Big Data

Personalised healthcare revolution: The rise of digital twins in medicine

The trajectory of medicine is being redefined by pioneering research into computational models, advancing towards a future where medical treatments are tailored not to the average patient, but to each individual. Envision possessing a ‘digital twin’—a virtual counterpart that can undergo trials and treatments, sparing you the need for direct medication or surgical intervention. Scientists project that within the next decade, we could witness the routine use of ‘in silico’ trials, utilising virtual organs to evaluate drug safety and effectiveness, while bespoke organ models might be employed to customise patient care and avert medical complications.

Digital twins represent sophisticated computer-generated replicas of physical entities or processes, continuously refined with data from their actual counterparts. In the medical realm, this entails the fusion of extensive biological data—including genetic, proteomic, cellular, and systemic information—with individual patient data to craft detailed virtual models of their organs, and potentially, in time, their entire body.

Professor Peter Coveney, Director of the Centre for Computational Science at University College London and co-author of ‘Virtual You’, suggests that much of current medical practice lacks a scientific underpinning. He compares it to navigating by looking in the rear-view mirror—basing treatment for the patient at hand on historical cases. “A digital twin utilises your own data within a model that encapsulates your unique physiology and pathology. It’s a move away from decisions based on potentially unrepresentative population data to truly personalised medicine,” explains Prof. Coveney.

Cardiology is at the forefront of this cutting-edge model. Companies are already harnessing patient-specific heart models to aid in the design of medical devices. Meanwhile, the Barcelona-based enterprise ELEM BioTech is at the forefront, granting companies the capability to test drugs and devices on simulated human hearts. “We’ve conducted numerous virtual human trials on several compounds and are on the cusp of launching a new phase, with our cloud-ready product accessible to pharmaceutical clients,” shares Chris Morton, co-founder and CEO of ELEM.

At the recent Digital Twins conference hosted by the Royal Society of Medicine in London, Dr. Caroline Roney from Queen Mary University of London detailed the development of tailored heart models which could significantly aid surgeons in planning interventions for atrial fibrillation patients. “Surgeons typically resort to average-based approaches, but crafting patient-specific predictions that forecast long-term outcomes remains a formidable challenge,” Dr. Roney stated. She foresees widespread application of this technology in cardiovascular treatments, including decisions on valve selection and placement during replacements.

The field of oncology is also poised to benefit from digital twins. Teams from GSK and King’s College London are joining forces to construct virtual duplicates of patient tumours, amalgamating imaging, genetic, and molecular data with 3D cultures of cancer cells, and observing their drug responses. Leveraging machine learning, researchers can foresee how individual patients may react to various treatments, drug combinations, and dosages. “Conducting repetitive trials on a real patient with multiple treatments isn’t viable. Our aim is to devise a strategy while the patient is still with us, preparing us for any recurrence of cancer,” said Professor Tony Ng from King’s College.

The advent of digital twins extends even to the realm of pregnancy, offering the potential to develop treatments for conditions such as placental insufficiency or pre-eclampsia, and deepening our grasp of pregnancy and labour physiology. Professor Michelle Oyen, Director of the Center for Women’s Health Engineering at Washington University in St Louis, is crafting placenta models from ultrasound scans and post-birth high-resolution imagery to predict complications during pregnancy. “We’re striving to identify measures in a live person that could forewarn us of placental issues, aiming to preempt adverse outcomes like stillbirth,” Prof. Oyen elucidates.

In collaboration, Professor Kristin Myers from Columbia University is modelling the cervix, uterus, and foetal membranes, with the overarching goal to merge these into a comprehensive individual model to predict pregnancy outcomes. “We hope to analyse a simple ultrasound scan to understand how the uterus will adapt and when labour might occur,” Prof. Myers aspires, potentially guiding decisions on interventions like caesarean sections.

Moreover, the concept of digital twins is being expanded to model entire hospitals to enhance patient flow and healthcare system efficiency. Dr. Jacob Koris, a trauma and orthopaedic surgeon and digital lead at Getting It Right First Time, describes how tracking digital footprints left by patient interactions—from X-rays to outpatient appointments—can provide a granular, real-time view of patient treatment pathways. “Such insights could pinpoint areas for improvement and exemplary practices that could revolutionise patient care,” Dr. Koris believes.

This ambitious step forward in computational medicine promises a leap from the traditional, one-size-fits-all model to a future where every treatment is as unique as the patient it serves.

Digital tool for specialist weight management services shows promising results

​​In a groundbreaking investigation led by the team at University Hospitals Coventry and Warwickshire NHS Trust (UHCW), the potential and efficacy of digital platforms tailored for advanced weight management have been highlighted.

The research revolved around assessing the eagerness, acceptance, and active participation of patients on standby for their first-ever specialist weight management consultation. In the United Kingdom, specialised weight management services, often referred to as tier 3 services, offer a holistic approach to tackling obesity. These specialised services are generally anchored in hospitals or clinic facilities and bring together a diverse group of healthcare specialists. This includes dietitians, psychologists, specialist nurses, and doctors, all proficient in the realm of weight management.

For the purposes of this research, an NHS-approved digital platform named Gro Health was integrated into the service offering. This avant-garde health application propels numerous healthcare routes, with its tier 3 weight management feature, “W8Buddy”, acting as an online weight loss clinic. This feature delivers structured learning sessions, both individual and group coaching, an expansive list of over 2,000 recipes and meal schedules, and tools for health and nutrition tracking to chart progress.

The study drew in 199 prospective patients eagerly waiting for their appointment at the NHS Trust’s tier 3 weight management service.

Preliminary results indicate that over half of these individuals expressed genuine interest in the application. An impressive one-third went on to actively engage with the digital platform, highlighting the immense potential of such digital interventions in the specialised weight management scenario.

