health of the NHS. Trust leaders indicated that their finances have been severely impacted by a number of factors, including ongoing industrial action, the effects of inflation, demands for increased efficiency, and the overall operational strain on the NHS. These challenges have exacerbated existing financial pressures, placing trust leaders in a difficult position as they attempt to manage their budgets for the upcoming year.

The survey found that a significant 51% of respondents were “extremely concerned” about their ability to meet operational priorities within their respective organisation’s financial budget for the 2024/25 fiscal year. This concern highlights the precarious financial state of many NHS trusts as they face increasing demand for services with limited resources.

Moreover, 92% of those surveyed expressed the view that the efficiency challenge for 2024/25 is more daunting than in the previous year, with the need to do more with less becoming ever more critical. This sentiment reflects the growing pressure on NHS leaders to find ways to increase efficiency in an already overstretched system.

When it comes to meeting recovery targets, trust leaders were notably divided in their confidence levels. While almost a third of respondents (32%) were optimistic that their system would achieve its targets to reduce waiting times for physical health services in 2024/25, only 8% felt confident that similar targets for mental health services would be met. This disparity underscores the particular difficulties the NHS faces in addressing long waits in mental health care, a sector that continues to experience significant challenges.

Respondents also shared their views on accident and emergency (A&E) services, with 46% of those providing these services indicating that their trust is likely to meet the new target of seeing 78% of A&E attendees within four hours by March 2025. This reflects the ongoing challenges in delivering timely emergency care, as the NHS grapples with rising demand and workforce shortages.

On the financial front, the outlook is similarly divided. A significant proportion of respondents (44%) forecasted a deficit for their trust’s financial position in 2024/25, while 45% predicted a breakeven position, and only 11% anticipated a surplus. These figures highlight the fragile financial footing on which many NHS trusts currently stand.

Sir Julian Hartley, Chief Executive of NHS Providers, praised the resilience and adaptability of trust leaders and their teams in the face of these considerable challenges. He stated: “It’s incredibly impressive how trust leaders and their teams are responding to the productivity challenge. You can see the benefits of collaboration, incentivising staff, and using digital tools to help them work more efficiently.” However, Sir Julian emphasised that these efforts, while commendable, can only achieve so much without substantial strategic investment.

He continued: “That’s all happening, but it can only go so far without significant strategic investment in infrastructure and digital technologies. We need to recognise the value of investment in the NHS – its impact as a catalyst for economic growth, employment, and research, and the importance of funding infrastructure, digital, and other technology to drive productivity.”

The timing of these survey findings is significant, coming just ahead of a much-anticipated review of the NHS by esteemed NHS surgeon and independent peer, Lord Ara Darzi. Commissioned by Health Secretary Wes Streeting, this review is expected to be published on 12 September 2024. Early indications suggest that the report will include sharp criticism of the NHS’s productivity levels, as well as concerns about the state of children’s health.

Prime Minister Sir Keir Starmer also weighed in on the state of the NHS during an interview broadcast by the BBC on 8 September 2024. In his remarks, the Prime Minister described the NHS as having been “broken” by successive Conservative governments. He referenced the upcoming review, underscoring the severity of the challenges facing the NHS.

Responding to the Prime Minister’s comments, Sir Hartley expressed that these concerns mirrored those frequently raised by NHS trust leaders. He said: “The Prime Minister’s comments today echo what trust leaders have been telling us. The shockwaves of the longest and deepest squeeze in NHS financial history, a growing mismatch between capacity and demand, major workforce challenges, and the after-effects of the Covid-19 pandemic are still being felt throughout the health service.”

Sir Hartley went on to highlight the need for sustained, long-term financial investment in the NHS, pointing out that the current approach to funding is unsustainable. He stated: “Time and again, trust leaders tell us they want to see long-term, multi-year investment in the health service which allows them to plan for the future instead of this stop-start approach to NHS funding which leaves them constantly worrying about budget cuts followed by quick-fix, short-term funding announcements.”

