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The core business of medical technology companies (MedTechs) has been manufacturing and marketing physical devices
Physical devices will continue to be a substantial part of their business, but on their own, are unlikely to deliver high growth rates, which are more likely to come from artificial intelligence (AI) data driven strategies that improve patient outcomes

The impact of big data, artificial intelligence, and machine learning on the medical technology industry

James Carville, an American strategist, who played a leading role in Bill Clinton winning the 1992 presidential race, insisted that the campaign focus on the economy and coined the phrase “It’s the economy, stupid”. If Carville was asked today for a winning long-term growth strategy for medical technology companies, might he say, “It’s big data, stupid”?

This Commentary suggests that while physical products have been the backbone of MedTech companies in the past, they are unlikely to contribute significantly to future growth rates, which are more likely to come from artificial intelligence (AI) driven big data innovations, which create new solutions that improve patient journeys and outcomes.

In this Commentary

This Commentary describes the meaning of ‘big data’ in a healthcare context, explains ‘the data universe’ and stresses not only its immense volume, but also its variety, and the phenomenal speed at which the data universe is growing. Today, most industries leverage big data and AI techniques to create innovative offerings that drive growth and enhance competitive advantage. However, with few exceptions, traditional MedTechs have been relatively slow to collect and analyse a wide range of health, medical and lifestyle data which have the potential to provide innovative software offerings that improve patients’ therapeutic journeys and complement physical products. This is partly because the industry must adhere to strict regulations and partly because many medical technology companies lack the necessary capabilities and mindsets to collect and leverage big data. Most have business models that tweak legacy physical products and accept growth rates of ~5% as the ‘new normal’. We provide a brief history of big data and AI business strategies mainly to underline that these are relatively new. It was only in the early 2000s that electronic health records (EHR) began to replace paper-based patient records, which were stored in numerous filing cabinets in healthcare silos. It was not until ~2015 that EHRs became standard practice and researchers started to apply algorithms to EHRs and other data to detect patterns and make predictions that could improve diagnoses and treatments, enhance patient outcomes, and reduce healthcare costs. The increased use of big data and AI techniques in healthcare raises important cybersecurity concerns and trust issues because health professionals and patients do not understand how algorithms arrive at their conclusions and actions. Cybersecurity concerns are addresses by a range of encryption techniques and security protocols. Trust in algorithms has been helped by the development of ‘explainable AI’, which is software that describes the essence of algorithms in easily understood terms. However, more work is still needed in these two areas. We introduce cloud and cloud services together with an explanation why these have experienced such rapid growth across all industries in recent years. The cloud makes it easier to store and access big data via the internet from anywhere in the world. Cloud services provide security for big data as well as a range of management and analytical tools that help to transform data into revenue generating software offerings. For MedTech companies, the cloud and cloud services provide the basis for more efficacious R&D. The medical technology industry has become bifurcated between companies that leverage AI driven big data strategies to enhance growth rates and those that predominantly focus on legacy physical product offerings and settle for lower growth rates. Over the past decade the nature of the medical technology industry has changed; partly because of AI big data strategies supported by the cloud computing and a large and rapidly growing range of open-source, easy-to-use AI tools. This has given small companies a competitive advantage. The Commentary concludes by describing a few of these small MedTechs with disruptive digital products that target large, rapidly growing, underserved market segments.

Big data and healthcare

Big data are comprised of a wide range of information collected from multiple sources that surpasses the traditionally used amount of storage, processing, and analytical power and is unmanageable using conventional software tools. In healthcare settings, big data include hospital records, medical records of patients, results of medical examinations, and data generated by traditional medical devices as well as various biomedical and healthcare tools such as genomics, wearable biometric sensors, and smartphone apps. Biomedical research also generates data relevant for the medical technology industry.

The data universe

The massive amount of data, which is generated from the entirety of the internet is referred to as the ‘data universe’. It is not only its volume that makes this special, but it is also the variety of the data and the phenomenal speed at which the universe is growing. The International Data Corporation (IDC) estimated that the data universe grew from ~130 exabytes in 2005 to >40,000 exabytes in 2020. To put this in perspective: 1 gigabyte of data is 1bn bytes (18 zeros after the 1 or 2³⁰ bytes), and 1 exabyte is equal to 1bn gigabytes.

Data generated healthcare innovations

In the past, collecting and interpreting vast quantities of data was not feasible, partly because computer systems were relatively small and did not generate much data, and partly because technologies to manage big data were underdeveloped. Fast forward to the present, and businesses across most industries now generate enormous amounts of data. Organizations apply AI and machine learning (ML) techniques to these data to create innovative product offerings to access new revenue streams with significant growth potential. Such technologies, combined with health-related big data, can positively impact the medical technology industry by generating novel diagnostics and treatments for patients, streamlining the process of medical record keeping and developing more personalized and responsive care plans that improve patient journeys and outcomes.

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Leaning-in on digital and AI

The new rapidly evolving AI data driven healthcare ecosystem

Despite the potential commercial advantages of AI data driven diagnostic and therapeutic solutions, many traditional MedTechs have been slow to collect health and lifestyle data from multiple sources to develop software offerings, which complement their legacy physical products. One notable exception is Philips Healthcare. In the early 2000s, the company was challenged by new entrants to the market who were successfully leveraging information from health wearables and other sources to create and market AI data driven offerings. At the 2016 annual conference of the American Healthcare Information and Management Systems Society (HIMSS) in Chicago, Jeroen Tas, a Philips executive, said, “We are in the midst of one of the most challenging times in healthcare history, facing growing and aging populations, the rise of chronic diseases, global resource constraints, and the transition to value-based care. These challenges demand connected health IT solutions that integrate, collect, combine, and deliver quality data for actionable insights to help improve patient outcomes, reduce costs, and improve access to quality care”.

Philips had the mindset and resources to respond positively to this rapidly changing ecosystem. In 2017 the company appointed Tas as its Chief Innovation & Strategy Officer, tasked with launching a suite of big data AI driven solutions, the IntelliVue® patient monitors, which support the growing demands of health professionals to provide quality care and improved outcomes for an expanding population of older, sicker patients with fewer resources. These monitoring solutions seamlessly connect big data, AI technology and patients to support health professionals to manage patients as they transition through their care journeys. In 2016, Philips and Masimo, a medical technology company specializing in non-invasive AI data driven patient monitoring devices, entered a multi-year business partnership involving both companies’ innovations in patient monitoring. Philips agreed to integrate Masimo's measurement technologies into its IntelliVue® monitors, to help clinicians assess patients’ cerebral oximetry and ventilation status. The outcome of the collaboration was the launch of a new suite of patient solutions, called Connected Care, which give healthcare providers the ability to monitor patients more effectively and reduce costs.

The bifurcation of the MedTech market

In addition to large MedTechs such as Philips and Masimo, there are hundreds of small companies developing AI driven big data offerings aimed at improving patient outcomes. The reasons for many traditional companies’ slowness to fully leverage big data and AI applications are partly because medical devices are required to comply with stringent regulatory guidelines and partly because of the lack of capabilities. The different responses have bifurcated the industry. On the one hand there are traditional MedTechs, which predominantly focus on existing customers and market legacy physical offerings in slow growing markets. On the other hand, there are many small companies and a few very large medical technology corporations, which have embraced AI driven big data patient-centric solutions.

A brief history

Big data has its genesis in the 1950s and 1960s when scientists and mathematicians began exploring the possibility of using computers to process large amounts of data to make intelligent decisions. This led to the development of technologies such as the first neural networks, which laid the foundation for modern Deep Learning. In the 1980s, researchers at IBM popularized the concept of big data to describe the process of collecting and analyzing large amounts of data, which empowered organizations to gain insights from information that previously was too complex to process. The 1990s saw the development of AI and ML, which enabled computers to learn from data and make decisions without the need for explicit programming. By the early 2000s, AI-based algorithms empowered machines to learn from data and make predictions. Many organizations, across a range of industries, saw the commercial opportunities of this and acquired capabilities to collect, store and analyse large amounts of information to identify patterns and trends that were previously impossible to detect. Without large amounts of data, AI and ML techniques are less effective, which is significant for healthcare and the medical technology industry.

Big data in healthcare

AI driven big data strategies are becoming increasingly important in healthcare. This is because AI techniques applied to masses of health-related information can improve patient care, enable more effective decision-making, reduce costs, identify new treatments, explore new markets, and create more efficient healthcare systems. Further, such applications can provide more accurate and timely diagnoses, as well as insights into how various treatments affect different people. As increasing amounts of health information become available, and data handling techniques improve, so traditional MedTech companies will have opportunities to boost their growth by complementing their physical devices and volume-based care with digital assets and personalised care.

Paper-based mindset

Until recently health professionals were responsible for most of the different types of data associated with a patient’s treatment journey, which included medical histories, known allergies, medical and clinical narratives, images, laboratory examinations, and other private and personal information. Until the early 2000s these data were recorded on paper and stored in filing cabinets across numerous healthcare departments. It was not until 2003 that the US Institute of Medicine used the term ‘electronic health records’ (EHR). By 2008, only ~10% of US hospitals were using EHRs, which increased to ~80% by 2015. As EHRs became standard practice across multiple providers and data interoperability issues were resolved, the provision of healthcare improved, and medical errors and healthcare costs were reduced. Currently, the American National Institutes of Health (NIH) is inviting 1m people from diverse backgrounds across the US to help build a comprehensive big data set, which can be used to learn more about how biology, environment and lifestyles affect health in the expectation of discovering new ways to treat and prevent disease.

Trust and medical algorithms

As AI driven big data applications have increased, so trust in algorithms has been raised as an issue. This has been a major concern in healthcare. To address this challenge, explainable AI, has been developed. This is an AI technology that explains decisions and actions made by algorithms in a way that is easily understood by health professionals and patients. Explainable AI has helped to create trust in algorithms by providing a level of transparency, understanding and accountability. Further, incorporating feedback from medical professionals, patients, and other stakeholders into the development of medical algorithms has also helped to build trust. However, this entails collecting a wider variety of data than many healthcare companies are used to.

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Big data healthcare strategies and security

With the increasing number of big data and AI healthcare solutions, cybersecurity has become a concern. Reducing this involves using technologies such as data encryption, secure cloud computing (see below), and authorization protocols to protect data stored in large databases. Additionally, organizations may use AI-driven applications to monitor their systems to find anomalies, detect malicious activity and unauthorized access to sensitive, personal information. To ensure the security of healthcare data, organizations also employ measures such as risk assessments, incident response plans, and regular security training of their staff.

Cloud storage and services

Since the early 1990s, big data have benefitted from cloud storage, which makes it easier to store and access data over the internet and helps businesses to become more efficient and productive. It also offers organizations scalability, more control over their data and reduced costs. Organizations can: (i) easily increase their storage capacity as their data needs grow, (ii) access their data from anywhere in the world, and (iii) stop investing in expensive local storage devices. Further, cloud storage is becoming more secure, with encryption and other security measures making it safer to store data.

Companies moving their data from local storage devices to the cloud is more than just a simple transfer process and can be a complex, multi-year journey. Any organization that has accumulated several legacy databases and infrastructures will have to develop and manage a hybrid architecture to transfer the data. However, once in place and shared among stakeholders, cloud-based platforms can assist in unlocking clinical and operational insights at scale while speeding up innovation cycles for continuous value delivery. In combination with a secure and interoperable network of connections to hospital systems, cloud-based solutions represent an opportunity for healthcare leaders to unlock the value of data generated along the entire patient journey, from the hospital to the home. By turning data into insights at scale, it is possible to empower healthcare professionals by helping them to deliver personalized care, improved patient outcomes and lower costs.