The engagement analysis unearthed intriguing data points. Those prone to emotional eating or those with an escalated BMI exhibited an increased propensity towards the Gro Health application. Meanwhile, aspects like age, ethnic background, and metabolic indicators like glycemia and lipid readings did not notably sway the interest.

These findings could serve as a blueprint for revolutionising weight management strategy. As digital healthcare tools evolve and gain traction, they stand poised as formidable and expansive strategies to confront the global issue of obesity.

Charlotte Summers, a behavioural change expert and the Founding Chief Operations Officer, expressed her enthusiasm, noting, “The pronounced interest demonstrated by patients in the Gro Health W8Buddy tool for weight management is truly heartening.”

She drew attention to the evident link between emotional eating, a raised BMI, and heightened engagement, highlighting, “This relationship underscores the transformative capacity of precise digital strategies in addressing weight-related concerns.”

Summers further elaborated on the journey ahead, “As we venture into providing tier 3 and 4 weight management services, we’re thrilled about tailoring these platforms with firsthand insights from both patients and healthcare providers. Such a collaborative effort not only champions a patient-driven model but also deepens our grasp on their preferences and anticipations. This, in turn, empowers us to offer top-tier, accurate care, be it through enhancing conventional healthcare avenues or pioneering virtual healthcare experiences.”

The study’s authors stress the need for continued exploration into understanding the challenges and motivators behind adopting digital tools and emphasise the importance of rigorously assessing their impact within specialised weight management services.

The rise of digital health platforms is sculpting the future of healthcare. This specific investigation underscores the transformative power of such tools, all while highlighting the necessity to unravel the complexities of patient engagement. As we witness the proliferation and capabilities of digital health platforms, the persistent quest to maximise their utility for patients and the broader healthcare spectrum is paramount.

Stay tuned for more revelations as ongoing studies continue to sculpt this rapidly evolving domain of weight management.

WHO Europe stresses the need for strengthened digital health literacy across the region

In a significant report released by WHO Europe, there’s an emphasised call for an immediate boost in investments towards digital health literacy across the region.

The report, titled “Digital Health in the European Region: The Ongoing Commitment to Transformation,” sheds light on a concerning statistic: only half of the countries spanning Europe and Central Asia have rolled out policies tailored to bolster digital health literacy. This oversight leaves a considerable portion of the population in the shadows, devoid of the benefits of evolving digital healthcare platforms.

The landscape of healthcare has experienced a monumental shift in the WHO European Region over recent times, as is evident from the burgeoning adoption rate of digital health solutions. This transformation has redefined the dynamics of patient care.

Released during the Second WHO Symposium on the Future of Health Systems in a Digital Era held for the European Region, the report encompasses insights from all 53 member nations. Many countries within this bracket witnessed a spontaneous surge in the creation and deployment of digital health tools and policies due to the pressing demands of the COVID-19 pandemic – a time dominated by lockdowns and social distancing mandates. This led to the broader acceptance of telemedicine services and the advent of user-centric health applications. However, the report firmly stresses that the journey is far from complete.

A pressing concern is the evident disparity in the adoption and assimilation of digital health solutions across the region. This “digital health divide” implies that a staggering number of individuals are yet to harness the potential benefits of digital health advancements.

Diving deeper, the report draws attention to several pivotal areas:

• Of the countries surveyed, 44 have an established national digital health strategy.
  
• A unanimous consensus is seen with all 53 member nations having legislation that focuses on protecting individual data privacy.
  
• However, there’s a considerable disparity in the preparedness and execution, with only 19 countries offering guidance on evaluating the efficacy and safety of digital health initiatives.
  
• Slightly above 50% of these countries have put forth policies advocating digital health literacy and have set into motion a digital inclusion agenda.
  
• The pandemic saw 30 countries devising legislation to champion the cause of telehealth.
  
• An area that requires immediate attention is the oversight of mobile health (mHealth) applications. Many nations lack a dedicated body to ensure the quality, safety, and reliability of these apps. A mere 15% have reported systematic evaluations of state-backed mHealth initiatives.
  
• Just above half of the countries have strategised the application of Big Data and avant-garde analytics within the healthcare domain.

Dr. Hans Henri P. Kluge, the WHO Regional Director for Europe, opined on the matter, “In numerous nations, the growth trajectory of digital health platforms has been somewhat sporadic. This approach warrants an overhaul. It’s imperative to perceive digital health as a long-term, strategic investment, rather than a fleeting addition or a privilege enjoyed by a select few. It is a clarion call for our political and health leaders to strategically invest in the digital health infrastructure of tomorrow, today.”

NHS gets £30m boost for tech advancements to improve patient care

In a recent announcement, the Department of Health and Social Care (DHSC) revealed its plans to allocate £30 million for state-of-the-art technology aimed at enhancing services provided by the NHS. This funding is anticipated to play a pivotal role in reducing patient wait times, expediting the diagnostic process, and introducing novel patient treatment methodologies.

The DHSC, on its website, highlighted that such financial backing is expected to alleviate some of the operational burdens the NHS might face during the upcoming winter season. Notably, the funds could potentially be utilised to expand 3D diagnostics, thus expediting cancer screenings, and to implement innovative logistic solutions such as drone deliveries.

Moreover, another significant avenue the investment could support is the augmentation of virtual wards. This would allow more patients to receive essential care within the comfort of their homes, ensuring hospital beds remain available for those in acute need. To date, the NHS has successfully established over 9,800 virtual ward beds, with plans to achieve the 10,000 bed milestone before winter strikes.

Regions throughout England can access this funding. Integrated care systems (ICSs) have been tasked with submitting proposals to both the DHSC and NHSE detailing how they would best leverage the technology. The application process is set to commence shortly.