Adding further weight to the conversation around NHS productivity, a report published by the Health Foundation in April 2024 examined clinicians’ views on the technologies that could offer the greatest opportunities to enhance productivity within the NHS. The report underscored the potential of digital tools to drive efficiencies, streamline operations, and improve patient outcomes, aligning closely with the calls from NHS Providers for greater investment in this area.

In conclusion, the message from NHS Providers is clear: while trust leaders and their teams are doing all they can to meet the challenges they face, there is a pressing need for the government to step up with significant investment in digital technologies and infrastructure. Such investment is critical not only for improving productivity but also for securing the long-term future of the NHS as it continues to navigate unprecedented operational and financial pressures.

AI-powered stethoscope doubles success in diagnosing heart failure during pregnancy

Heart failure during pregnancy is a life-threatening and often overlooked condition, primarily because its symptoms—such as shortness of breath, severe fatigue, and difficulty breathing while lying down—closely resemble typical discomforts associated with pregnancy. This confusion often leads to delayed diagnosis. However, a groundbreaking study presented at the European Society of Cardiology Congress, based on research by the Mayo Clinic, demonstrates that an artificial intelligence (AI)-enabled digital stethoscope allowed healthcare providers to diagnose twice as many cases of heart failure compared to conventional obstetric care methods. The full study has been published in Nature Medicine.

This clinical trial was conducted in Nigeria, where pregnancy-related heart failure occurs more frequently than in any other region worldwide. The findings revealed that the AI-enabled stethoscope was 12 times more likely to detect weakened heart function—specifically in cases with an ejection fraction of less than 45%—compared to traditional methods. An ejection fraction under 45% is a critical indicator of peripartum cardiomyopathy, a form of heart failure that can develop during the final months of pregnancy or soon after childbirth.

“Early detection of this form of heart failure is crucial for safeguarding maternal health and wellbeing,” explained Dr Demilade Adedinsewo, a cardiologist at the Mayo Clinic and the lead investigator of the study. “Symptoms of peripartum cardiomyopathy can progressively worsen as pregnancy advances, or more commonly after childbirth. If left undiagnosed and untreated, this condition can become life-threatening as the heart weakens further. Although medications can help when detected early, severe cases may necessitate advanced interventions, including intensive care, mechanical heart pumps, or even heart transplants in extreme situations.”

The randomised, controlled, open-label clinical trial involved nearly 1,200 participants. Each was screened for heart conditions using either standard obstetric care or AI-enhanced tools. Researchers at the Mayo Clinic had previously developed a 12-lead AI-electrocardiogram (ECG) algorithm, capable of predicting a weak heart pump, known clinically as low ejection fraction. This algorithm was further refined by Eko Health, which incorporated it into its point-of-care digital stethoscope. The stethoscope, cleared by the U.S. Food and Drug Administration (FDA), is designed to detect heart failure in patients with low ejection fractions.

The results of the study were compelling. The combination of the AI-based screening tools—comprising the digital stethoscope and the 12-lead ECG—enabled doctors to identify cases of weak heart function with a high degree of accuracy. Specifically, the AI-enhanced stethoscope doubled the number of heart failure cases detected with ejection fractions below 50%, and significantly increased detection rates for ejection fractions under 45%.

The researchers evaluated the AI-enabled screening tools across three different levels of ejection fraction, all of which are used in the clinical diagnosis of heart failure. An ejection fraction below 45% is the threshold for diagnosing peripartum cardiomyopathy, while a measurement below 40% indicates heart failure with reduced ejection fraction, for which specific medications are known to alleviate symptoms and lower the risk of mortality. Ejection fractions below 35% suggest critically low heart pump function, often requiring aggressive management, including advanced heart failure treatments or the implantation of a defibrillator if heart function fails to improve. Each participant in the intervention group underwent an echocardiogram at the start of the trial, which provided confirmation of the AI-predicted heart function.

“This research provides compelling evidence that AI-assisted tools can significantly improve the detection of peripartum cardiomyopathy, especially in Nigerian women, where the condition is more prevalent,” stated Dr Adedinsewo. “However, there are still important questions that need to be addressed. Our next step involves assessing the usability and adoption of these tools by Nigerian healthcare providers, including both doctors and nurses, as well as evaluating the impact of the AI-enabled stethoscope on patient outcomes. In the United States, peripartum cardiomyopathy affects approximately 1 in 2,000 women, but among African American women, the incidence is as high as 1 in 700. Evaluating the effectiveness of this AI tool in the U.S. will further test its capabilities across diverse populations and healthcare environments.”