The cloud also offers an increasing number of computing services. These are provided by companies such as Amazon Web Services, Google Cloud Platform, Microsoft Azure, IBM Cloud, Oracle Cloud, and Rackspace Cloud. The services include: (i) Infrastructure-as-a-Service (IaaS), which provides users with access to networks, storage, and computing resources, (ii) Platform-as-a-Service (PaaS) helps users to develop, run, and control applications without the need to manage infrastructure, (iii) Software-as-a-Service (SaaS), provides access to a variety of applications, (iv) Data-as-a-Service (DBaaS), gives users access to several types of databases, and (v) Serverless Computing enables users to run code without needing to provision or manage servers. Such services are expected to continue growing and help to transform healthcare. The provision of cloud computing services in healthcare makes medical record-sharing easier and safer, automates backend operations and facilitates the creation and maintenance of telehealth apps. The increasing use of data and cloud services by MedTech companies helps to break down data silos and develop evidence-based personalized solutions for a connected patient journey. In 2020, the healthcare cloud computing market was valued at ~US$24bn, and it is expected to reach ~US$52bn by 2026, registering a CAGR of >14% during the forecast period. Major drivers of cloud services include the increasing significance of AI driven big data applications.

Changes the nature of R&D

Further, the cloud can change and speed up R&D. The starting point for MedTech R&D should be evolving patient needs and affordability. Healthcare-compliant cloud platforms offer a flexible foundation for the rapid development and testing of AI driven big data solutions created by cross functional teams working across an entire life cycle of an application: from development and testing to deployment. This changes medical technology companies’ traditional approach to R&D by transforming it into short cycles undertaken by multiple stakeholders. This modus operandi is replacing traditional lengthy and expensive R&D often carried out in an organisational silo and constrained by annual budgeting cycles. This often means that a significant length of time passes before an innovation gets into the hands of health professionals and patients for testing. Digital health solutions, on the other hand, can be tested by physicians and patients early in their development and improved features quickly added.

Free and easy to use AI and ML software libraries

In the early 2000s, when AI and ML were in their infancy, companies needed data engineers with advanced mathematical capabilities to build complex AI systems. Today, this is unnecessary because of the development of simplified AI and ML libraries such as PyTorch and Tensorflow. These are free, easy to use, open-source, scalable AI, and ML packages, which reduce the need for data engineers to have advanced mathematical skills to build effective software health solutions. PyTorch, released in 2016, was developed by Facebook and then Meta AI, and is now part of the Linux Foundation. The technology is known for its ease of use and flexibility, making it favoured by developers who want to rapidly prototype and experiment with new ideas. Its tools support graphics processing, which is popular with deep learning medical imaging strategies that involve training large, complex models on big data. TensorFlow was developed by the Google Brain team and originally released in 2015 for internal use. It is a highly scalable library for numerical computations and allows its users to build, train and deploy large-scale ML models. Both platforms have become significant open-source tools for AI and ML due to their ability to support the development and training of complex models on large datasets. They have been widely adopted by researchers and developers throughout the world and are regularly used in a variety of applications relevant to the medical technology industry. Significantly, they give smaller MedTechs a competitive advantage.

Disruptive effects of AI driven big data strategies

The development and availability of big data and predictive AI help small medical technology companies enter markets, grow, and strengthen their competitive positions, which has the potential to change market dynamics. Over the past decade, several large medical technology companies have experienced their markets dented by small companies, which have successfully used open-source AI applications to leverage big data. For example, Philips Healthcare’s market was affected by the emergence of innovative offerings developed by new entrants using cloud computing services and big data from medical wearables. Above we described how Philips robustly responded to this and became a market leader in AI data-driven patient monitoring technology. Siemens Healthineers’ market share suffered from small MedTechs with innovative AI driven offerings. Further, the rise of digital imaging technology caused GE Healthcare’s market share to shrink. These vast companies have since developed AI driven big data strategies and bounced back. However, traditional MedTechs that fail to leverage big data and AI capabilities risk being left behind in an increasingly competitive digitalized industry.

Small MedTechs using big data and AI

Examples of small MedTechs that leverage big data, AI, and ML techniques to capture share of large underserved fast-growing market segments include Brainomix, which was spun out of Oxford University, UK, in 2010 and serves the stroke market. Iradys, a French start-up specialising in interventional neuroradiology. Elucid, a Boston, US-based MedTech founded in 2013, which has developed innovative technology that supports the clinical adoption of coronary computed tomography angiography, and Orpyx Medical Technologies, a Canadian company that provides sensory insoles for people living with diabetes. These are just a few examples of small agile companies that collectively have helped to bifurcate and disrupt segments of the medical technology industry by developing offerings predicated upon big data, AI and ML that deliver faster, more accurate diagnoses to ensure that patients get the treatment they need, when they need it.

Brainomex’s lead product offering is a CE-marked e-Stroke platform, which has been developed using data from images sourced across 27 countries including the UK, Germany, Spain, Italy, and the US and provides fast, effective and accurate analysis of brain scans that expedite treatment decisions for stroke patients. The platform has been adopted across multiple healthcare systems throughout the world, and for the past two years, England’s National Health Service (NHS) has been using the technology on suspected stroke patients. Early-stage analysis of the technology predicated on >110,000 patients suggests that eStroke can reduce the time between presenting with a stroke and treatment by ~1 hour and is associated with a tripling in the number of stroke patients recovering with no or only slight disability - defined as achieving functional independence - from 16% to 49%. With this disease, time is of the essence because after a stroke, each minute that passes without treatment leads to the death of ~2m neurons (nerve cells in the brain), which cause permanent damage. It can be challenging for health professionals to determine whether stroke patients need an operation or drugs, because the interpretation of brain scans is complicated and specialist doctors are required. Sajid Alam, stroke consultant at a large regional hospital in the UK, (Ipswich Hospital), reflected: “As a district general hospital, we don’t have ready access to dedicated neuroradiologists to interpret every stroke scan. Having Brainomix’s AI software gives us more confidence when interpreting each scan.”

Intradys is a French start-up, which develops algorithms that combine ML and mixed reality to empower interventional neuroradiologists and help them enhance the care of stroke patients. Orpyx Medical Technologies provides sensory insoles for people living with diabetes who have developed peripheral neuropathy to help prevent foot ulcers. The insoles collect data on pressure, temperature, and steps and give feedback to the wearer and healthcare professionals. Elucid is a Boston-based MedTech founded in 2013. The company’s offerings are predicated on big data, AI, and ML to provide fast and precise treatments that improve the outcomes of patients with cardiovascular disease and reduce healthcare costs. Heart attack and stroke are primarily caused by unstable, non-obstructive plaque (the buildup of fats, cholesterol, and other substances in and on the artery walls) that often goes undiagnosed and untreated. Current non-invasive testing cannot visualize the biology deep inside artery walls where heart disease develops. Elucid’s lead offering is an FDA-Cleared and CE-marked non-invasive software to quantify atherosclerotic plaque.

Takeaways

The potential benefits for medical technology companies that leverage AI driven big data strategies include: (i) improved diagnoses and treatments, (ii) enhanced patient journeys and outcomes, (iii) cost savings, (iv) a better understanding of stakeholders’ needs, (v) superior decision-making, (vi) more effective products and services, and (vii) increased competitive advantage. Big data strategies may also be used to uncover insights from large datasets to develop predictive models that can automate repetitive tasks, optimize care processes, free up resources for healthcare professionals to focus on providing care, and staying ahead of the competition by providing greater insights into customer trends and needs. Medical technology companies that do not leverage AI driven big data strategies to develop innovative products for growth and competitive advantage potentially risk: (i) falling behind the competition in terms of product innovation, (ii) missing out on key market opportunities, as data-driven insights can help identify new trends and customer needs, (iii) struggling to keep up with the changing pace of technological change, as staying ahead of the competition requires a deep understanding of the latest developments in data-driven product development and (iv) losing the trust of customers, as they may be wary of MedTechs that do not use advanced technologies to develop their product offerings. Future significant growth for medical technology companies is more likely than not to come from AI driven big data strategies. Start collecting data.

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Should MedTechs follow surgeons or patients?

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medical devices

patient groups

The traditional strategy of the medical devices industry has been to maximise the experience of the surgeon
This has resulted in paying little attention to the demands of patients
Surgeon populations are shrinking while the general population is growing, aging, becoming ill and demanding care
This creates care gaps, which are challenging to reconcile, prolong unnecessary suffering and cause unnecessary deaths
Reconciling the shrinking supply of health professionals with the increasing healthcare demands has given more weight to patient demands
MedTechs will be obliged to recalibrate their approach to patients principally because regulators are involving them in the approval process of medical devices
Patient centric digital therapeutic solutions help to reduce care gaps
However, developing such digital therapeutics and involving patients will not come easy to traditional MedTechs because of their lack of capabilities and organizational culture
Notwithstanding, to be relevant in the future, MedTechs will need to continue to improve their ties with surgeons while increasing their focus on the large and rapidly growing patient demands

Should MedTechs follow surgeons or patients?

Traditional MedTech business models are overwhelmingly focussed on manufacturing physical devices for surgeons to use in episodic, hospital-based, interventions. Over decades, a symbiotic relationship between surgeons and medical device manufactures has been established and led to significant commercial success for both parties. This has meant that MedTechs have not paid the attention they should have to the growing demands of patients, which include primary prevention and screening through diagnosis and staging to treatment, rehabilitation, and the subsequent management of a condition. Should medical device companies double-down on their business models to follow surgeons, or should they change approach and follow patients?

In this Commentary

This Commentary has 2 sections: (i) Follow surgeons, and (ii) Follow patients. Section1 suggests that medical device companies will need to continue their mutually beneficial relationships with physicians but tighten their governance ties. Further, leaders might consider some aspects of surgeon populations, which could impact their business model. These include: (i) the increasing shortages and aging of surgical populations, (ii) burnout among surgeons that prompts early retirement, and (iii) the prevalence of unnecessary surgeries. Section 2 considers the business model of MedTechs following patients and suggests that this is likely to become more relevant in the future as regulators are encouraging patient participation in the approval process for medical devices. Further, patient demands are supported by advancing technologies and smart platforms such as PatientsLikeMe. Patient centric solutions tend to be digital therapeutics, based on software rather than hardware. Solutions that address patient care pathways require scarce digital, data management and artificial intelligence (AI) capabilities, which MedTechs tend not to have. To stand a chance of attracting these, MedTechs will need to develop non-hierarchical, agile working cultures with the capacity to innovate at speed. The significance of business models that improve patients’ care pathways is illustrated by two recent, transformative MedTech deals. Takeaways suggest MedTechs should continue following surgeons, albeit under enhanced governance principles and involve patients in the development of devices and increase their capabilities to provide patient centric digital solutions.

SECTION 1
Follow surgeons

The medical devices industry is “big business”. In 2021, the US devoted ~US$199bn (~5.2%) of annual national health expenditures to medical devices. Over the past four decades mutually beneficial relationships between surgeons and medical device companies have been built, and this forms the basis of a dominant industry business model to “follow surgeons”.

Surgeons play a crucial role in the conceptualization, development, and enhancement of medical devices; they influence hospital purchasing decisions, and are compensated for providing these services. Further, they are remunerated for representing MedTechs at conferences, giving speeches on behalf of corporations, and playing a critical role in training physicians to use devices because their efficacy is often associated with a specific use technique that needs to be taught. Further, surgeons may receive research grants from MedTechs and be promoted because of their association with a successful innovation. More recently, with the rise of medical device start-ups, the financial incentives to surgeons have included equity stakes in lieu of cash for various contributions. This means that significant financial ties between medical device companies and surgeons are relatively common, which can be the basis for potential conflicts of interest.

MedTechs code of conduct

AdvaMed, a US medical device trade association, based in Washington, DC, is aware of such conflicts and suggests that physicians should be compensated at fair market rates for work they perform. The Association is against equity compensation and says that there should be no link between the commercial success of a medical device and a physician. AdvaMed encourages voluntary, ethical interactions and advises member organizations and physicians to disclose all potential conflicts of interest, which include consulting arrangements, training, support of third-party educational conferences, participation in sales and promotional meetings, gifts, grants, and charitable donations.

Despite AdvaMed’s best efforts its suggested code of conduct does not appear to work. A bibliometric analysis of 100 clinicians receiving compensation from 10 large MedTechs and published in the November 2018 edition of JAMA Surgery found that conflicts of interest were not declared in 63% of 225 research projects that resulted in publications. Given the increasing significance of environmental, social, and governance (ESG) criteria among socially conscious investors to screen potential investments, it seems reasonable to suggest that MedTechs might consider regularly disclosing all their financial ties with surgeons and health professionals.