Health and Social Care Secretary, Steve Barclay, emphasised the government’s commitment to ensuring the medical fraternity is equipped with cutting-edge technology, stating, “From virtual ward beds to wearable medical devices, this new funding is a testament to our dedication to enhancing patient care, preparing for winter, and relieving hospital pressures.”

In addition to supporting the use of wearable devices that monitor vital signs and aid in the management of chronic ailments, ICSs might channel investments into advanced digital imaging, a move that would undoubtedly bolster diagnostic capabilities, especially in the realms of cancer detection and other severe illnesses.

Dr Vin Diwakar, NHS’s interim national director of transformation, applauded the NHS’s innovative prowess, stating that such tech advancements have already positively impacted over 210,000 patients through virtual ward setups. Ellie Kearney, a spokesperson from the Health Tech Alliance, welcomed the financial boost but also expressed some members’ discontent with certain previous funding strategies.

In further developments, the DHSC referenced the Medical Technology Strategy they unveiled earlier in the year, which laid down a roadmap for enabling patient access to secure, efficient, and pioneering tech via the NHS. This latest £30 million injection builds upon a prior £21 million allocation towards AI diagnostic tools.

This strategic funding alignment is in sync with the government’s overarching vision for fortifying the NHS, especially with the challenges that winter typically brings. In addition to this tech fund, the government, in September, infused £200 million into the NHS, aiming to fortify its resilience. The Urgent and Emergency Care Recovery Plan, rolled out at the beginning of the year, pledges to furnish 5,000 more hospital beds, 10,000 virtual ward beds, and 800 brand-new ambulances, supported by an impressive £1 billion fund.

Public support key to leveraging UK’s healthcare data, research suggests

The latest research from the Boston Consulting Group Centre for Growth underscores the potential held by healthcare data in the UK. The report, titled ‘Towards a healthier, wealthier UK: unlocking the value of healthcare data‘, highlights robust public support for optimising the UK’s healthcare data, emphasising its dual potential to revitalise the economy and alleviate strains on the healthcare system.

Historically, the concept of harnessing healthcare data has been proposed numerous times in the UK. Still, its full potential remains largely untapped. According to the Boston Consulting Group, the roadmap to actualising this vision rests on two pillars: gaining comprehensive public endorsement for healthcare data utilisation and redirecting its generated value back into the healthcare sector.

Empirical evidence has demonstrated the tangible benefits of leveraging healthcare data. A standout case is the Royal Free London NHS Foundation Trust, which managed to curtail £2,000 in hospital admissions for acute kidney injury patients. Their success was anchored on an innovative app which synthesised data from blood tests, medical archives, and clinical assessment tools to provide timely alerts to healthcare professionals upon identifying potential risks.

An exemplary initiative from the Netherlands further fortifies this narrative. A consortium of seven academic hospitals implemented a value-driven healthcare model, leading to a remarkable 30% decline in unnecessary patient admissions and a staggering 74% drop in follow-up surgeries due to complications. This was achieved by pinpointing the right metrics to enhance patient outcomes and fostering a culture of data-sharing across institutions.

Lord O’Shaughnessy, a luminary from Newmarket Strategy and ex-parliamentary undersecretary of state at the Department of Health, opined, “Properly harnessed healthcare data has the potential to amplify both the UK’s health standards and economic vigour. However, this transformation hinges on unshakeable public trust. Establishing this trust mandates a rigorous, inclusive, and adaptive mechanism for public engagement and decision-making.”

Contrary to prevalent misconceptions, the Boston Consulting Group’s report revealed that a staggering 90% of individuals are amenable to sharing their health data with the NHS, reiterating that this openness is contingent upon the intended use of this data. While 73% endorsed the use of their data to gauge potential health risks, 72% favoured its application in refining clinical care methodologies.

The public’s fervent desire to be privy to discussions concerning their health data’s usage was palpable. In London, this dialogue has already gained momentum with the OneLondon Citizen Advisory Group finalising recommendations for the city’s Health Data Strategy.

On the matter of monetising healthcare data, most respondents were comfortable with their data being profitable, provided a portion of the proceeds revitalises the healthcare sector or catalyses broader societal benefits.

Key Recommendations:

In its concluding remarks, the Boston Consulting Group’s report delineated actionable steps to actualise the potential of healthcare data:

1. Showcase the tangible benefits derived from accessible healthcare data to foster public comprehension and endorsement.
2. Orchestrating a cohesive public engagement drive across all NHS data-centric ventures, elucidating the tangible benefits of optimised data usage.
3. Involve the public in data-related decision-making through decision panels and data usage logs, ensuring they remain integral stakeholders in shaping the discourse.
4. Establish a central reservoir to channel the monetary value extracted from data into local NHS frameworks.

Concluding his thoughts, Lord O’Shaughnessy urged policymakers to champion a grand public outreach initiative around healthcare data, stating, “Without engaging the masses in this crucial dialogue, we stand at the precipice of forfeiting an unprecedented opportunity.”

Revolutionary AI tool forecasts pancreatic cancer risk up to three years in advance

Ground-breaking research spearheaded by Harvard Medical School, in collaboration with the University of Copenhagen, VA Boston Healthcare System, Dana-Farber Cancer Institute, and the Harvard T.H. Chan School of Public Health, has developed an artificial intelligence (AI) instrument capable of identifying individuals at the greatest risk of developing pancreatic cancer up to three years before diagnosis, using solely their medical records.

The study, published in Nature Medicine on May 8, indicates that implementing AI-driven population screening could be a key strategy in detecting those at a high risk of pancreatic cancer earlier. This could, in turn, hasten the diagnosis of a condition often detected at advanced stages when treatment options are less effective, resulting in poorer outcomes. Pancreatic cancer, one of the world’s deadliest malignancies, is anticipated to increase its mortality toll.