The clinical trial received financial backing from several sources, including the Mayo Clinic’s Centres for Digital Health and Community Health and Engagement Research, the Mayo Clinic’s Building Interdisciplinary Research Careers in Women’s Health (BIRCWH) programme, which is funded by the National Institutes of Health (NIH), and the Mayo Clinic’s Centre for Clinical and Translational Sciences (CCATS), also funded by the NIH.

The study not only underscores the potential of AI in improving maternal healthcare, but also highlights the critical importance of early diagnosis in preventing life-threatening complications related to heart failure during and after pregnancy. With further refinement and wider implementation, this AI-enhanced tool could transform the way pregnancy-related heart failure is detected and managed, potentially saving countless lives in the process.

Digital consultations significantly enhance medication optimisation in heart failure patients

Heart failure, a condition impacting over 60 million people globally, presents a significant challenge in ensuring patients receive the most effective combination of medications. A recent study has revealed that patients are four times more likely to achieve optimal medication regimens after just 12 weeks of engaging in digital consultations. This groundbreaking research, led by a collaboration of five Dutch hospitals under the coordination of Amsterdam UMC, demonstrates that digital consultations not only improve the quality of care but also maintain high levels of patient satisfaction. These findings have been published in Nature Medicine and were also presented at the European Society of Cardiology’s annual conference.

Reflecting on the origins of the study, Mark Schuuring, a former cardiologist at Amsterdam UMC and currently practising at Medical Spectrum Twente, stated, “During the COVID-19 pandemic, almost all of our patients were suddenly digital consult patients and, to be honest, this worked well but there were also concerns. Those concerns gave us the idea for this study.”

The programme developed by the researchers measures the quality of care by comparing the treatment approaches taken during digital consultations with the most current medical guidelines. Schuuring elaborated, “We investigated the digital data exchange between patients and doctors and provided them both with more information. The programme encourages doctors and nurses to give treatment that is closest to international guidelines. The business community makes extensive use of such programmes, but they are not yet commonplace in the care sector.”

The study involved 150 heart failure patients who were randomly assigned to one of two groups: one group participated in a digital consultation strategy, while the other received traditional care. After 12 weeks, the researchers assessed how many patients in each group had achieved the optimal combination of medications. The results were striking: 28% of patients in the digital consultation group reached optimal medication levels, compared to only 7% in the traditional care group.

Schuuring emphasised the significance of these findings, stating, “Ultimately, we saw that this is superior to the way we currently organise care and this is demonstrated by the data.”

In addition to evaluating the effectiveness of digital consultations in improving medication optimisation, the researchers also addressed common concerns associated with digital healthcare. They found no significant differences in the time invested by healthcare providers, patient satisfaction levels, or, most importantly, the patients’ quality of life.

“This study shows that digital consultations are really a win-win; the patient’s care was improved, and their experience was also not in any way reduced,” Schuuring concluded. “We think that this could work way beyond heart failure. Something which is urgently needed as the increases in patient populations outstrip the growth in staff numbers.”

This study highlights the potential for digital consultations to play a crucial role in the future of healthcare, particularly in managing chronic conditions such as heart failure. The findings suggest that with the right digital tools, healthcare providers can offer high-quality care that adheres closely to international guidelines, ultimately improving patient outcomes while also addressing the growing demands on healthcare systems worldwide.

AI tool simplifies cardiology reports making them ‘more understandable’ for patients

A pioneering study has revealed that an artificial intelligence (AI) tool can simplify heart test results into explanations that are accurate, relevant, and comprehensible to patients.

The focal point of the research was the echocardiogram—a diagnostic tool that employs sound waves to visualise the flow of blood through the heart’s chambers and valves. Traditional echocardiogram reports are replete with technical jargon and numerical data relating to heart function, dimensions, vessel pressure, and tissue thickness, all indicative of potential cardiac ailments. However, such reports are notoriously difficult for patients to decipher, often causing undue anxiety, according to the researchers.