More issues to consider

In addition to the increasing significance of ESG issues, there are some further questions associated with MedTech business models that follow surgeons, which corporate leaders might wish to reflect upon. These include: (i) the surgeon population is aging and shrinking, (ii) surgeons have a higher propensity to burnout than other medical specialities, and (iii) surgeons are responsible for a substantial number of unnecessary operations. Let us describe these in a little more detail.

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Shrinking surgeon populations

Throughout the world, populations of surgeons and health professionals are shrinking. Findings of a 2016 US Department of Health and Human Services report suggest that by 2025, there will be shortages in 9 out of 10 surgical specialties in America, with the greatest reduction in ophthalmology, orthopaedics, urology, and general surgery. Research prepared for the Association of American Medical Colleges (AAMC) by the healthcare consulting firm IHS Markit and published in June 2020, suggests that, by 2032, the US could lack ~23,000 surgeons. Although the US has a higher number of total hospital employees than most countries, nearly half of that workforce is comprised of non-clinical staff who are not directly involved in delivering care. For instance, compared to Italy and Spain, America has fewer practicing physicians per capita: 2.6 per 1,000 inhabitants, compared to 4 in Italy and 3.9 in Spain. According to the World Health Organization (WHO), the global shortage of health workers is projected to reach 13m by 2033.

Care gaps

One reason for this projected shrinkage is that a large percentage of surgeons are nearing traditional retirement age. For instance, more than 2 in 5 currently active American doctors will be ≥65 years within the next decade. Further, people are living longer, and a substantial percentage are not staying healthy and need care. According to the US Census Bureau the number of Americans ≥65 is expected to reach ~84m by 2050, which is ~2X the 2012 level of 43m. Among this older population there is a large and growing prevalence of chronic lifetime diseases such as cancer, diabetes, heart conditions, respiratory diseases, and mental illness. In the US there are ~150m people with such conditions and ~40% of these are living with ≥2 chronic diseases. According to the US Centers for Disease Control and Prevention, ~90% of the US$4.1trn annual medical spend (~20% of the country's GDP) is attributable to chronic disorders. Such trends magnify the vast and growing pressure on a shrinking pool of health professionals, and this creates challenging care gaps.

Digital therapeutics

Care gaps will not be reduced by medical schools training more physicians and nurses. This takes too long to have an impact on the size of the problem. The UK has attempted to reduce care gaps by importing physicians: ~190,000 of the 1.35m NHS staff in England report a non-British nationality, and ~27% of NHS staff in London report a nationality other than British. This policy raises some ethical issues as most are imported from developing economies with underdeveloped healthcare systems and a scarcity of health professionals. The option to import physicians is not open to the US because its immigration policies make it difficult for international health professionals to work in America. Recently, many advanced industrial economies have sought to reduce their care gaps by developing digital therapeutic solutions for patients, which extend the reach of physicians by overcoming time, place and personal constraints that limit care delivery.

Surgeon burnout

Findings of a research study published in the June 2018 edition of the Journal of the American College of Surgeons suggest that the prevalence of burnout among surgeons has increased over time. The research references the 2015 Medscape Physician Lifestyle Report, which argues that burnout among surgeons is on the rise and documents burnout rates among various specialisms ranging ~37% to ~53%, with general surgeons nearing the top of the list at 50%. Research on the impact of the COVID-19 crisis on healthcare professionals published in the December 2021 edition of the Mayo Clinic Proceedings, found that ~1 in 3 US physicians expressed a clear intention to reduce their work hours, and ~1 in 4 intended to leave their practice altogether. Such trends are concerning considering the aging of the US population and the subsequent increased pressure this puts on healthcare systems.

Many factors contribute to surgeon burnout. Common causes among American surgeons include long work hours, delayed gratification, challenges with work-home balance, and issues associated with patient care in a changing healthcare ecosystem. According to the WHO’s International Classification of Diseases, (ICD-11) burnout results from “chronic workplace stress that has not been successfully managed”. It is characterised by being emotionally exhausted, feelings of cynicism and loss of empathy and a sense of low personal accomplishment with respect to one’s work. A meta-analysis of the prevalence of burnout published in the March 2019 edition of the International Journal of Environmental Research and Public Health suggests that surgeons experience elevated rates of depression and psychiatric distress and posits that burnout among junior surgeons is at an epidemic level, which affects patient safety, quality of care and patient satisfaction.

Unnecessary surgeries

Another issue for medical device leaders to consider is the incidence rates of unnecessary surgeries. These are any intervention, which is not needed, not indicated, or not in the patient’s best interest when weighed against other available options. Unnecessary surgeries are not a recent phenomenon: they are a significant reality that continue to expose patients to unjustified surgical risks. In 1976, the American Medical Association (AMA) called for a congressional hearing to address the issue, claiming that each year there are “2.4m unnecessary operations performed on Americans at a cost of US$3.9bn and that 11,900 patients had died from unneeded operations”. Across the US, the phenomenon is patchy. A cross-sectional study of five US metropolitan areas and published in the January 2022 edition of the Journal of the American Medical Association found significant differences in physician treatment recommendations across a range of specialisms.

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Most common unnecessary surgeries

The incidence rates of unnecessary surgeries appear more prevalent in spinal, gynaecological and some orthopaedic procedures. Clinical trials have shown that a significant percentage of spinal fusions for back pain do not lead to improved long-term patient outcomes when compared to non-operative treatment modalities, including physical therapy and core strengthening exercises. Despite these findings, spinal fusion rates continue to increase significantly in the US.

Further, women are at high risk of unnecessary hysterectomies and caesarean sections. Although these rates are moderating, a study for the American College of Obstetricians and Gynecologists, suggested that hysterectomies were improperly recommended in ~70% of cases, even though there were non-surgical alternatives. Hysterectomies can lead to bladder and bowel dysfunction, prolapse, and incontinence, as well as a 4-fold increased risk of pelvic organ fistula surgery. A study in Health Affairs found that caesarean rates varied significantly (from 2.4% to 36.5%) in hospitals across the US, even among those with low-risk pregnancies.

Another study published in Health Affairs suggests that after patients received information on alternatives to joint replacement surgeries, ~26% had fewer hip replacements and ~38% had fewer knee replacements. Each year in the US, >1m total hip and total knee replacement procedures are performed.

SECTION 2
Follow patients

It is not uncommon for MedTech leaders to say that they put “patients first” when developing devices. However, although things are changing, which we describe below, this is more rhetorical that factual. MedTech R&D teams tend to be relatively remote, inwardly focussed, and, particularly in the US, patient voices are generally ignored and not perceived as an integral part of the process.

However, the healthcare ecosystem is changing and “following surgeons” cannot constitute an entire strategy for MedTechs. In the future, MedTech business models that follow patients will be driven by patients’ knowledge and their increasing demands to participate in their healthcare decisions, the movement towards personalized care, and regulators’ mandates to incorporate patient perspectives into the development of medical devices and approval processes (see below). Earlier, we suggested that, when surgeons engage with medical device corporations there are competing interests, which often are not disclosed. By contrast, patients are primarily driven by their own safety and wellbeing, which, contrary to surgeons, are grounds for promoting mutual accountability and understanding with healthcare providers.

To remain relevant, MedTechs will need to incorporate patient perspectives and patient data into their business models, not least because patients are co-producers of their health and represent a consistent factor, probably the only consistent factor, throughout the care pathway. Further, patients, empowered by digital therapeutics and health information from wearables, hold invaluable personal data, which are often critical to improving care pathways, and outcomes.

PatientsLikeMe

Patient voices were loud and influential long before MedTechs recognised the significance of engaging patients in development processes. Consider PatientsLikeMe, a digital platform founded in 2004, with a mission to improve the lives of patients by sharing knowledge, experiences, and outcomes. The company quickly grew to become the world’s largest integrated community, health management, and real-world data platform. Via the site, users can document and share their experiences, track their conditions, and communicate with others living with similar disease states. Data generated by patients who use the site are systemically collected and quantified by the company, while providing users with an environment for peer support and learning. Today, PatientsLikeMe has >0.8bn users representing >2,900 conditions. The company makes money by selling the information patients share in de-identified, aggregated, and individual formats. In 2019, the platform was acquired by the UnitedHealth Group, an American multinational healthcare and insurance company, after former President Trump’s administration forced it to seek a buyer because its majority owner was China-based iCarbonX.

Increasing patient input in approval processes for medical devices

What will make MedTechs wake up to the significance of patient perspectives in the development of medical devices are initiatives and demands made by regulators. For the past decade, European regulators through the European Medicine’s Agency (EMA). have solicited patient inputs into their approval process for medical devices. In 2014, the FDA and the EMA created a joint working group to share knowledge and information on patient engagements. In 2007, the Clinical Trials Transformation Initiative (CTTI), a public-private partnership was co-founded by the US Food and Drug Administration (FDA) and Duke University and modelled on the EMA Patients’ and Consumers’ Working Party. CTTI’s mission is to develop and drive patient involvement in the development and approval of devices, which is expected to increase the quality and efficiency of clinical trials. Since its foundation, the CTTI has become a leader in evolving and advancing clinical trials, making them more efficient, and patient focused.

In December 2017, a nationwide request in the US was made for patients and patient advocate groups to join the CTTI and become more involved in healthcare product development and in the FDA product reviews. This call came ~1 year after the 21st Century Cures Act became law in December 2016. The Act’s intention is to expedite the process by which new medical devices and drugs are approved by easing the requirements put on companies seeking FDA approval for new products and indications. Under Section 3001 of the Act, the FDA is required to report any patient experience data that were used to support an approval process and to publicly provide aggregate reports on agency use of those data at five-year intervals. This suggests that MedTechs wanting new FDA approvals will need to provide patient-driven data.

These initiatives are driven by an ever-improving consumer-controlled social and health data ecosystem, advancements in personal genetic understanding, and increased healthcare cost-sharing. Patient-driven changes are systematically beginning to inject more than token patient participation and viewpoints into all stages of device and drug development.

A cultural shift

Improving patient engagement in the development process of medical devices will be challenging for MedTechs that have focussed their business models mainly on manufacturing physical devices and building relationships with surgeons, rather than developing digital assets for patients. The latter requires scarce data management and AI capabilities, which do not thrive in conservative hierarchical organizations. Rather, they require a culture, which promotes innovation at speed and agile ways of working. A recent survey of European executives by The Economist Intelligence Unit, found that poor collaboration between a company’s IT function and its business units slows progress in a firms’ digital objectives. MedTechs that are slow to develop digital capabilities that address patient needs and integrate these into their business models risk not being a party to decisions shaping the emerging healthcare ecosystem.

The increasing significance of scarce AI talent

Digital therapeutics predicated upon AI techniques, which are growing in significance with healthcare systems, require large amounts of data collected from electronic health records (EHR), medical images, and information from patients’ wearables. Key areas where AI techniques can improve the delivery of care include: (i) diagnoses, (ii) managing patient journeys, and (iii) improving patient engagement. Streamlining these three areas can ease administrative burdens on healthcare systems, optimize physicians’ time, improve patient outcomes, and lower costs. However, a significant challenge for MedTechs is the scarcity of essential capabilities to develop digital strategies. A 2020 research report by Deloitte Insights suggested that there are significant shortages of “AI developers and engineers, AI researchers, and data scientists”. Corporate leaders might consider bolstering their chances of attracting digital and AI talent by: (i) leveraging their company’s unique value and purpose, (ii) prioritizing and offering best-in-class training over recruiting, (ii) prioritizing diversity, and (iv) engaging with universities.

Transformative MedTech deals

The significant shift in MedTech strategies towards patients is demonstrated by two recent transformative deals: Teledoc’s 2020 acquisition of Livongo and Siemens Healthineers AG’s 2021 acquisition of Varian Medical Systems Inc. Both combinations emphasise the significance of digitalization and demonstrate the strategic shift towards patients.