At present, there is an absence of population-wide screening tools for pancreatic cancer. Targeted screenings are performed for individuals with certain genetic mutations or a family history that increases their risk of developing the disease. However, these screenings may overlook other cases not fitting these criteria, the researchers highlighted.

The study’s co-senior investigator, Chris Sander, a faculty member in the Department of Systems Biology at the Blavatnik Institute at HMS, underscored the significance of the AI tool. “Deciding who is at a high risk for a disease and would benefit from additional testing is one of the most challenging determinations clinicians have to make. The tests can be more invasive, more costly, and carry their own risks. An AI tool that accurately identifies those at the highest risk for pancreatic cancer and who would gain the most from additional tests could greatly enhance clinical decision-making.”

If implemented widely, this AI-driven method could expedite the detection of pancreatic cancer, lead to earlier treatment, and improve patient outcomes, possibly extending their life spans.

“AI-driven screening provides the opportunity to change the course of pancreatic cancer, a formidable disease that is exceptionally challenging to diagnose early and treat promptly,” said study co-senior investigator Søren Brunak, a professor of disease systems biology and research director at the Novo Nordisk Foundation Center for Protein Research at the University of Copenhagen.

In this novel study, the researchers trained the AI algorithm on two separate data sets, containing a total of 9 million patient records from Denmark and the United States. They instructed the AI model to identify potential signs of pancreatic cancer risk based on the data in the records.

The model could predict patients likely to develop pancreatic cancer in the future by identifying combinations of disease codes and the timing of their occurrence. Interestingly, many of the symptoms and disease codes were not directly related to or derived from the pancreas.

The researchers evaluated different versions of the AI models for their capacity to identify individuals at a heightened risk of disease development over different timescales – 6 months, one year, two years, and three years.

Overall, each iteration of the AI algorithm proved considerably more precise in predicting who would develop pancreatic cancer than current estimates of disease incidence in the general population. The researchers proposed that the model is likely as accurate in predicting disease onset as the existing genetic sequencing tests, which are generally only accessible to a small subset of patients in data sets.

Screening techniques for certain prevalent cancers, such as breast, cervix, and prostate cancer, rely on relatively straightforward and highly effective techniques, such as a mammogram, a Pap smear, and a blood test. These methods have significantly improved the outcomes for these diseases by ensuring early detection and intervention.

In contrast, pancreatic cancer poses greater challenges and costs in terms of screening and testing. Doctors predominantly focus on family history and the presence of genetic mutations. While these are crucial indicators of future risk, they often overlook many patients.

The AI tool presents a significant advantage in its potential applicability to any patient for whom health records and medical history are available, not solely those with a known family history or genetic predisposition for the disease. This is particularly important, the researchers noted, because many patients at a high risk may not be aware of their genetic predisposition or family history.

In the absence of clear indications that a person is at high risk for pancreatic cancer and without symptoms, clinicians may understandably hesitate to recommend more sophisticated and costlier testing methods such as CT scans, MRI, or endoscopic ultrasound.

When these tests are performed and suspicious lesions are detected, the patient must undergo a procedure to obtain a biopsy. Given its deep placement in the abdomen, the pancreas is difficult to reach and easy to inflame, leading to its nickname as “the angry organ.”

The researchers advocate for an AI tool that singles out those at the greatest risk for pancreatic cancer. This would ensure clinicians are testing the correct population, while also preventing others from undergoing unnecessary testing and additional procedures.

The survival rate for those diagnosed with pancreatic cancer in its early stages is about 44 percent, five years post-diagnosis. However, only 12 percent of cases are diagnosed at this stage. The survival rate decreases dramatically to 2 to 9 percent for those with tumours that have spread beyond their origin, the researchers estimated.

Chris Sander emphasised, “Despite significant advancements in surgical techniques, chemotherapy, and immunotherapy, the survival rate remains low. Therefore, besides advanced treatments, there’s a pressing need for better screening, more focused testing, and earlier diagnosis. This is where the AI-based approach serves as the initial critical step in this process.”

For the current study, the researchers created multiple versions of the AI model and trained them on the health records of 6.2 million patients from Denmark’s national health system over a 41-year span. Of these patients, 23,985 developed pancreatic cancer over time.

During the training, the algorithm identified patterns suggesting future pancreatic cancer risk based on disease trajectories. For instance, diagnoses such as gallstones, anaemia, type 2 diabetes, and other gastrointestinal-related issues pointed to a higher risk for pancreatic cancer within three years of evaluation.

Inflammation of the pancreas was a strong predictor of future pancreatic cancer within an even shorter time span of two years.

The researchers caution that none of these diagnoses on their own should be deemed indicative or causative of future pancreatic cancer. However, the pattern and sequence in which they occur over time provide clues for an AI-based surveillance model and could prompt physicians to closely monitor or test those at elevated risk.

Next, the researchers tested the best-performing algorithm on an entirely new set of patient records it had not previously seen — a U.S. Veterans Health Administration data set comprising nearly 3 million records over 21 years, including 3,864 individuals diagnosed with pancreatic cancer.

The tool’s predictive accuracy was somewhat lower on the US data set. The researchers attributed this to the shorter collection period and the different patient population profiles in the U.S. dataset compared to the Danish dataset.

When the algorithm was retrained from scratch on the U.S. dataset, its predictive accuracy improved. This, the researchers said, underscores the importance of training AI models on high quality, rich data and the necessity of access to large representative datasets of clinical records aggregated nationally and internationally.

In the absence of globally valid models, AI models should be trained on local health data to ensure their training reflects the specific characteristics of local populations.

New report illuminates the promising role of cloud technology in healthcare

A fresh report spotlighting the transformative role of cloud technology in combating the prevalent challenges in healthcare has been released by the Health Policy Partnership. The publication, titled “Our Health in the Cloud: Exploring the Evolving Role of Cloud Technology in Healthcare,” outlines the practical application of this technology. The report was created with the support of the European Institute for Innovation through Health Data (i~HD).