To mitigate this issue, NYU Langone Health explored the potential of a specific AI technology capable of generating contextually appropriate language predictions. This AI, developed by OpenAI and known as GPT-4, was accessed in March 2023 when NYU Langone secured one of the earliest “private instances” of the tool, allowing them to safely experiment with real patient data under stringent privacy regulations.

The study, which was published online on July 31 in the Journal of the American College of Cardiology (JACC) Cardiovascular Imaging, evaluated 100 cardiologist-authored echocardiogram reports. GPT-4 was tasked with reformulating these into patient-friendly summaries. These AI-generated explanations were then assessed by five board-certified echocardiographers using a five-point scale to rate accuracy, relevance, and understandability. Impressively, 73 percent of these summaries were deemed accurate enough to be shared with patients without any modifications.

The evaluations showed that 84 percent of AI-generated summaries were completely accurate, and 76 percent included all critical information. The significance of these findings is underscored by none of the AI-generated explanations being rated as “potentially dangerous” due to missing information.

“Our study is the first of its kind to test GPT-4 for this purpose, and our findings suggest that generative AI can significantly aid clinicians in communicating complex echocardiogram results to patients,” explained Dr. Lior Jankelson, MD, PhD, the corresponding study author and an associate professor at NYU Grossman School of Medicine. He noted that such rapid, precise explanations could alleviate patient anxiety and reduce the overwhelming number of inquiries clinicians receive.

The need for such advancements is partially driven by the 21st Century Cures Act of 2016, which mandates the swift release of test results directly to patients, leading to a spike in patient inquiries due to misunderstandings about their health data.

Dr. Jacob Martin, MD, the study’s lead author, emphasised the ongoing need for AI tools to deliver results explanations promptly as they become available, thus potentially easing the burden on healthcare providers who must manually input vast amounts of data into electronic health records.

However, the study also revealed that 16 percent of AI explanations contained some inaccuracies. For instance, one AI summary incorrectly described the size of a pleural effusion—a mistake attributed to an ‘AI hallucination’, a known issue where AI tools generate erroneous or fabricated data. This underscores the essential role of human oversight in verifying and refining AI-generated drafts before they are finalised for patient review.

Additionally, the study involved a survey of non-medical participants to gauge the clarity of AI-generated explanations compared to traditional reports. A resounding 97 percent found the AI versions easier to understand, which often reduced their anxiety.

“This analysis confirms the potential of AI to enhance patient understanding and reduce anxiety,” said Dr. Martin. He revealed that the next steps would involve integrating these AI enhancements into regular clinical practice to improve patient care and reduce the workload on healthcare providers.

The research was a collaborative effort involving multiple specialists from NYU Langone’s Leon H. Charney Division of Cardiology and other departments, highlighting the interdisciplinary approach necessary for such innovative healthcare solutions.

Researchers apply innovative mathematical technique to help combat obesity

Researchers from Katz School and UMass Dartmouth have unveiled a groundbreaking method that employs an advanced mathematical technique to deepen understanding of the factors influencing weight loss. Detailed in their white paper, “A Choquet-Integral Based Approach to Identify Weight Loss Component Subsets,” their research was showcased at the IEEE/ACM International Conference on Connected Health: Applications, Systems, and Engineering Technologies (CHASE) in June.

Traditionally used in theoretical mathematics and economics, the Choquet Integral is now making strides in the biomedical field. This technique provides a sophisticated method for targeting and optimising various metrics and factors affecting weight loss, showing promise in the practical application to health data.

The innovative use of the Choquet Integral has enabled the researchers to identify key health factors critical for weight loss, improving the precision and efficiency of their study methods over traditional approaches. This method not only minimises errors but also enhances the extraction of useful information by concentrating on the most pertinent data. The Choquet Integral utilises a fuzzy measure, which unlike traditional metrics that provide definite values such as size or length, can manage uncertainty and overlaps. This allows for a more adaptable evaluation of complex, interrelated data sets.