The US telehealth giant Teledoc’s acquisition of Livongo for US$18.5bn was the largest digital healthcare deal in history, which valued the combined company at US$38bn. Livongo, founded in 2014, provides digital therapeutic solutions to improve patient health outcomes for a range of chronic conditions including diabetes, and hypertension. The other transformative MedTech digitalization deal was the German health imaging giant Siemens Healthineers AG’s acquisition of cancer device and software specialist Varian in April 2021 for US$16.4bn. Siemens Healthineers is the leading supplier of medical imaging solutions used to support the planning and delivery of radiotherapy. Varian was the leading supplier of radiotherapy solutions. Both deals were substantially larger than Amazon’s US$0.75bn 2019 acquisition of PillPack, and Google’s US$2.1bn 2021 acquisition of Fitbit, and they signal a new and permanent path for MedTech companies towards a digital-first future.

Takeaways

To remain relevant MedTechs will need to continue their symbiotic relationships with surgeons albeit in a modified form, while becoming significantly more patient centric and digitally savvy. However, a bigger challenge Western MedTechs will have to face in the next five years is whether they can develop digital therapeutic solutions for patients fast enough to compete with the looming threat from China’s large and rapidly growing capacity to develop and market medical robotics for surgeons and innovative digital therapeutics for patients. This will be the subject of a forthcoming Commentary.

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2 years, 11 months ago

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Leaning-in on digital and AI

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Digital therapeutics and artificial intelligence (AI) techniques are increasing their influence on the medical devices industry and fuelling a shift of healthcare away from hospitals into peoples’ homes
This poses a challenge to traditional medical device companies (MedTechs) that solely focus on manufacturing physical devices for hospital-based episodic interventions
Some MedTechs are changing their business models and strategies, diverting their focus to patients, and adding digital therapeutic applications to their legacy offerings
Zimmer-Biomet and Stryker are MedTechs that have embraced digital therapeutics and AI
Stryker’s CEO advises other MedTechs to ‘lean-in on AI and don’t be sceptical’

Leaning-in on digital and AI

Rapidly growing digital therapeutic technologies are disrupting hospital-based healthcare and posing a challenge to those medical device companies that are slow to complement their legacy physical product offerings with patient centric digital solutions. Such technologies have the potential to enhance patient outcomes, reduce healthcare costs, and give providers access to new revenue streams. Today, digital solutions increasingly contribute to the prevention, management, and treatment of a wide range of diseases and health conditions. Their rapid growth is driven by advances in the behavioural sciences, artificial intelligence (AI) techniques and the increase in the consumer health wearables market, which is converging with the regulated medical devices market. This convergence facilitates care to move away from hospitals and into peoples’ homes.

In this Commentary

This Commentary describes how two decades ago a world-renowned surgeon and CEO of a large hospital group warned that digital therapeutics would disrupt healthcare and push a lot of hospital-based care to peoples’ homes. For years the medical devices industry did not pay too much attention to such warnings and continued to focus on manufacturing physical products for surgeons in hospitals. The Commentary describes two leading MedTechs - Zimmer-Biomet and Stryker – which have recently begun to reinvent themselves and embrace digital therapeutics and AI techniques expected to improve patient outcomes and reduce surgical inconsistencies. We briefly develop this thought process by suggesting how machine learning AI techniques might be employed to reduce the high failure rates of spinal surgeries. The Commentary describes the large and growing global market for digital therapeutics and prescription digital therapeutics, a large proportion of which are enabled by wearables and telehealth. The market for digital therapeutics is large enough and growing fast enough to pose a threat to traditional medical device companies that solely manufacture physical offerings and fail to develop digital solutions to improve patient journeys. Although some MedTechs neither have the resources nor the mindsets to develop digital solutions, it seems reasonable to suggest that, in the medium term, they will be obliged to acquire or develop such assets to remain competitive. However, achieving this will be challenging.

Early warnings of change

Over a decade ago, Devi Shetty, warned health professionals to prepare for care to become heavily influenced by digital therapeutics, which he argued would move a significant portion of care away from hospitals and into peoples’ homes. This warning had resonance because Shetty is a surgeon as well as being the founder and executive director of Narayana Health, one of India’s largest hospital groups. In an interview with HealthPad in 2012 he suggested that hospitals were becoming less relevant in a new, and rapidly growing digitally driven healthcare ecosystem. “Healthcare of the future will be dramatically different to that of the past. The future is not an extension of the past. In the future, chronic illnesses will be treated at home”, said Shetty and continued, “The next big thing in healthcare is not going to be a magic pill, a faster scanner, or a new operation. It’s going to be digital therapeutics, which will dramatically change the way health professionals interact with patients. Every step of a patient’s care journey will be informed by software. This will make healthcare safer for the patient and shift most of hospital activities to the home. If a physician doesn’t have to operate on a patient, the patient can be anywhere, distance doesn’t matter”. Shetty repeated this argument at a 2022 Microsoft ‘Future Ready’ conference suggesting that, “95% of people who are unwell, don’t need an operation. All they need is medical intervention, which can be enabled by digital technology and telehealth and treated in the home”.

Leading MedTech companies reinventing themselves

Two decades after Shetty’s warning, the CEOs of Zimmer-Biomet and Stryker, respectively Bryan Hanson, and Kevin Lobo, have made substantial commitments to digital therapeutic solutions that improve patient outcomes, reduce surgical inconsistencies and extend treatment and monitoring to the entirety of patients’ journeys, much of which takes place in patients' homes. Medical device companies that fail to develop software solutions or link-up with providers of such technologies could risk losing market share to emerging competitors.

Zimmer-Biomet and digital therapeutics

Zimmer is a player in total knee arthroplasties, which involve replacing the knee joint with a prosthetic device that carries out similar functions as a person’s own knee. The surgery has become routine. In 2020, US physicians carried out ~1m total knee arthroplasties, and by 2030, ~2m such procedures are expected to be carried out annually in the US. In 2020, the global total knee replacement market was valued at ~US$7.8bn, expected to grow at a CAGR of >6%, and reach ~US$12.5bn by 2027.

In 2021, Zimmer and Canary Medical, a software company, which had developed an implantable digital therapeutic application, received approval from the US Food and Drug Administration (FDA) to market Persona IQ: the world’s first ‘intelligent’ total knee replacement. Zimmer’s traditional knee prosthesis is embedded with Canary’s technology to provide a range of automatic, reliable, and accurate data and analyses that facilitates remote monitoring and tracking of patients' post-operative progress long after they have left hospital. Following this success, Hanson is directing a substantial percentage of Zimmer’s R&D spend on the development of digital therapeutic solutions, and Persona IQ is expected to be the first in a pipeline of intelligent joint prostheses.

Stryker and digital therapeutics

In a March 2022 interview, Stryker’s CEO, Kevin Lobo, stressed his ongoing commitment to increase his company’s digital therapeutic and AI capabilities. In 2021 Stryker acquired Gauss Surgical, which had developed Triton™, an AI-enabled app for real-time monitoring of blood loss during surgery. “After a mother gives birth”, says Lobo “it’s important to calculate how much blood she’s lost. Today, this quantification is very crude and rudimentary. Triton™ allows you to use your smartphone to accurately measure the amount of blood that is in sponges as well as cannisters. It can distinguish between different liquids and measure only the haemoglobin. This is critical to determining whether a mother needs a transfusion or not. You would be shocked, even here in the US, how often a mother doesn’t get a transfusion she needs or gets one she doesn’t need”.

In January 2022, Stryker acquired Vocera Communications for ~US$3bn. Vocera is a US Nasdaq traded company founded in 2000 that makes wireless communications systems for healthcare and has developed a digital platform, which helps connect caregivers and "disparate data-generating medical devices". The platform is used by >2,300 facilities throughout the world, including ~1,900 hospitals. Interoperability between the platform and >150 clinical and operational systems reduce health risks and enhance the consistency of surgical procedures, speeds up staff response times; and improves patient outcomes, safety, and affordability. According to Lobo, "Vocera will help Stryker significantly accelerate our digital therapeutic aspirations to improve the lives of caregivers and patients".

Lobo has made AI a shared service. Stryker employs ~200 software engineers that are using AI. “This we never had before at Stryker. AI is going to be a central core competence for our company. I can see that all our business units are going to be using AI within the next two to three years”, says Lobo, who expects AI inspired digital therapeutic applications to “lead to more consistent outcomes for our procedures”. According to Lobo this is “a big deal because today there are a lot of variations in surgical outcomes”.

AI and its potential impact on spinal surgery

Spinal surgery is a good example of significant inconsistencies in outcomes. Each year, ~7.6m spinal surgeries are performed globally, and ~1.2m in the US, where spinal fusions account for ~60% of all procedures. Although ~50% of primary spinal surgeries are successful, ~30%, ~15%, and ~5% of patients only experience a successful outcome after the second, third, and fourth surgeries, respectively. Machine learning AI techniques applied to patients’ electronic medical records (EMR) and clinical data could potentially reduce this high failure rate by predicting what product and surgical procedure could produce an optimal solution for individual patients.

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Robotic surgical spine systems, China, and machine learning

Let us briefly explain. Machine learning, a subfield of AI, is the capability of a machine to imitate intelligent human behaviour. It is the process of using mathematical models of data to help a computer to learn and adapt without following explicit human instructions. Machine learning employs algorithms (a set of instructions for solving a problem) to identify patterns in large data sets, potentially comprised of multiple sources, and then uses these patterns to create a predictive model. With increased training on more data, the results of a machine learning algorithm may become more accurate, much like how humans improve with practice. Once this point is reached, regulatory approval for the algorithm can be applied for under the FDA’s category of “software as a medical device”. Once approved, the algorithm may be used to help reduce the high failure rates of spinal surgery.

The digitalization of healthcare

MedTech leaders should be mindful of the impact that digital therapeutics is having on their industry, which goes far beyond embedding legacy physical offerings with sensors. Digital therapeutics is a rapidly growing healthcare modality, predicated upon scientific advances in the behavioural sciences and AI techniques, that help individuals to form habits, which improve their health, reduce healthcare costs and boosts productivity. Such software tools increasingly are used for the management and prevention of a range of debilitating and costly chronic conditions, including mental health challenges, substance abuse disorders, opioid-induced conditions, cancer, cardiovascular diseases, metabolic disorders, respiratory conditions, and inflammatory diseases. Chronic disease is a public health emergency. In the US, six in ten citizens are living with at least one chronic disorder. Not only are such conditions the leading cause of hospitalizations, disability, and death, but their total annual cost to the US exchequer, which includes lost economic productivity, is ~US$3.7trn.

The market for digital therapeutics is driven by a combination of different factors, including: technological advances, particularly consumer wearables (such as the Apple Watch and Fitbit apps, see below), the high penetration levels of mobile telephony, the growth of telehealth, the increasing demand from consumers to take more control of their health, aging populations, the large and escalating incidence of preventable chronic diseases, the need to control healthcare costs, and rising investments in digital therapeutics. According to Statista, a business data platform, in 2021 the number of people globally using digital therapeutic applications reached ~44m. Almost double the number of 2020. By 2025 the number of users is expected to reach >362m, and this only includes devices that have sought validation in clinical trials. The global digital therapeutics market is growing at a CAGR of ~31% and is projected to reach ~US$13bn by 2026, up from ~US$3.4bn in 2021.

An advantage of digital health modalities is their ability to deliver continuous personalized care and bridge large care gaps created by shortages of specialized health professionals. In the US, for instance, there are ~6,500 specialist physicians in full-time clinical practice to treat diabetes (endocrinologists), but there are ~27m Americans living with the condition. Similar health gaps occur in other common disease states. In developing economies, care gaps are even wider. For example, India has a chronic shortage of doctors and nurses and has ~77m people living with diabetes and ~55m people living with cardiovascular disease. The latter kills ~5m Indian citizens each year. India, like many other Asian countries, has chosen to deal with care gaps by establishing itself as a major presence in the digital health economy. By several key metrices, from internet connections to app downloads, both the volume and the growth of India’s digital economy now exceeds those of most other countries. Expect this shift to increasingly influence corporations looking to enter and extend their franchises in large and rapidly growing medical devices markets in developing economies.