The publication coincides with the HIMSS 2023 European Health Conference & Exhibition in Lisbon, and delves into how cloud technology has propelled innovative solutions within the healthcare industry, and its potential future contributions.

Dipak Kalra, president of the i~HD, commented, “Cloud technology is a crucial catalyst for leveraging health data to enhance health outcomes, boost patient safety, swiftly identify public health threats, and expedite research into novel medicines and medical technologies.”

“Cloud technology offers secure computational power surpassing conventional on-premise resources, facilitating data integration across entities. Our report seeks to express why health decision-makers should be concerned about cloud technology, what they need to comprehend and be assured about urgently, and how to progress in a manner that prioritises the needs and preferences of patients and the public in health sector’s cloud integration,” Kalra added.

Cloud technology plays a pivotal role in enabling the data-centric strategy currently prevalent in healthcare. It has already demonstrated its potential in the field, offering key advantages like improved efficiency in patient-focused care, a population-oriented health approach, research that sparks innovation, and durable, resilient health systems.

However, the report highlights the industry’s lack of general awareness and comprehension of cloud technology. This lack of understanding leads to perceived risks, such as those related to privacy and security, which continue to hinder its broad adoption.

Suzanne Wait, Managing Director at The Health Policy Partnership, stated, “As both healthcare delivery and research have become increasingly data-intensive and collaborative, the process of gathering, merging, storing, analysing, and exchanging these data demands computational power, cybersecurity, and speed that far surpass typical onsite capacities, thus necessitating cloud technology. All stakeholders, not just IT departments, should enhance their understanding of ‘the cloud’ and ensure its proper and maximal usage across healthcare settings.”

The report emphasises the necessity for both patients and healthcare professionals to actively participate in discussions and policymaking concerning the technology. This involvement is key to ensuring that its implementation caters to their needs and incorporates their perspectives.

According to a recent report from Netskope Threat Labs, the healthcare sector has a comparatively low count of cloud malware downloads relative to other sectors. However, as cloud technology’s deployment expands, it is crucial for organisations to ensure they are adequately protected.

“Digital health will just be healthcare”: Hospital chiefs predict seamless integration of healthcare and technology

Leading digital authorities within the healthcare sector foresee a more virtual, automated, and user-friendly health system in five years. Their vision includes seamless digital integration, a feature which has already begun to take shape across many hospitals, according to industry leaders interviewed by Becker’s, a leading healthcare publication.

Daniel Barchi, executive vice president and CIO of Chicago-based CommonSpirit Health, equates the evolution of digital health with the development of e-commerce, noting that just as electronic commerce became a mainstream aspect of business, so too will digital health simply become “health”. CommonSpirit, operating 143 hospitals in 22 states, is embracing this digital evolution by using its size and mission to leverage digital population health tools. These tools aggregate data to assist clinicians and patients in managing health and wellness.

Philadelphia-based Thomas Jefferson University and Jefferson Health’s executive vice president and chief information and digital officer, Nassar Nizami, expects to see a broad adoption, integration, and implementation of several technologies within the next five years. He asserts that digital health signifies a cultural revolution within traditional healthcare. The organisation is investing in enhancing its existing AI technology, which aids physicians in assessing cancer risk in lumps or nodules, stroke risk in CT scans, and the potential requirement for blood transfusions in patients. Moreover, the organisation is utilising automation in areas such as IT, human resources, sourcing and in its virtual nursing initiative. Jefferson’s telemedicine program, JeffConnect, showcases the effective use of mobile health and remote patient monitoring, and has served as a model for other healthcare systems.

Brenton Burns, executive vice president of UPMC Enterprises, points out that Pittsburgh-based UPMC is targeting increased access to care and efficiencies through automation in various departments, including call centres and scheduling. He emphasises that digital tools have enabled the healthcare provider to extend beyond traditional settings, offering care through diverse channels such as telemedicine and home visits. Accessible and interoperable data, he insists, are vital to success.

Cincinnati-based Bon Secours Mercy Health is also increasing its digital capacity, while concurrently assisting other health systems through its digital health subsidiary, Accrete Health Partners. Jason Szczuka, the organisation’s chief digital officer, describes how they are developing, investing, and partnering with industry leaders to optimise IT operations, improve patient access to care and unlock crucial data, analytics, and automation capabilities.

New Orleans-based Ochsner Health plans to expand its asynchronous virtual tools such as e-visits and e-consults, enhance its online scheduling system, and bolster its AI and remote monitoring capabilities, explains Denise Basow, MD, executive vice president and chief digital officer. The organisation is utilising technology to predict and prevent health issues, deliver personalised care, manage patients efficiently, and reduce total healthcare costs.

Orlando Health, in Florida, is investing in its foundational IT platforms, infrastructure, data, and analytics to enhance the connection between providers and patients, regardless of their geographical location. Novlet Mattis, the organisation’s senior vice president and chief digital and information officer, reveals plans for an enterprise digital platform infused with clinical decision support tools. She envisions digital health as a standard element of health and wellness management in five years, rather than a novel innovation.

Kelly Jo Golson, executive vice president and chief brand, communications and consumer experience officer at Charlotte, N.C.-based Advocate Health, affirms that their recent merger with Atrium Health and Advocate Aurora Health has enabled an acceleration in digital transformation. For Advocate Health, consumer-centricity is paramount. The strategy includes a flexible, dynamic platform that provides consistent experiences, simple scheduling, interconnected programs for remote patient monitoring, and the incorporation of 24-7 virtual access into clinical workflows.