Matthew Fried, the leading author of the paper and a Ph.D. student at Katz School under the mentorship of Dr. Honggang Wang, explained, “We believe this methodology could pave the way for more efficient and accurate health data analysis, ultimately contributing to better health outcomes and advancing the fight against obesity.”

The methodology was rigorously tested across four distinct datasets, including random numbers, fabricated data, standard heart data from the UC Irvine Libraries, and National Institutes of Health (NIH) health data. The technique proved effective in differentiating between actual data and noise, underscoring its suitability in modelling interactive features and measuring variables like insulin and glucose levels, LDL (bad cholesterol), HDL (good cholesterol), height, and more.

Dr. Honggang Wang, co-author and chair of the Graduate Department of Computer Science and Engineering at Katz School, stated, “We studied how different health factors affect each other, whether positively or negatively, using this special mathematical approach. This method helped us understand more clearly which biological factors are most important for weight loss.”

This cross-disciplinary application of the Choquet Integral to health data analysis not only enhances the efficiency of machine learning models by selecting reduced versions of power sets but also has the potential to transform weight loss studies and other areas in the biomedical field.

Hua Fang, another co-author and professor of computer & information science at the University of Massachusetts Dartmouth and UMass Chan Medical School, noted, “The benefits of this technique extend beyond weight loss studies. It has broad potential applications in various biomedical fields where analysing complex inter-variable relationships is crucial.” This marks a significant step forward in the use of mathematical models to enhance biomedical research and health care outcomes.

Machine learning enhances tracking and prediction of Parkinson’s disease symptoms

A groundbreaking study published in the IEEE Transactions on Neural Systems and Rehabilitation Engineering has introduced a novel automated system employing machine learning (ML) techniques to assess and predict the progression of Parkinson’s disease (PD). This system offers a promising enhancement in the evaluation of motor symptoms, potentially transforming how this neurodegenerative disorder is managed.

Parkinson’s disease, a condition with no known cure, is primarily managed through symptomatic treatments focusing on alleviating tremors, mood disturbances, bradykinesia (slowness of movement), and postural instability. Traditionally, clinicians have relied on the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) to gauge disease progression through stages such as mild, moderate, or advanced, and to assess patient responses to treatments. While the MDS-UPDRS Part III scoring method is deemed sensitive and reliable, it is not without its shortcomings, including its subjective nature and limited capacity to detect early-stage or prodromal PD symptoms.

Recognising these limitations, researchers have explored a digital method that utilises ML algorithms to extract movement markers from MDS-UPDRS Part III video recordings, such as those from the finger-tapping test, which evaluates limb bradykinesia. This approach has proven to yield a higher diagnostic and prognostic accuracy regarding PD severity.

Existing digital detection models often assume a uniform set of kinematic features across all stages of PD, varying only with disease severity. However, this assumption is increasingly challenged by findings suggesting that motor symptoms evolve non-uniformly throughout the disease’s progression. The recent study hypothesised that a more nuanced consideration of varying kinematic features could enhance the detection and accurate classification of PD severity at different stages.

The study analysed video data from 66 PD patients and 24 age-matched healthy controls, excluding any individuals with a history of brain tumours, strokes, or neurostimulatory implants. All PD diagnoses were validated using the United Kingdom PD Brain Bank criteria. Data collection occurred at baseline and was repeated a year later, with participants abstaining from anti-Parkinsonian medications overnight prior to the recordings. These videos not only captured motor tasks but also cognitive evaluations under the MDS-UPDRS III.

Researchers compared three classification models: a multiclass classification model that uses consistent features across all severity levels; an ordinal binary classification model that accounts for the progressive nature of the disease; and a novel tiered binary classification approach that adjusts the kinematic features considered based on the severity of symptoms.

In total, 180 videos were analysed, including 123 from PD patients. These were categorised by motor symptom severity scores of zero to three. The videos demonstrated significant variations in kinematic features, such as movement amplitude and sequence effect, which is the diminishing amplitude during repeated movements. Notably, the study found that kinematic features associated with lower severity scores differed distinctly from those linked with higher scores, supporting the hypothesis that PD-related motor impairments evolve uniquely over time.