Cybersecurity challenges

Headwinds for digital therapeutic applications, particularly in Western democracies, include challenges of informed consent to use, safety and transparency, algorithmic fairness and biases, and data privacy. Digital therapeutic applications tend to be more vulnerable to cyberattacks than traditional medical devices, which are manufactured according to strict protocols by a handful of regulated manufacturing partners. By contrast, digital applications often rely on third-party software, which may be less rigorous than the usual medical device standards. Cybersecurity threats to digital therapeutics include data theft, identity disclosure, illegally accessing data, corruption of data, loss of data, and violation of data protection. These risks are accentuated by the fact that the modality is predicated upon the continuous monitoring of patients’ vital signs and increased connectivity between physicians, providers, payers, and patients and breaches can occur at various points along the path of data movement. Risk mitigation includes encryption protocols and the ability to control data access and data integrity. An indication of how quickly the US policy environment around cybersecurity is changing is in March 2022, the US Senate unanimously passed legislation, which would usher in sweeping changes to the federal legal landscape relating to cybersecurity and mandate companies to report damaging hacks and ransomware payments to the government.

Prescription digital therapeutics

Another indication of the growing significance of digital therapeutics is a recent US policy push to establish an equivalence between some wearable healthcare solutions and prescription drugs and medical devices. On 10 March 2022, two US senators, Catherine Cortez Masto, D-Nevada, and Todd Young, R-Indiana, introduced legislation to expand Medicare and Medicaid coverage to include prescription digital therapeutics. Medicare is a federally run US medical insurance programme covering ~64m citizens >65 and younger disabled people. Medicaid is a government assistance programme, funded by both federal and state governments, but run by individual states and covers the medical expenses of ~75m Americans on low incomes and with limited resources. This is significant because of the vast number of individuals covered by these health insurances and the fact that the US regulatory hurdle is one of the toughest in the world. Prescription digital therapeutics fall under the FDA category of “software as a medical device” and are subject to the same stringent requirements as drugs and medical devices, and must demonstrate evidence of clinical effectiveness, safety, and quality. After that they require a prescription for use, following a consultation with a doctor.

The bill would standardize US reimbursement codings for prescription digital therapeutics, which is expected to incentivize American doctors to increase prescribing them. This would not only facilitate greater access to a wide range of digital therapies for >44% of Americans receiving state healthcare support but potentially create a precedent for US private health insurance companies to increase their coverage of prescription digital therapeutics. This would significantly help to propel the modality into mainstream healthcare.

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The future of health wearables

In June 2020, as the COVID-19 crisis escalated, the FDA expanded its guidance for non-invasive patient-monitoring technologies, including the Apple Watch’s ECG function. In 2021, ~34m Apple Watches were sold worldwide; up from ~22.5m in 2018. In addition to smartwatches, there is a wide range of intelligent wearables that monitor your vital signs in real time, promote self-management of chronic conditions, help people to engage with their own health and incentivize them to change their behaviour to improve their health and lifestyles. Thus, digital therapeutic applications have the potential, among other things, to slow the development of chronic disorders and reduce hospital visits and readmissions. The size and growth rate of the wearable health technology market influences the decisions of insurers, employers, health providers and producers. For example, insurers use data from wearables to adjust their premiums, corporates derive benefits from their employees using wearables, which include healthier company cultures, a reduction in employee turnover, an increase in workplace safety and enhanced efficiency.

In the US, consumers' use of wearables increased from 9% to 33% in four years as of 2021. The use of wearables is likely to increase as they become more conventional, connectivity expands, and more accurate sensors are developed. Such developments are likely to provide further incentives for insurers and employers to use wearables to develop healthier lifestyles to boost profitability and cut costs. According to Gartner, a technological research and consulting firm, in 2021 worldwide user spending on wearable devices was ~ US$82bn, ~18% increase from the previous year. This seems reflective of consumers, encouraged by the COVID-19 pandemic, becoming more conscious about their health, wellbeing, and changes to their lifestyles. According to a 2021 Deloitte’s survey, ~58% of US households own a smartwatch or fitness tracker, and ~39% of Americans personally own a smartwatch or fitness tracker. ~14% of consumers have bought their fitness devices since the start of the COVID pandemic in 2020, and activities such as counting steps, workout performance, heart health, and sleep quality monitoring are amongst the most popular activities.

Telehealth

Another factor driving the shift of care away from hospitals to peoples’ homes is the development of telehealth. The COVID-19 pandemic caused telehealth usage to surge as consumers and providers sought ways to safely access and deliver healthcare. According to the US Centers for Disease Control and Prevention (CDC), by late March 2020, telehealth had increased >154% compared to the same period in 2019. Since the peak of the COVID-19 pandemic, telehealth has become a permanent part in the delivery of healthcare. The telehealth market is expected to rise to >US$397bn by 2027 from US$42bn in 2019. According to Devi Shetty the history of healthcare will be written in two sections, BC, and AC: before COVID and after COVID. “COVID-19 disrupted and transformed healthcare and forced inward looking healthcare professionals to rapidly change and adopt digital therapeutic technologies”, says Shetty.

The legacy of the COVID-19 related surge in digital therapeutics is an opportunity to make permanent hybrid care modalities created during the pandemic. The foundations for the opportunity are described in a 2021 McKinsey research report, which suggests that the pandemic, (i) accelerated the growth and acceptance of telehealth, which “stabilized at ~38X higher than before the crisis”, (ii) improved the attitudes of consumers and providers towards telehealth, (iii) made permanent some regulatory changes put in place during the pandemic (for example, Medicare and Medicaid’s expansion of reimbursable telehealth codes introduced in 2021 for US physician fee schedules, which have been made permanent), (iv) fuelled venture capital’s digital health investments, and (v) drove the adoption of digital therapeutics across a wide range of disease states.

Shift in mindset

In the changing healthcare ecosystem, a primary strategic objective for MedTech leaders is to define relevant planning cycles and efficaciously manage from one cycle to the next. The current planning cycle in the medical devices industry is influenced by data, AI techniques, and patient centric digital therapeutic solutions. To effectively manage this cycle, MedTechs might consider copying Zimmer and Stryker and acquire complementary digital therapeutic assets and capabilities. Adapting M&A knowhow and experience to make such acquisitions is an option but not without risk.

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MedTech must digitize to remain relevant

This is because enterprises with digital assets and capabilities have different cultures, development practices, reimbursement policies and data management policies and practices compared to traditional medical device companies. It seems reasonable to suggest that poorly managed acquisitions could result in MedTechs ending up with a graveyard of unfulfilled digital technologies. To reduce this risk industry leaders might consider following Stryker’s example and recruit experienced digital and AI specialists, and make them a core competence.

Takeaways

In the near-term, disruptive digital technologies present both challenges and opportunities for medical device companies. Zimmer and Stryker have started to reinvent themselves through parallel efforts to digitize their legacy businesses, acquire complementary digital assets, and make AI a core competence. However, many MedTechs have not changed their business models and still focus R&D on making small improvements to existing product offerings. Corporate leaders considering changing their business models and strategies should be mindful that digital and AI assets and capabilities with the potential to create disruptive growth need to be protected from unnecessary bureaucratic burdens common in many traditional companies. To survive and prosper, managers might consider rethinking their operating models for innovation-led growth. The most effective models appear to combine a strategic process with multiple mechanisms for driving innovation development and scale-up. Stryker’s shared service of AI expertise is one example of a contrived core “capability” expected to transform legacy devices into growth engines that could help secure the company’s long-term survival. MedTech CEOs might do well to follow Lobo’s advice and, “lean-in on AI and do not be sceptical.”.

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3 years, 6 months ago

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A prescription for an AI inspired MedTech industry

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data mining

Medtech

A wind of change is blowing through MedTech markets
MedTech markets have matured and are experiencing slower growth and increased competition, which have fuelled endeavours to increase growth rates
Artificial intelligence (AI) techniques applied to data from existing devices have the potential to achieve this and improve care
Obstacles to developing AI solutions include rigid manufacturing mindsets and a dearth of appropriate talent
To remain relevant MedTech leaders will need to “think beyond physical products”, develop new business models, new types of investments and new approaches to R&D
Will a wind of change that is blowing through MedTech markets be perceived as a temporary breeze?

A prescription for an AI inspired MedTech industry

Thinking beyond physical products and the growing significance of AI in MedTech markets

A wind of change is blowing through MedTech markets, which has prompted some key opinion leaders to think beyond physical products and begin to use artificial intelligence (AI) techniques to develop value added services that bolt-on to their existing physical offerings to improve clinical care and economic efficiencies while providing access to new revenue streams.

Bryan Hanson, Zimmer-Biomet’s CEO, recently suggested that >70% of his company’s R&D spend is now being invested in data informatics and robotics. Not far behind is Stryker, another global orthopaedic corporation, which has implemented AI strategies to improve care and differentiate its offerings. Both are thinking beyond their physical products to create a suite of services derived from AI enhanced data collected from their existing devices. Such actions provide a template that can be copied by other enterprises. How long will it take for AI solutions to represent a significant percentage of MedTechs’ revenues?

In this Commentary

This Commentary: (i) describes the growing significance of AI, (ii) explains the difference between data mining, AI, and machine learning, (iii) illustrates AI technologies that have become an accepted part of our everyday lives, (iv) highlights technical drivers of AI solutions, (v) describes obstacles to the development of AI systems, (vi) indicates how such obstacles may be reduced, (vii) describes Zimmer’s and Stryker’s AI driven data initiatives, (viii) suggests that the Zimmer-Stryker AI template has broad potential, (ix) suggests that AI systems can breathe life into 'dead data', (x) provides an example of a company at the intersection of medical information and AI techniques, (xi) describes the origins of the phrase, ‘wind of change’, and defines the ‘winds’ driving change in current MedTech markets, (xii) reports that ~80% of B2B sales in the economy generally are digitally driven, (xiii) provides some reasons for MedTechs’ slow adoption of AI systems, (xiv) floats the idea that the future for producers is to partner with tech savvy start-ups and (xv) describes how US AI supremacy is being challenged.

AI: vast and fast growing

It is challenging for baby boomers and older millennials, who populate MedTechs’ C suites, to fully grasp the potential of AI. This is largely because their corporate careers were underway before the digital age started, and for three decades they have personally prospered from manufacturing physical devices without the help of AI.

A person who understands the potential of AI is Sundar Pichai, the CEO of Alphabet, one of the world’s largest tech companies. In a recent BBC interview Pichai suggested, "AI is the most profound technology that humanity will ever develop and work on . . . If you think about fire or electricity or the Internet, it's like that, but even more profound". This suggests that Hanson is right to redirect Zimmer’s R&D spend towards AI-driven solutions. A February 2021 report from the International Data Corporation (IDC), a market intelligence firm, suggests that the current global AI market is growing at a compound annual growth rate (CAGR) of ~17% and is projected to reach ~US$554bn by 2024.

Data mining, AI, machine learning and neural networks

Among MedTechs’ C suites there is some confusion about data strategies and AI solutions. Many enterprises use data mining techniques on existing large datasets to search for patterns and trends that cannot be found using simple analysis. They employ the outcomes to increase revenues, cut costs, improve customer relationships, reduce risks and more. Although data mining is commonly used when working on AI projects, in of itself, it is not AI. So, let us briefly clarify.

AI is the science and engineering of developing intelligent computer programs to enable machines to provide requested information, supply analysis, or trigger events based on findings. AI creates machines that think, learn, and solve problems better and faster than humans. This is different to traditional computing, where coders provide computers with exact inputs, outputs, and logic. By contrast, AI systems can be “schooled” to carry out specific tasks without being programmed to do so. This is referred to as machine learning, which usually requires large amounts of data to train algorithms [mathematical rules to solve recurrent problems].

A critical element of machine learning’s success is neural networks, which is an AI technique modelled on the human brain that is capable of learning and improving over time. Neural networks are comprised of interconnected algorithms that share data and are trained by triaging those data: a process referred to as ‘back propagation’. In healthcare, machine learning outputs range from the ability to recognise images faster and more accurately than health professionals to making in vivo diagnoses.