Ardent Health Services, based in Nashville, Tenn., is endeavouring to make care easier to access, whether in-person or digital. The chief consumer officer, Reed Smith, predicts that in the future, digital health will be synonymous with healthcare, without any segregation in delivery methods. He anticipates that consumers will have more control, and healthcare providers will be able to offer more support, especially for less critical needs, as care delivery adapts to accommodate more individual, do-it-yourself approaches.

Harnessing machine learning to predict obesity: A focus on the first 1000 days of life

The recent publication in the Scientific Reports Journal showcases a novel utilisation of machine learning (ML) to forecast obesity in adults by examining risk factors and monitoring body mass index (BMI) during the initial 1,000 days of life, spanning from two to four years old.

The rise in obesity rates in both children and adults worldwide is undeniable. Early onset obesity in children is indicative of potential adult obesity, cardiometabolic risks, and other childhood diseases.

Obesity, once entrenched, is challenging to manage and is often chronic. As a result, a preventative approach to obesity is becoming a research priority. Identifying individuals at an elevated risk of obesity in adulthood during their early years could significantly enhance these prevention efforts.

Known adjustable risk factors encompass a mother’s higher pre-pregnancy BMI, pregnancy weight gain, socioeconomic status, high neonatal weight, and local community variables such as crime rates and food availability. Despite this, the cumulative risk estimation of these factors remains underexplored.

Currently, there is a lack of initiatives aimed at estimating childhood obesity, particularly those considering prenatal and neonatal risk factors. This is despite studies highlighting that the period between two to four years of age provides a valuable window for intervention due to heightened developmental flexibility and the ability to influence health behaviours.

The study in question employs ML algorithms to pinpoint children with a higher risk of obesity, providing vital data for the creation of prevention policies and strategies. Additionally, the researchers introduced a dynamic BMI tracker for use throughout childhood to help identify obesity risks in adulthood.

The researchers utilised a machine learning technique known as least absolute shrinkage and selection operator (LASSO) regression. This allowed them to maintain features that most significantly and relevantly relate to paediatric obesity, outside of height, weight, and body mass index.

The study examined data from 149,625 visits by 19,724 individuals aged up to 48 months, with an analysis of 10,348 individuals specifically aged between 30.0 and 48.0 months. Following data correction, the supplementation of missing values, and variable normalisation, 50 variables were chosen for consideration. After application of LASSO regression and subsequent tests, a final 19 variables were scrutinised.

The proposed model comprised variables such as mean height, BMI, weight at various intervals within the first two years, time differences between visits, and percentile ranks for weight and height at two years.

The predictive ability of the model was tested with a validation dataset comprising 20% of the patients. It showed an impressive accuracy in estimating childhood BMI, with a mean error of 1.0 across all three age ranges (30.0 to 36.0 months, 36.0 to 42.0 months, and 42.0 to 48.0 months).

Most variables in the model showed a significant association with paediatric BMI across all estimated ranges. These findings suggest that this predictive model could bolster both clinical and population-wide obesity prevention efforts in the earliest days of life.

Risk factors associated with higher childhood BMI identified in the study included maternal risks during pregnancy, C-section delivery, higher infant birth weight, and sleep disturbances in infants requiring assistance to sleep.

Interestingly, living in a food desert and having Hispanic ethnicity were factors that appeared protective against high BMI.

In summary, this study highlights that machine learning can help track paediatric BMI trajectories and identify modifiable risk factors during early childhood. This supports efforts to intervene before the onset of unhealthy weight gain, aiming to alleviate the health burden of obesity.

Factors such as maternal health, a child’s sleep quality, and socioeconomic influences can shape the weight trajectories of children into later

Saudi Arabia’s healthcare industry embraces major digital overhaul

Saudi Arabia is at the forefront of the digital revolution in the wellness industry, propelling improvements in patient care, overall experience, and sustainable health development to match international standards.

The Kingdom’s strategic focus is to reorganise its healthcare sector, augmenting its potential to operate as a cohesive, value-driven ecosystem centred around patient health.

To accomplish these lofty objectives, Saudi Arabia is dedicated to substantial investments in the health technology industry. Reflecting the government’s commitment to this initiative, the 2023 budget allocates more than SR180 billion ($50.3 billion) to healthcare and social development.

A significant portion of this budget is channelled towards digital health strategies to promote accessibility, efficiency, and transparency within the healthcare system.

One such initiative is the establishment of a national electronic health record system, serving as a comprehensive database for patient data. This ensures nationwide access for medical professionals, facilitating smooth collaboration and expedited decision-making.

The Kingdom is also prioritising investments in telemedicine platforms to guarantee healthcare access even in isolated regions.

Under its Vision 2030 plan, the government is also aiming to privatise the healthcare industry, focusing its efforts on 290 government hospitals and 2,300 primary health centres within the Kingdom.

In a conversation with Arab News, Jalil Allabadi, CEO of Amman-based digital health platform Altibbi, clarified that the government’s initiatives to decentralise would significantly improve the sector and boost healthcare technology.

Allabadi shared that larger institutions and corporations are developing their health tech solutions, while smaller companies are focusing on the consumer end.

He emphasised that as hospitals and clinical centres move towards decentralisation, they will concentrate on profit generation. This shift will motivate the adoption of healthcare technology for automation and digitisation of their operations, enhancing efficiency.

Altibbi, one of the largest digital health platforms in the Middle East, has raised over $52.4 million in funding since its launch.

In line with the Kingdom’s focus on preventive health services and reducing reliance on hospital care, the aim is to digitise 70 percent of patient activities by 2030.

According to Allabadi, digital health consultations and activities are still in the early stages compared to the Vision 2030’s targets, but growth is “happening very fast.”

Startups are invigorating the health tech sector by integrating digital tools such as artificial intelligence, the Internet of Things, and big data analytics into healthcare services for more effective prediction, prevention, and disease management.