The study identified several innovative kinematic features, including amplitude decay and variations in the speed of opening and closing movements. These features, quantifiable through video analysis, showcased differences between various severity levels with higher precision than traditional methods. The tiered binary classification model, in particular, demonstrated superior effectiveness in predicting PD severity, suggesting that a multi-stage model or a combination of models that consider different features at various disease stages could significantly enhance PD management and treatment efficacy assessments.

This machine learning-based approach to video analysis holds great potential to revolutionise PD management by enabling more accurate monitoring and quantification of motor symptoms, thus paving the way for more tailored and effective treatment strategies.

Ireland’s new legislation to revolutionise digital health records

In a significant move to enhance healthcare management, Ireland’s Department of Health has introduced the Health Information Bill 2024, poised to revolutionise the management of digital health records across the nation. This pivotal legislation follows the approval of the Irish Government and is specifically crafted to create a comprehensive legal framework facilitating the efficient sharing of patient data necessary for care and treatment.

This legislative initiative aligns with Ireland’s obligations under the impending European Health Data Space (EHDS) Regulation, set for implementation within the year. The Health Information Bill 2024 aims to establish a robust national health information system, designed to significantly improve the quality of patient care as well as the planning and delivery of healthcare services.

A cornerstone of this initiative, as outlined by the Department of Health, is the bill’s crucial role in the successful implementation of the ‘Digital for Care: A Digital Health Framework for Ireland 2024 – 2030’. This framework, launched in June, 2024, outlines a strategy for integrating digital technology into healthcare systems over the next several years.

Upon enactment, the bill will empower the Health Service Executive (HSE) to amalgamate health information across diverse care settings—encompassing private, public, and voluntary sectors. This comprehensive consolidation is intended to facilitate the development and deployment of digital health records accessible to all patients across Ireland.

Moreover, the bill proposes enhanced accessibility of health information for both patients and healthcare providers, which is anticipated to lead to improved health outcomes. It also focuses on augmenting patient safety by refining patient identification processes, incorporating tools like Eircode and the Personal Public Service Number (PPSN) to seamlessly integrate patient data with Ireland’s national digital strategy.

Ireland’s Health Minister, Stephen Donnelly, emphasised the importance of integrated care, as envisioned in the Sláintecare reform. He stated, “Integrated care as envisioned in Sláintecare, needs the right information, in the right place, at the right time. The Health Information Bill gives patients greater access to their own information so they can make informed decisions about their health and care options. It also enables health professionals to have a more complete, holistic view of the patients they are treating.”

Minister Donnelly also noted that the bill is slated to progress through the Houses of the Oireachtas during the upcoming autumn legislative session. Furthermore, additional legislative measures are planned for the forthcoming months and years to fully realise the policy intent of the approved General Scheme of the Bill and to ensure compliance with the EHDS implementation deadlines set for 2026 to 2030. This series of legislative updates underscores Ireland’s commitment to a fully integrated and digitally enhanced healthcare system.

NHS Wales prepares for drone-based transfer service for blood products

In an ambitious stride towards modernising healthcare delivery, NHS Wales is set to embrace drone technology for transporting blood products, as revealed in a recent initiative involving key stakeholders from the aerospace and healthcare sectors. The initiative, which is gaining momentum, includes the pioneering Welsh NHS Medical Drone Delivery Network project. This project has been recognised as one of the UK Research and Innovation (UKRI) Future Flight Challenge winners, spotlighting its innovative approach to healthcare logistics.

Scheduled for an exhibition on 11 July 2024 at Snowdonia Aerospace Centre, this demonstration will bring together government and healthcare leaders to witness the potential of drone technology in revolutionising medical service delivery. The event is pivotal for garnering support and setting a strategic direction for the integration of drones into the NHS framework.

The Welsh NHS Medical Drone Delivery Network project is spearheaded by Snowdonia Aerospace in collaboration with SLiNK-TECH Ltd and the Welsh Health Drone Innovation Partnership. This consortium, under the leadership of the Welsh Ambulance Service Trust and supported by the Welsh Blood Service, has secured a significant £500,000 funding in April 2024 from Innovate UK. This funding is part of a broader initiative that supports five health sector projects aimed at leveraging drones for the delivery of medicines and medical supplies.