AI systems have become an accepted part of our everyday lives without us realising it

Most people are aware of significant AI breakthroughs such as self-driving cars and IBM’s Watson computer winning the US quiz show Jeopardy by beating two of the best players the show had produced. Lesser known, is in 2012, AlexNet, a neural network learning system, won a large-scale visual recognition contest, which previously was thought too complex for any machine. In 2016, Google’s AlphaGo, a machine learning algorithm, defeated Lee Sedol, who was widely considered the world’s greatest ever player of the ancient Chinese game Go. Most observers believed it would be >10 years before an AI programme would defeat a seasoned Go champion. Although Go’s rules are simple, the game is deceptively complex, significantly more so than chess. It has a staggering 10¹⁷⁰ possible moves, which is more than the number of atoms known in the universe. Significantly, machine learning algorithms embedded in AlphaGo, mastered the game without any prior knowledge and without any human input. More recently Google launched AlphaGo Zero, an AI system, which can play random games against itself and learn from it. During the decade of these breakthroughs, AI systems became an accepted part of our everyday lives without us realising it. Examples include, Google searches, GPS navigation, facial recognition, recommendations for products and services, bank loans we receive, insurance premiums we are charged, and chatbots, which organizations use to provide us with information.

Technical drivers of AI systems

In addition to commercial drivers, AI techniques are driven by easy availability of data, an explosion in computing power and the increased use of clusters of graphic processing units (GPUs) to train machine-learning systems. These clusters, which are widely available as cloud services over the Internet, facilitate the training of more powerful machine-learning models. An example is Google's Tensor Processing Unit (TPU), which has the capability to carry out more than one hundred thousand trillion floating-point operations per second (100 petaflops). This has the potential to accelerate the rate at which machine-learning models can be trained. Further, the cloud has made data storage and recovery easier, which has motivated government agencies and healthcare institutions to build vast unstructured data sets that they make accessible to researchers throughout the world to stimulate innovation.

Obstacles to the development of AI systems

So far, we have emphasised the benefits of AI, but there are concerns that machine intelligence will accelerate at an incomprehensible rate, surpass human intelligence, and transform our reality. This is referred to as “singularity”, which has generated concerns from key opinion leaders. Nearly a decade ago, Stephen Hawking, a pre-eminent British scientist, warned in a BBC interview, that singularity “could spell the end of the human race”. More recently, Hawking’s view has been echoed by Elon Musk, founder, and CEO of Tesla and SpaceX, who suggests that AI is, “more dangerous than nuclear warheads and poses a fundamental risk to the existence of human civilization". Musk has called for stronger regulatory oversight of AI, and more responsible research into mitigating its downsides. In 2015, he set up OpenAI, a non-profit research organization, with a mission to promote and develop AI systems that benefit society.

In the June 2018 edition of the Atlantic Review, Henry Kissinger, who served as national security adviser and secretary of state for two US Presidents, described the potential harms from AI by addressing the question: “What would be the impact on history of self-learning machines that acquired knowledge by processes particular to themselves, and applied that knowledge to ends for which there may be no category of human understanding?”. Singularity might be more imminent than once thought. In a book published in 2015, futurist Ray Kurzweil predicted that singularity would occur in ~2045, but a paper published in the June 2020 edition of the International Journal of Astrobiology suggests that it is more likely to occur within the next decade.

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Robotic surgical spine systems, China, and machine learning

Overcoming obstacles to AI

In clinical settings there are growing concerns that complex algorithms can blur the reasoning behind specific machine interpretations and consequent actions of robotic surgical systems. As AI and machine learning develop so surgical robots are expected to become more autonomous and have the capability to make instantaneous diagnoses and pursue immediate therapies, which surgeons using the systems do not fully understand. The failure of humans to understand the workings of an AI system is referred to as an “interpretability challenge”, or more commonly, the “black-box” problem, which could impact future clinical regulations.

Combatting the possible dangers of AI systems not being understood by humans is a relatively new and growing research area, referred to as “Explainable AI” (XAI). XAI attempts to use AI techniques to develop solutions that can describe the intent, reasoning, and decision-making processes of complex AI systems in a manner that humans can understand. This could provide Stryker and Zimmer, and other manufacturers, a solution to potential future regulatory obstacles associated with advances in their robotic surgical systems
.

Zimmer’s and Stryker’s initiatives

In August 2021, the FDA granted De Novo marketing authorization [applicable for a new and novel device whose type has not previously been classified] for a “smart knee”, which Zimmer had developed in partnership with Canary Medical, a data analytics company. The device, called Persona IQ^®, is the world's first and only smart knee cleared by the FDA for total knee replacement surgery. It combines Zimmer’s proven and trusted knee implant, Persona^® The Personalized Knee^®, with Canary’s proprietary sensor technology, which provides real-time feedback on how surgical implants and devices are working by generating self-reports on patient activity, recovery, and treatment failures, without the need for physician intervention and dependence upon patient compliance. The partnership is also expected to leverage Canary’s machine learning capabilities to identify further patterns in data from implants that could help clinicians catch problems, such as infections or loosening of the implants before they worsen. Persona IQ^® will work together with Zimmer’s remote care management platform, mymobility^® with Apple Watch^®, as well as with other components of the ZBEdge™ connected intelligence suite of currently available, and soon to be launched, digital and robotic technologies engineered to deliver transformative data-powered clinical insights, shared seamlessly across the patient journey, to improve patient outcomes.

In January 2021, Stryker acquired OrthoSensor, a privately held technology company that makes intraoperative sensors for use in total joint replacements. Stryker expects these sensors to empower surgeons with AI-driven solutions and enhance its surgical robotic systems by eventually providing them with the capability to predict surgical outcomes. Additionally, OrthoSensor’s remote patient monitoring wearables, combined with a cloud-based data platform, are expected to significantly improve Stryker’s data analytics capabilities. According to a Stryker press release issued at the time of the acquisition, “OrthoSensor quantifies orthopaedics through intelligent devices and data services that allow surgeons and hospitals to deliver evidence-based treatments for all healthcare stakeholders. The company’s advancements in sensor technology, coupled with expanded data analytics and increasing computational power, will strengthen the foundation of Stryker’s digital ecosystem”.

The Zimmer-Stryker AI template has potential across MedTech

Despite Zimmer’s and Stryker’s AI-driven data initiatives to improve their respective competitive advantages and gain access to new revenue streams, few MedTechs collect, and store the data produced by their existing devices, and even fewer use such data to provide novel AI solutions. The Zimmer-Stryker template for achieving this is not limited to orthopaedics. For example, consider neuro critical care and traumatic brain injuries (TBI), which are a “silent epidemic”. Each year, globally ~69m individuals sustain TBIs. In the US, every 15 seconds, someone suffers a TBI. In England, ~1.4m people present at A&E departments each year following a head injury.

Despite extensive research, successful drug therapies for TBI have proven to be elusive. The gold standard management of the condition is to monitor intracranial pressure (ICP) and attempt to avoid elevated levels, which can cause further insults to an already damaged brain. Currently, there are no FDA approved means to identify advance warnings of changes in ICP. However, it might be possible to create an early warning of ICP crises by applying machine learning algorithms to standard physiological data produced by existing medical devices commonly used to monitor patients with TBI. This would not only provide time for interventions to prevent further trauma to critically ill patients but would also give producers access to new revenue streams.

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MedTech must digitize to remain relevant

Breathing life into dead data

There are potentially limitless opportunities to improve care by breathing life into 'dead data'. This can be achieved simply by applying AI solutions to underutilized data from existing medical devices. The global MedTech industry is comprised of ~6,000 companies (mostly small to medium size). The overwhelming majority of these manufacture devices that produce, or could produce, patient data. These companies serve ~14 surgical specialisms each of which treat numerous conditions. For each condition there are millions of patients at any one time. For each patient, multiple devices used in therapies display real time data. Most producers are awash with dead data because they do not collect, store, and analyse these data to improve the quality of care. AI systems can change this.

A MedTech start-up at the intersection of medical information and AI techniques

A start-up, which understands the clinical and economic potential from the intersection of medical data and AI solutions is Komodo Health, which was founded in 2014. According to Web Sun, the company’s co-founder, and president, “We had a vision that integrating robust data with software solutions was the way forward for healthcare at a time when no one was doing this”. Komodo has created an AI platform, which it refers to as a "healthcare map", comprised of large-scale anonymous health outcome data from hundreds of sources.

In January 2020, Komodo announced a deal to import Blue Health Intelligence’s patient data onto its platform. Blue Health provides US healthcare claims data and actionable analytics to payers, employers, brokers, and healthcare services. The combined database charts >325m individual patient care journeys through tests and therapies at hospitals and clinics. In March 2021, Komodo raised US$220m to extend its platform to offer real-time assessments of patients’ healthcare journeys to detect disparities in the quality of care and outcomes, and to provide a basis for interventions aimed at improving outcomes and lowering costs.

The ability to introduce clinical insights into enterprise workflows potentially helps producers and providers close gaps in care journeys and address unmet patient needs. Not only are Komodo’s services designed to deliver timely interventions and alerts to improve care, but the company also records and reports the performance of specific medical products on patient cohorts. These data provide a basis to develop and market further innovative healthcare services, and novel therapeutics, which are expected to boost Komodo’s revenues.

A wind of change

We borrowed the ‘wind of change’ phrase used in our introduction from a famous speech made by British Prime Minister Harold Macmillan to the Parliament of South Africa on 3 February 1960 in Cape Town. Macmillan was referring to a system of institutionalised racial segregation, called Apartheid, which enforced racial discrimination against non-Whites, mainly predicated on skin colour and facial features. Despite the UK Prime Minister’s belief that in 1960, the days of White supremacy in South Africa were numbered, it took >30 years before Apartheid was ended and Nelson Mandela was inaugurated as the first Black President of South Africa on 10 May 1994. Mandela was an anti-apartheid activist and lawyer, who had spent 27 years as a political prisoner under the Apartheid regime.

A wind of change is now blowing through MedTech markets. In less than a decade, healthcare will be faced with significantly more patients, more data, more technology, more costs, more competition, and less money for producers and providers. Over the past five years, US providers’ profit margins have fallen, in Europe the gap between public health expenditure and government budgets has increased, and throughout the world healthcare systems are under budget pressure and actively managing their costs. With such strong headwinds, a sustainable future for MedTechs might be to reduce their emphasis on manufactured products distributed through labour intensive sales channels and increase their AI service offerings using data from their existing devices. Over the past five years AI solutions have become more prolific, easier to deploy, and increasingly sophisticated at doing what health professionals do, but more efficiently, more quickly and at a lower cost.

~80% of B2B sales are digital

In addition to AI solutions being used to improve clinical outcomes, they can be employed to enhance business efficiencies. A previous Commentary described how AI systems can help to transform traditional labour intensive MedTech supply chains and personalise sales. A recent study undertaken by Gartner, a global research and advisory firm, suggests that, “Over the next five years, an exponential rise in digital interactions between buyers and suppliers will break traditional sales models, and by 2025, ~80% of B2B sales will occur in digital channels”. Giant tech companies are taking advantage of this to enter healthcare markets, MedTechs have been slow to implement such changes despite the boost in online engagements provided by the COVID-19 pandemic.

Reasons for slow adoption of AI systems

So, why are MedTechs slow to implement AI solutions to enhance clinical outcomes and improve economic efficiencies? Over ~3 decades they have achieved double-digit revenue growth from manufacturing physical devices and marketing them through labour intensive channels in a few wealthy regions of the world with relatively benign reimbursement policies. During this period of rapid growth and commercial success, MedTechs have not been required to confront data issues, bridge the science, technology, engineering, and mathematics (STEM) skills gap, and commit to new structures, new processes, new behaviours, and new aptitudes.

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Will a rear mirror mindset slow the adoption of technologies poised to influence the spine market?

This suggests that despite a wind of change, now blowing through MedTech markets and challenging traditional business models and strategies, it could be perceived as a 'temporary breeze' and nothing will change. However, a step change in the direction of more AI solutions might occur when digital natives [people who have grown up in a digital age] replace digital immigrants [people whose careers were well underway before the onset of the digital age] in MedTechs’ C suites. According to a Gartner executive, “As baby boomers retire and millennials mature into key decision-making positions, a digital-first buying posture will become the norm. . . . . . Sales reps will need to embrace new tools and channels, as well as a new manner of engaging customers, matching their sales activity to their customers’ buying practices and information collecting needs”. A 2019 research report from the Boston Consulting Group (BCG), suggests that companies, which use AI systems to personalise sales can expect productivity gains of ~10%, and incremental revenue growth of ~10%.