Saudi Arabia’s health tech sector offers a blueprint for a future where digital health solutions are integral to comprehensive and patient-focused care. This groundbreaking transformation represents not only an investment in the health of its citizens but also a stimulus for economic diversification and sustainable development.

Chronic diseases, prevalent among the elderly, are a significant concern. A report by the Saudi government estimates that by 2050, 25 percent of its projected 40 million population will be 60 or older, necessitating an overhaul in healthcare delivery.

In conversation with Arab News, Sacha Haider, a partner at the UAE-based venture capital firm Global Ventures, explains that the next evolution in Saudi health tech focuses on preventive healthcare and longevity.

Haider elaborates that regular consultations and check-ins will significantly energise health tech and digital health in the Kingdom.

In the post-COVID-19 era, the industry has embraced digital technologies to enhance patient experiences and improve care quality. Saudi-based platforms like Nala and Cura are leading examples of successful digital health services companies, offering a range of services from instant consultations to tailored digital care programs.

Moreover, Saudi Arabia’s Ministry of Health has introduced apps like Mawid, Tabaud, and Seha, which offer virtual consultations, effectively reducing the need for in-person hospital visits.

The advent of express clinics within pharmacies, providing immediate primary care services, is another trend gaining traction. These clinics offer services ranging from consultation, blood glucose and blood pressure measurements, skincare analysis, weight management, and vaccination.

Global data firm Statista projects the digital health market in Saudi Arabia to grow by 9.06 percent from 2023 to 2027, culminating in a market volume of $1.16 billion.

BT’s innovative drive to digitally transform UK healthcare

Leading telecommunications entity BT aims to leverage its established presence and specialised connectivity know-how to vitalize the digital infrastructure of the UK healthcare system, and is amplifying its healthcare portfolio to meet this goal.

The corporation has spent the last two years fostering its healthcare division, instituting a clinical advisory board to guide the creation of products tailored for the NHS needs. It also initiated its Vanguard Programme, envisioned as an interactive platform that enables healthcare professionals on the frontline to test and assess technology to guarantee its compatibility with local necessities.

“Our objective is to capitalise on opportunities that complement BT’s core business of connectivity,” Neal Herman, HealthTech Director at BT’s innovation centre, Etc., shared. “Our aim revolves around connecting individuals to the appropriate care at the right moment, and every endeavour we undertake contributes towards achieving that.”

An independent unit of Etc. is testing the implementation of drone technology for medicine deliveries over BT networks, and according to Herman, drones might also be incorporated into future healthcare solutions.

Herman detailed that BT’s vision incorporates three major themes: health navigation that aids in shaping patient interactions with the healthcare system; patient flow that enables hospitals and healthcare providers to streamline patient movement within the system; and remote care.

He depicted the first category as a future where “the healthcare professional contacts you, instead of the other way around.” Health navigation is fundamentally a guiding tool that enhances digital platforms and interactive voice response (IVR) technology. Commencing at general practice, the initial point of patient access, these solutions ensure that individuals reach the suitable healthcare provider from the outset, whether it be an immediate referral to a specialist or a physiotherapy session.

Patient flow, as Herman explained, becomes effective once patients arrive at the hospital, facilitating efficient management of patient capacity by nursing staff and site managers. “It’s about offering site managers real-time data flow to track the availability of beds,” Herman noted. These solutions are presently active in northeast Essex.

The remote component of the process incorporates products that vary from wearable technology to virtual ward monitoring platforms and is currently being piloted in Warrington for patients with chronic obstructive pulmonary disease (COPD) and hypertension.

Recently, BT unveiled a virtual ward initiative that will integrate smart monitoring devices and collaborations with other service providers to link artificial intelligence (AI)-enabled virtual care platforms. This will facilitate real-time health data capture and evaluation of patient conditions in care homes, community nursing, and virtual wards.

“BT excels at implementing technology on a grand scale and we possess a significant privilege to contribute,” said Professor Sultan Mahmud, BT’s Healthcare Business Director.

Having previously served as the chief innovation, integration, and research officer at Royal Wolverhampton Hospitals NHS Trust (RWT), Mahmud joined BT in 2021. He added, “BT is adept at bridging the translational gap. This is fundamentally about achieving technical interoperability and interoperation.” A crucial objective of interoperability, he emphasised, is to ensure that technology procurement avoids “closed systems or vendor lock-in.”

Mahmud further pointed out that the company’s strategy is reflective of its commitment to assist the NHS in addressing staffing shortages and managing waiting lists. Employed effectively, remote technology can function as a tool for staff retention and aid in easing the demand for hospital beds.

Making care smarter using predictive analytics

As more elderly and disabled individuals aim to live independently, monitoring apps designed for frail adults have become increasingly popular. These apps can also help family and caregivers distinguish between short-term problems and long-term declines. Falls, in particular, can be costly. The UK’s NHS estimates that unaddressed fall hazards in the home cost around £435 million per year, with fragility fractures costing £4.4 billion per year. Entrepreneurs have responded to the need for these apps by creating systems that can collect information for a better understanding of the individual’s daily functioning and factors that might be affecting it.

MySense, a predictive wellbeing analytics company, is one of these entrepreneurs. The founder and CEO, Lucie Glenday, created MySense after her sister was diagnosed with a rare form of motor neurone disease and died at age 23. She struggled to understand her sister’s symptoms and wanted to help others with complex needs. MySense uses eight devices, the majority of which are passive sensors, that pick up signs of daily activities. This data is then analysed to create a personalised digital portrait of what normal looks like for each person. The company’s sensors pick up on context around activity rather than the activity itself. MySense has been adopted by several NHS hospitals, including South Warwickshire NHS Foundation Trust, Leicestershire County Council, and Care Hub. The technology has reduced unplanned hospital admissions by 50% and moved 25% of people out of pathways for patients with fewer than 1,000 days to live.