In his April 2024 statement, following the announcement of the Future Flight Challenge winners, Health Minister Andrew Stephenson reflected on the transformative potential of technology within the NHS: “Technology has huge potential to transform the NHS for patients but it can also help automate processes behind the scenes too. These projects will help future-proof our medical supply chains by using drones to deliver medical products, reducing the chances of supply disruption while saving costs, energy, and resources. If successful, they could be rolled out across the NHS to boost resilience and help people live more independent lives, building on the government’s long-term ambitions.”

Echoing this sentiment, Simon Masters, Deputy Director of the Future Flight Challenge, emphasised the synergistic benefits of this cross-sector collaboration: “This partnership between the drone industry and the medical sector highlights the value that drones can bring to our front-line public services.”

The initiative also includes exploratory projects, such as the collaborative effort with the University of Warwick and industry partners SkyBound, to investigate the feasibility of using drones to deliver defibrillators in emergency scenarios involving cardiac arrests. This investigation, known as the Drone-Delivered Defibrillators study or 3D project, is backed by funding from Resuscitation Council UK and is set to conclude in October 2024, with results expected in early 2025.

This development follows other successful trials, such as the one conducted by University Hospitals of Morecambe Bay NHS Foundation Trust and Lancashire Teaching Hospitals NHS Foundation Trust, which utilised drones for transporting pathology samples, as reported by Digital Health News in September 2022.

With a business case and roadmap due for submission to the Welsh government by the end of 2024, NHS Wales is poised at the brink of a logistical revolution, promising enhanced efficiency and responsiveness in healthcare delivery.

AI-generated alerts proven to reduce death risk in hospital patients

Recent research highlights the transformative role of artificial intelligence (AI) in improving patient outcomes in hospitals. AI-generated alerts, designed to notify hospital staff of any significant changes in a patient’s condition, have been found to dramatically reduce the risk of mortality.

The innovative system increases the likelihood of timely medical intervention, with patients 43% more likely to have their care escalated, markedly boosting survival rates. According to Matthew Levin, Professor of Anesthesiology, Perioperative and Pain Medicine at The Mount Sinai Hospital, this approach leverages AI and machine learning technologies to proactively address potential declines in patient health. “We wanted to see if quick alerts made by AI and machine learning, trained on many different types of patient data, could help reduce both how often patients need intensive care and their chances of dying in the hospital,” Levin explained.

Traditionally, tools like the Modified Early Warning Score (MEWS) have been used to assess the risk of clinical deterioration. However, Levin points out that the automated machine learning algorithms used in this study surpass these older methods in both accuracy and timeliness, facilitating earlier and potentially life-saving interventions.

The study evaluated the effectiveness of these AI alerts across four surgical units at The Mount Sinai Hospital in New York, encompassing a total of 2,740 patients. These individuals were divided into two groups: the first group received real-time alerts about health deterioration directly to their doctors or rapid response teams, while the second group had alerts generated but not immediately transmitted to their healthcare providers.

David Reich, President of The Mount Sinai Hospital and Mount Sinai Queens and a leading figure in both Anesthesiology and Artificial Intelligence and Human Health at Icahn Mount Sinai, highlighted the substantial impact of real-time, AI-driven alerts on patient care. “Our research shows that real-time alerts using machine learning can substantially improve patient outcomes,” Reich stated. He described these tools as “augmented intelligence” that enhance clinical decisions, ensuring that timely and appropriate interventions are made to improve patient safety and outcomes, aligning with the goals of a learning health system.

Further benefits of the AI alert system include a higher likelihood of patients receiving necessary medications for heart and circulation issues, alongside a decreased mortality rate within 30 days of care. The system is under continual development, with a dedicated team of intensive care doctors assessing high-risk patients daily, providing targeted recommendations to the treating physicians. This ongoing refinement of the algorithm, driven by an increasing amount of patient data, is enhancing its accuracy and reliability.

This study not only reaffirms the critical role of AI in modern healthcare but also points towards a future where technology and human expertise converge to deliver superior patient care.