Partnering with tech savvy start ups

Currently, many MedTechs neither have the mindsets nor the in-house STEM capabilities to create AI enhanced services. So, what might be a way forward? STEM skills, although scarce, tend to reside in people <30. Although there are ~68m of these people in the US, people with STEM skills tend to prefer to work either for giant tech companies or tech start-ups devoted to leveraging the potential of AI. Giant tech companies and start-ups are outside the comfort zones of most MedTechs. However, in the future, they may be obliged to partner with tech savvy start-ups engaged in developing AI driven solutions. Such collaboration will be challenging because it requires MedTechs to change their business models, create new ways of making strategic investments, and develop novel approaches to R&D that encompass a broader spectrum of partners.

Most of MedTechs’ R&D investment is consumed by incremental innovations to their current suite of devices. This tends to reinforce existing revenues rather than develop disruptive technologies aimed at capturing new revenue streams. Such strategies are efficacious in stable, fast growing economic environments, but lose their edge in slower markets. It seems reasonable to assume that, as market conditions tighten, MedTechs will need to consider shifting their R&D strategies towards the development of more disruptive technologies. We see this already in Stryker’s R&D investment in robotic surgical systems and Zimmer’s proposed R&D spend on AI, data informatics and robotics.

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China’s rising MedTech industry and the dilemma facing Western companies

and

Can Western companies engage with and benefit from China?

US supremacy challenged

US tech giants are investing heavily in AI R&D and driving the adoption of advanced technologies in healthcare. Although these companies have made, and will continue to make, a significant contribution to the field, it would be a mistake to think that they have AI healthcare markets sewn up.

Three Chinese tech giants, collectively referred to as ‘BAT’, are also investing heavily in AI systems. All three offer services well beyond their core products and have far-reaching global ambitions. BAT is comprised of Baidu, China’s largest search provider, Alibaba the nation’s biggest eCommerce platform and Tencent, which runs WeChat that has access to >1bn users on its platform. For the past five years BAT has been expanding into other Asian countries, recruiting US talent, investing in US AI start-ups, and forming global partnerships to advance their AI ambitions.

In addition to these private endeavours, China has made AI a national project. Since 2017, Beijing has been pursuing a three-step New Generation AI Development Plan, which aims to turn AI into a core national industry. To this end, China is vigorously carrying out research on brain science, brain computing, quantum information and quantum computing, intelligent manufacturing, robotics, and big data. Already, China has become a world leader in AI publications and patents. The nation’s global share of AI research papers increased from 1,086 (4.26%) in 1997 to 37,343 (27.68%) in 2017, surpassing any other country, including the US. Most AI patents are registered by companies in the US and Japan. However, when it comes to AI patents registered by research institutes, China is the undisputed leader. According to a 2021 report on China's AI development, ~390,000 AI patent applications were filed in China over the past decade, accounting for ~75% of the world total. Beijing’s competitive advantage in big data and AI strategies is driven by a combination of its weak privacy laws, a national plan, huge government investments, concerted data-gathering, and big data analytics by the BAT tech giants and others. Currently, China’s AI market is valued at ~US$22bn, and by 2030, the nation is expected to become a leader in AI-empowered healthcare businesses and the world’s leading AI power.

Beijing’s policies have given rise to hundreds of AI driven start-ups aimed at gaining access to new revenue streams in China’s rapidly growing healthcare market. Western MedTechs might consider accepting Beijing’s Made in China 2025 policy, partner with these tech savvy start-ups and jointly benefit from the nation’s current 5-year economic plan aimed at a “healthier China”.

Takeaways

We have presented an AI-driven prescription for MedTechs to enhance the quality of care while providing access to new revenue streams. We suggest that this can be achieved by bolting on AI solutions to existing devices, and over time through partnerships with tech savvy start-ups. But ~30 years of double-digit growth derived from manufacturing physical products and distributing them through labour intensive sales channels might have cemented mindsets among C suite incumbents that find it challenging to think beyond physical product offerings. This could suggest that the wind of change, now blowing through MedTech markets, will be perceived as a temporary breeze that does not require thinking beyond physical products, and AI solutions will be a long time coming.

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WeDoctor’s impact on global healthcare

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WeDoctor

Experienced Western healthcare professionals have little knowledge of WeDoctor a Chinese internet healthcare start-up positioned to have a significant impact on global healthcare systems over the next decade
Founded in 2010 and backed by Tencent, a US$0.5trn Chinese conglomerate, WeDoctor has grown rapidly to become an influential US$6bn enterprise
WeDoctor bundles services AI and big data strategies into smart devices to help unclog China’s fragmented and complex healthcare system and increases citizens’ access to affordable quality healthcare
WeDoctor has expanded its franchise outside of China and has global ambitions to become the “Amazon of healthcare”
Is WeDoctor an exemplar for Western healthcare providers?

WeDoctor’s impact on global healthcare

The speed and scoop of technological change is forcing traditional healthcare providers to move beyond the comfort of their production models, embrace services and develop smart devices, which support customer-centric, value-based, data driven strategies. To illustrate this shift, we describe a Chinese internet healthcare start-up WeDoctor, which is having an impact on re-engineering China’s overly bureaucratic, fragmented and complex healthcare system and is positioned to influence the delivery of value-based healthcare services globally in the next decade.

In this Commentary

This Commentary describes WeDoctor and some of its recent activities to expand its influence and market share. Three things of note:

The partnerships that WeDoctor has developed with payers and providers, which are different to conventional transaction-based contracts
WeDoctor’s pragmatic approach to evolving technologies, which differentiates it from Western technology companies entering healthcare markets
WeDoctor might be considered as an exemplar and its strategy copied by Western companies. Because most giant Western technology companies are banned in China, local firms have stopped copying Western counterparts and innovate. This has resulted in many Chinese apps and services being better than their Western rivals. For example, Huawei’s mobiles outperform Apple’s, and China is ahead on 5G, mobile money and artificial intelligence. In 2016 the US technology publication Wired ran a cover story entitled: “It’s Time to Copy China”.

Smart Clinics

Imagine going to your primary care physician and, within a 15-minute consultation, receiving up to eleven tests, which include analysing your blood and urine, taking your blood pressure and measuring the electrical activity of your heart; and all the tests being delivered by a small portable all-in-one diagnostic device weighing just 5 kilos (11Ibs) and situated on the table of your doctor’s consulting room.

Imagine further that your test results are returned in minutes rather than days or even weeks and uploaded to your cloud-based electronic medical record to be reviewed in real time by your doctor. Simultaneously, your data are anonymously merged with similar information collected from millions of other patients and stored in a cloud file embedded with AI, in the forms of machine learning and cognitive computing, which complement and enhance the capabilities of your doctor. Your physician plays a key role in interpreting your test results and providing you with a diagnosis and treatment options as well as giving you an essential human touch of reassurance and guidance. Notwithstanding, as soon as you leave your doctor’s office, your mobile phone will suggest smart ways to monitor and manage your condition remotely. Information about your condition will appear on your social media feeds, you will also receive prompts for treatments, alerts about health supplements and suggestions about appropriate insurance policies. Currently, no amount of money can buy such a service in advanced wealthy Western economies, but it is a lead device of WeDoctor, which is available in rural China and in other emerging countries. According to Frost and Sullivan, a consultancy, the China market alone for remote diagnostics is currently estimated to be US$2bn and projected to grow to US$28bn in 10 years. WeDoctor’s near-term goal is to capture a significant share of this market and help re-engineer China’s healthcare system by nudging individuals with the right piece of information at the time to maintain their health. This makes the device valuable to patients, healthcare providers and payers.

Reverse innovation

It seems reasonable to assume that, in addition to being useful in China and other emerging countries, WeDoctor’s all-in-one diagnostic device is well positioned to help enhance primary care practice in developed Western nations by a process of ‘reverse innovation’. This refers to a strategy where a product offering, which is specifically developed for emerging countries is subsequently successfully marketed in developed wealthy nations. It is particularly relevant to healthcare systems, which are universally challenged to deliver high quality outcomes with increasingly scarce resources. The strategy was formalized in a paper entitled, ‘How GE is Disrupting Itself’, which was published in the October 2009 edition of the Harvard Business Review (HBR), and subsequently expanded into a book published in 2018 entitled, ‘Reverse Innovation in Healthcare: How to make value-based delivery work’.

In the early 2000s, General Electric (GE) took an affordable, high quality portable ultrasound device, which it had developed for the Chinese market and successfully marketed it in the US and elsewhere. GE found that ‘affordability’ and ‘portability’ were universally valued healthcare factors. Jeffrey Immelt, then chairman and CEO of GE and one of the authors of the 2009 HBR paper, challenged other multinationals, “to see innovation opportunities in emerging markets in a new light”. Reverse innovation was more widespread than Immelt first thought and over the past decade the strategy has become a significant part in the armoury of many multinational corporations. Although the strategy is relevant for value-based healthcare,it is rarely practiced by Western healthcare providers.

The starting point for reverse innovation healthcare strategies is emerging markets where the rapid growth in the demand for quality healthcare outstrips the development of resources and infrastructure. This creates significant opportunities for Western companies with smart solutions to common healthcare challenges. Similar to GE’s portable ultrasound device, WeDoctor’s smart all-in-one diagnostic device, in time, could be marketed in developed regions of the world where healthcare systems are struggling to improve patient outcomes while reducing costs.

WeDoctor’s pragmatism

WeDoctor, founded by Liao Jieyuan an AI specialist, is backed by Tencent, which is one of the world’s largest technology and internet companies with a market cap of US$0.5trn and a mission to enhance the quality of life through the development and global distribution of emerging technologies. WeDoctor has a market cap of US$6bn, an established network in China of some 240,000 doctors, 2,700 large premier hospitals, over 15,000 pharmacies in 30 of China’s 34 provinces and about 160m platform users and joins a growing contingent of technology companies with a mission to change the healthcare industry, which to-date has resisted online disruption.

Notwithstanding, there is a significant difference between giant Western technology companies who have entered healthcare markets and WeDoctor. While the former have tended to invest heavily in aspirational projects such as unravelling the medical mysteries of anti-ageing, and AI systems to replace clinicians, WeDoctor has been more pragmatic and focused on making money by unclogging bottlenecks in the Chinese US$1trn healthcare market. Although Liao is an AI expert and WeDoctor is a significant user of AI, Liao believes, “AI won’t replace doctors, but will become an important tool for doctors to help improve their efficiency and accuracy”. WeDoctor has a practical mission: to enhance access to quality medical resources, improve patient outcomes and reduce costs. Indeed, Liao founded WeDoctor simply to help people book physician appointments, which is challenging in China. Chinese primary care practices are underused due to the poor distribution of resources, a lack of reputable practitioners and the nation’s relatively low number of doctors per capita. Further, waiting times to see a hospital specialist are long and patients reportedly have to pay significant amounts of money to middlemen to secure appointments.

AI healthcare systems are more challenged in the West than in China

In 2017, the Chinese central government released a plan to become the world leader in AI by 2030, aiming to surpass its rivals technologically and build a domestic industry worth almost $150 bn. WeDoctor and other Chinese healthcare providers are mindful that AI is a transformative technology for healthcare partly because of its ability to recognise patterns in vast amounts of data and to detect and quantify biomarkers in non-solid biological materials. Jamie Susskind, in his book Future Politics, published in 2018, suggests that doctors consulting both medical and legal big data banks in support of diagnoses and treatments, will become as commonplace as consulting standard images such as MRIs or X-rays. And if such data banks are not consulted it will be considered negligent.

WeDoctor’s AI systems hold out the prospect of delivering rapid diagnoses, efficient triage, enhanced monitoring of diseases, improvements in personalized care and making medicine safer. Notwithstanding, a limiting factor in the use of AI systems in healthcare generally is neither investment nor the technology, but the ability to amass vast amounts of reliable personal and genomic data. This is a bigger challenge in the West than in China. More robust privacy legislation, higher levels of security and broader-based ethical concerns in the West are substantial obstacles. A significant advantage of WeDoctor is the freedom in China to collect, store, analyse and use patient, personal and genomic data on an unparalleled scale. China has yet to establish laws to protect such personal information and is systematically building health profiles on its 1.4bn citizens, which, together with Beijing’s commitment to AI, will provide scientists in China a significant advantage to lead and dominate life sciences over the next decade.

