“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.
Read MoreHarnessing 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
Read MoreSaudi 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.
Read MoreBT’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.
Read MoreMaking 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.
Read MoreSmart 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.
Read MoreAI 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.
Read More