AI enhances efficiency of artificial pancreas, study confirms

Recent research conducted by the University of Virginia’s Center for Diabetes Technology reveals that incorporating artificial intelligence (AI) into an artificial pancreas system can significantly improve its operational efficiency. This innovative study marks a crucial advancement in the management of type 1 diabetes.

The study highlights that an AI-equipped artificial pancreas system is comparable in performance to a state-of-the-art experimental version in maintaining optimal blood glucose levels. The integration of AI not only matches the effectiveness of advanced systems but also offers potential applications in other medical devices that require minimal computational resources, like insulin pumps.

Dr Boris Kovatchev, the study’s lead author, emphasised the novelty of their approach, stating, “So far, this is the first clinical trial of a data-driven artificial pancreas system, which used an extensively trained neural network to deliver insulin automatically.” This system represents a significant shift towards more autonomous patient care in diabetes management.

The experimental setup involved 15 adult participants who used both the advanced artificial pancreas and the AI-enhanced system for 20 hours each. Results showed that the traditional advanced system kept blood sugar levels within the target range 87% of the time, closely followed by the AI-supported system at 86%.

Notably, the research demonstrated that the AI-supported pancreas system drastically cuts down the computational load by six times compared to traditional methods. “The AI-supported artificial pancreas is therefore more suitable for implementation in devices with low processing power, such as insulin pumps or pods,” the report noted, pointing towards a broader applicability in diabetes care technology.

Dr Kovatchev further explained the technical breakthroughs, saying, “Neural-net implementation allows the algorithm to learn from the data of the person wearing the system. This opens the door to real-time, AI-driven personalised insulin delivery.” This adaptation could lead to more tailored and effective diabetes management solutions for individuals.

The findings, published in the journal Diabetes Technology & Therapeutics, set a precedent for the future of diabetes care, highlighting the critical role of AI in enhancing the functionality and efficiency of medical devices aimed at chronic disease management.

Advancements in infant precision medicine through creation of ‘digital babies’

In an ambitious collaboration between the University of Galway and Heidelberg University, along with the Heidelberg Institute for Theoretical Studies and Heidelberg University Hospital, a breakthrough has been made in the realm of infant healthcare through the development of ‘digital babies’. These are sophisticated computational models that replicate the metabolic processes of newborn infants, aiming to enhance the precision in diagnosing and treating their medical conditions.

At the Digital Metabolic Twin Centre at the University of Galway, the team utilised real-world data gathered from a significant cohort of 10,000 newborns. This data encompassed various parameters including sex, birth weight, and metabolite concentrations. From this, the researchers successfully constructed 360 detailed whole-body computational models. These models are specifically designed to emulate the metabolic systems of infants over their first six months of life.

Published on 3 June 2024 in the journal Cell Metabolism, the findings of this study underscore the efficacy of these models. Not only were they capable of predicting known biomarkers for inherited metabolic diseases, but they also accurately forecasted the metabolic responses infants would have to different treatment methodologies.

Elaine Zaunseder, the lead author from Heidelberg University, highlighted the unique metabolic characteristics of babies, which are pivotal for their growth and health. She explained, “Babies require more energy for body temperature regulation due to their high surface-area-to-mass ratio and the fact that they cannot shiver during the first six months. Our task was to decode these metabolic activities and integrate them into mathematical frameworks within our computational models.”

Zaunseder also emphasised that this research marks a significant initial stride towards creating digital twins for infants. Such digital twins could potentially transform paediatric healthcare by offering customised disease management that aligns with the distinct metabolic needs of each infant.

Professor Ines Thiele, who led the project, stressed the importance of newborn screening programmes which are vital for the early detection of metabolic diseases, thus improving survival rates and health outcomes for infants. “However,” she noted, “the observed variability in disease manifestation among babies highlights the critical need for personalised treatment plans. Our models enable in-depth studies into the metabolism of both healthy and diseased infants, including those conditions screened for in newborns.”
Parallel to these developments, Imperial College London announced in May 2024 that their researchers are also working on digital twin models, specifically heart models for NHS patients suffering from pulmonary arterial hypertension. This indicates a broader trend towards adopting digital twin technology across various areas of healthcare, promising more targeted and effective treatment strategies.