Digital Social Care has created the Adult Social Care Technology Fund, which will provide funding for technology that increases care quality and safety, reduces avoidable hospital admissions, and increases support for independent living. AVERio, another initiative focused on falls prevention, has created non-intrusive sensors that detect falls using 4D radar technology to scan a room constantly. EIT Health, a European Union initiative, has also created a monitoring device, FFalls Predictor, which aims to provide early detection and prevention of falls.

Smart data and population health can build better, healthier lives

In recent years, there has been growing interest in how population health management can help improve the lives of citizens. Here, we explore how machine learning can provide the data intelligence we need to deliver better healthcare for everyone.

Preventative healthcare is known to be more effective than reactive healthcare. By intervening early and preventing health issues from escalating, individuals, communities, and healthcare providers can all benefit from improved outcomes. With the UK healthcare system under immense pressure, there is a pressing need to shift towards a proactive approach. However, in order to achieve this, we need to understand what factors influence health outcomes. For example, why do people in one area have fewer healthy life years than those in another part of the country?

Population health intelligence is the discipline of finding answers to questions like this. By making the right interventions at the right time, we can improve health outcomes. As Integrated Care Systems (ICSs) work to tackle some of the NHS’s most pressing problems, such as health and care inequalities and financial sustainability, population health intelligence can help make the connections between health outcomes and the factors that influence them.

ICSs bring together NHS, local authority, and third sector bodies, providing access to all the data needed to gain a deeper understanding of population health. This includes everything from NHS records to data on education, housing, and crime. However, the challenge is how to extract the relevant insights that can point to new and better ways of doing things.

Data on life expectancy is a good starting point. It is also a sound measure of health inequality, which is currently in the spotlight. Unlike health data or patient reported outcome measures (PROMS), life expectancy data doesn’t depend on people having accessed healthcare, making it a more inclusive and accurate proxy for overall population health. The variations in the data are stark, with men and women born in Glasgow City today expected to live around 10 years less than those born in Westminster or Kensington & Chelsea.

To understand what’s behind these stats, machine learning can make meaningful analysis of disparate datasets. It can rapidly work across huge volumes and multiple sources of data to identify patterns that can guide decision-making. Population health intelligence can help us analyse the causes of death at different ages in different demographics and the wide range of influences on them.

Factors such as education and housing can affect health outcomes. For example, data analysis may connect high levels of poor housing stock with respiratory illness. This could ultimately show that making improvements to living conditions today could prevent people from developing chronic conditions that lead them to depend on multiple health and care services in the future.

In a similar way, information on dental health, such as the number of people in a single area having teeth removed at a young age, could also be a predictor of chronic conditions such as diabetes and heart disease in future life. This information could direct healthcare interventions towards support for diet and lifestyle changes.

By connecting health data with environmental information, such as air quality or the amount of available green space, population health intelligence techniques could show local authorities where to focus their investment, where people’s physical and mental wellbeing will benefit the most.

Health-related wearables also support the shift towards more personalised and proactive healthcare. From simple step counters and heart rate monitors to sophisticated continuous glucose monitors, people are increasingly willing and motivated to track their own wellbeing. When connected to healthcare systems and analysed by machine learning algorithms, wearable devices and apps could support preventive healthcare by alerting professionals to potential issues, for example, an individual showing pre-diabetic symptoms.

The UK’s move towards integrated care systems presents a huge opportunity to build a proactive approach to healthcare based on insights gleaned from many different data sources. Machine learning is vital for unlocking this potential, helping to build more innovative, impactful, and cost-effective healthcare.

AI could help predict medical conditions earlier thanks to unique health data sets

Nightingale Open Science, a project launched by Ziad Obermeyer, a physician and machine learning scientist at the University of California, Berkeley, offers a unique and free medical dataset aimed at solving medical mysteries through the use of artificial intelligence (AI). 

The project, which was funded with $2 million from former Google CEO Eric Schmidt, consists of 40 terabytes of medical imagery such as X-rays, electrocardiogram waveforms, and pathology specimens from patients with various conditions, including Covid-19, sudden cardiac arrest, fractures, and high-risk breast cancer. Each image is labelled with the patient’s medical outcomes, such as the stage of breast cancer, whether it resulted in death, or whether a Covid-19 patient required a ventilator.

The datasets were curated with the help of hospitals in the US and Taiwan over a period of two years, and Obermeyer plans to expand this to include more medical diversity in Kenya and Lebanon in the coming months. The key differentiating factor between these datasets and those that are available online is that they are labelled with “ground truth,” meaning that they are based on what actually happened to the patient rather than a doctor’s opinion.

The AI community has recently shifted its focus from collecting “big data” to more meaningful data, which is relevant to a specific problem and can be used to address issues such as inherent biases in healthcare, image recognition, or natural language processing. In healthcare, algorithms have been shown to amplify existing health disparities. For example, an AI system used by hospitals to allocate additional medical support for patients with chronic illnesses was found to be prioritising healthier white patients over sicker black patients who needed help, due to the use of healthcare costs as a proxy for healthcare needs.

The Nightingale Open Science project is aimed at producing high-quality datasets that are more representative of the population and can be used to root out underlying biases that are discriminatory towards underserved and underrepresented groups in healthcare systems, such as women and minorities. These diverse datasets can also help to study the spread of diseases in different cultures and locations, where people may react differently to illnesses.

The availability of high-quality, diverse medical datasets can enable AI to predict medical conditions earlier, triage better, and ultimately save lives. The Nightingale Open Science project, which provides unique and curated datasets based on actual patient outcomes, has the potential to drive progress in healthcare and reduce the impact of biases on healthcare systems.