WeDoctor is one of several similar start-ups

WeDoctor is just one of several recent Chinese online start-ups employing evolving technologies to improve China’s healthcare system. Another is Good Doctor, which is an offshoot of the Ping An Insurance Group, a financial giant with a US$181bn market cap, annual revenues of US$142b and 343,000 employees. Both start-ups compete to build smart clinics in rural China.

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Can Western companies engage with and benefit from China?

WeDoctor endeavours to extend its franchise

In addition to its smart diagnostic device, WeDoctor has leveraged Tencent’s substantial expertise and resources in mobile, AI and cloud-based technology to develop a significant customer-focused retail prowess and is rapidly developing a range of services for healthcare providers and manufacturers of medical devices. This positions the company well to have a significant near-term impact on Asia’s healthcare systems. In 2018 alone, WeDoctor has strengthened and extended its franchise by entering into a number of partnerships with a range of healthcare stakeholders, which include insurance companies, specialist in the procurement and distribution of medical devises and also investment companies interested in improving the physical infrastructure of southeast Asian healthcare systems. We describe some of these partnerships, which enable WeDoctor to consolidate and expand its market position both in China and internationally and suggest that Western healthcare providers should be considering similar partnerships to help them make the product to service shift.

WeDoctor and the AIA insurance group

In May 2018, WeDoctor formed a strategic alliance with the AIA Group, which is the largest public listed pan-Asian life insurance group with customers in China and across the Asia-Pacific region. WeDoctor and AIA are aligned in their ambition to partner with consumers in China and across southeast Asia to provide innovative quality healthcare and wellness offerings and financial protection solutions. The partnership provides WeDoctor with preferred access to AIA’s customer base and thereby strengthens and enlarges its networks and strategies to deliver affordable, digitally-enabled personalised healthcare offerings. AIA becomes WeDoctor’s preferred provider of life and health insurance solutions and gains access to its 160m registered users. According to Liao the partnership, “leverages AIA’s long history and extensive operations across the Asia-Pacific region . . . and is crucial to meeting the diversified life and health insurance requirements of our growing user base as we look to anticipate users’ needs, through our platform’s expanding functionality and our mission to transform healthcare through technology. This partnership not only helps us to cement our position as the premier technology-enabled healthcare solutions platform in China but also supports us as we expand our international presence in the years to come”.

WeDoctor and China’s IVF market

Also, in May 2018, WeDoctor made a strategic investment in Reproductive Healthcare,a new in-vitro fertilisation (IVF) group, which was formed by a merger between two of Hong Kong’s largest and most reputable IVF practices. This was WeDoctor’s first investment outside of Mainland China and represents a significant milestone for the implementation of its international strategy. The new company provides a comprehensive range of IVF services, which include intra-uterine insemination, frozen-thawed embryo transfer and egg freezing services for China and the Asian region. The new company’s established frozen embryo services benefit from findings of a paper published in the January 2018 edition of the New England Journal of Medicine, which suggest that pregnancy and live birth rates are similar among women who use fresh or frozen embryos.

WeDoctor and its expanded international IVF market

In August 2018 WeDoctor, entered into an agreement with the Mason Group and Aldworth Management to acquire an 89.9% stake in Genea, Australia's leading provider of integrated advanced assisted reproductive technology (ART) services. Headquartered in Sydney, Genea has over 400 employees and is a leading international fertility group with a 30-year track record and a significant presence in New Zealand and Thailand as well as Australia. The company offers a comprehensive range of ART services, including IVF, egg and embryo freezing, genetic testing, sperm banking, day surgeries and pathology. Genea has developed proprietary technologies, including culture media and embryo transfer catheters, which are used in more than 600 clinics across 60 countries and is the only ART platform, with both services and technology, in the industry worldwide. The agreement strengthens both WeDoctor’s international strategy and its ability to increase its share of China’s US$2bn and fast-growing IVF market. WeDoctor also is targeting a bigger share of the outbound Chinese IVF medical tourism market, which in 2017, grew approximately 40% year-over-year to approximately US$151m. According to Grand View Research, the global IVF market in 2017 was valued at about US$15bn and is expected to grow at a CAGR of around 10%.

WeDoctor is China's first smart medical supply chain solutions and procurement company

In July 2018, WeDoctor entered into a joint venture (JV) with IDS Medical Systems Group (idsMED Group), to form idsMED WeDoctor China Ltd. This is China's first smart medical supply chain solutions and procurement company and is positioned to transform China’s fragmented, multi-layered and relationship-driven medical device distribution systems.

idsMED is a leading Asian medical supply chain solutions company specialising in the distribution of medical devices and consumables, clinical education and hospital design and planning. It represents over 200 global MedTech companies and has extensive Asia Pacific distribution networks with access to over 10,000 healthcare institutions. The company has 1,600 employees, including 700 experienced field sales, product and clinical specialists and 300 professional bio-medical engineers providing installation and maintenance services.

The JV, owned 51% by WeDoctor and 49% by idsMED Group leverages the respective companies’ strengths, innovative resources and networks to procure medical devices and services centrally by connecting global manufacturers directly to China’s hospitals and healthcare providers. The JV will further enhance WeDoctor’s value proposition by managing and optimizing China’s entire medical supply chain, which until now has been fragmented, overly bureaucratic and complicated. In addition, idsMED WeDoctor will set up medical education and training academies throughout China to deliver and promote medical devices and clinical education as well as accredited medical training courses for doctors and nurses.

WeDoctor & Fullerton

In September 2018 WeDoctor entered into a strategic partnership with Fullerton Health a Singapore-headquartered healthcare service provider. The alliance is, “In line with WeDoctor’s international growth strategy and will extend our reach and facilitate our development in Asia,” said Jeff Chen, WeDoctor’s Chief Strategy Officer. The JV provides WeDoctor access to Fullerton Health’s 500 healthcare facilities and its network of over 8,000 healthcare providers across eight Asian pacific markets. Fullerton Health benefits from WeDoctor’s footprint in China and broadens its patients’ access to online healthcare consultations. In the near term, both companies aim to broaden their reach in the corporate healthcare service market by opening onsite medical centres for businesses across China. In addition, the partnership plans to create about 100 primary care and specialist outpatient facilities.

Takeaways

Healthcare has become digital and global and long ago, the geo-political axis of the world has moved East. To remain competitive, Western healthcare providers must increase their knowledge and understanding of initiatives in China and southeast Asia, be prepared to transform their strategies and business models and engage in partnerships with a range of healthcare stakeholders, complementary enterprises and start-ups in emerging nations.

Two of China’s largest healthcare challenges are the uneven distribution of its services and its vast and escalating costs. The nation has an underserved primary care sector and the most qualified and experienced doctors are concentrated in a few premier mega-city hospitals, which account for 8% of the total number of medical centres but handle 50% of the nation’s outpatient visits. These challenges are not unique to China but experienced by healthcare systems throughout the world.

WeDoctor is an exemplar of how such universal healthcare challenges might be improved by a combination of evolving smart technologies and strategic partnerships with a range of healthcare stakeholders. As MedTech companies continue to transform their business models to increase customer-centricity, the types of partners they need to engage will only expand. In a rapidly moving market, keeping abreast of these potential collaborators is critical.

Another takeaway is that WeDoctor does not use AI and big data technologies to resolve the mysteries of medicine, but to increase access to healthcare, improve diagnoses, enhance patient outcomes and lower costs. The company also is increasing the effectiveness and efficiency of healthcare providers by simplifying and centralizing procurement processes of medical devices and pharmaceuticals.

Once WeDoctor has helped to improve China’s healthcare infrastructure, the nation would have amassed the world’s largest personal, medical and genomic data base of its citizens. WeDoctor will then be well positioned to turn its formidable AI prowess to accelerating R&D in lifesciences, improving the accuracy of early diagnoses, enhancing the monitoring of devastating life-threatening diseases and improving personalized care.

WeDoctor is an exemplar for Western MedTech companies.

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The IoT and healthcare

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IoT

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Over the next decade the combination of big data, analytics and the Internet of Things (IoT) will radically change healthcare
The social media revolution has raised peoples’ awareness of lifestyles and healthcare
The rise of smart watches and fitness sensors combined with IOT and Artificial Intelligence (AI) paves the way for preventative medicine becoming a key driver in the management of straining healthcare services and spending
Big data, analytics and the IoT is positioned to accelerate change away from output-orientated healthcare systems to value-based outcome-orientated systems
Patients and payers are increasingly aware of the opportunities and demanding change
The slowness for MedTech companies to change creates opportunities for newcomers to penetrate and grab share of healthcare markets
Regulation and requirements to undergo significant clinical studies to become standard of care will slow consumer and patient access to services

The IoT and healthcare

The Internet of Things (IoT) is positioned to radically transform healthcare. There are powerful social, demographic, technological, and economic drivers of this change. We describe some of these, and suggest that, within the next 10 years, there will be hundreds of millions of networked medical devices sharing data and knowhow, and this will drive a significant shift away from traditional healthcare systems focused on outputs to value-based systems dedicated to prevention and improving outcomes while lowering costs.

The IoT and its potential impact on healthcare

The IoT, which Cisco refers to as “the Internet of Everything” and GE as the “Industrial Internet” is also referred to as “machine-to-machine” (M2M) technologies, and as “smart sensors”. Whatever term is used, the IoT is an ever-expanding universe of devices embedded with microchips, sensors, and wireless communications capabilities, which enable them to collect, store, send and receive data. These smart devices and the data they collect are interconnected via the Internet, which significantly expands their potential uses and value. The IoT enables connectivity from anywhere to anywhere at any time, and facilitates the accumulation of big data and artificial intelligence (AI) to either complement or replace the human decision-maker. Over the next decade, anything that can be connected to the Internet probably will be. The Internet provides an almost ubiquitous, high-speed network, and cloud-based analytics, which, in nanoseconds, can read, analyse and act upon terabytes of aggregated medical data. Smart distributed services are positioned to become a powerful tool for health providers by optimizing medical results, preventing mistakes, relieving overburdened health professionals, improving patient outcomes, and lowering costs.

Two approaches to a common healthcare challenge

Let us illustrate the shift in healthcare referred to above by considering two different approaches to a shared healthcare challenge: that of providing people with personalized advice about maintaining and improving their wellbeing in order to ward-off lifestyle related illnesses, such as type 2 diabetes (T2DM). This is important because T2DM is a devastating lifestyle induced condition, which affects millions, costs billions, and in most cases can be prevented by lifestyle changes.

Approach 1

One approach is the world’s first nationwide diabetes prevention program, Healthier You, which was launched by NHS England, Public Health England and Diabetes UK in 2016. It is aimed at the 11m people in England thought to have pre-diabetes, which is where blood sugar levels are higher than normal, but not high enough for a diagnosis of T2DM. About 5-10% of people with pre-diabetes progress to "full-blown" T2DM in any given year. Healthier You is expected to be fully operational by 2020. Each year thereafter the program is expected to recruit 100,000 people at risk of T2DM. Personal lifestyle coaches will periodically monitor the blood sugar levels of these, and make recommendations about their diets and lifestyles. This is expected to prevent or slow the people with pre-diabetes progressing to full-blown T2DM.

Approach 2

The second approach is GymKit and Chatbox. The former is a new feature Apple is expected to add to its watch in late 2017, and the latter is a mobile app developed by Equinox, a New York-based health club chain, for its members.

Gymkit will enable the Apple watch to have seamless connectivity to the overwhelming majority of different kinds of cardiovascular equipment used in most fitness centres. Currently, there are a variety of smartphone apps, which allow gym users to connect to cardiovascular machines, but these are at best patchy. Gymkit is different, and will automatically adjust a user’s personalized needs to any cardiovascular machine without the user having to press a button. Itwill then wirelessly collect a range of data - if on a treadmill: speed, duration, incline, etc., - and combine these data with the user’s heart rate, age, gender, weight and body type to make health-related calculations and recommendations, and wirelessly transmit these to the user.

Chatbox does something similar. Ituses artificial intelligence (AI) to simulate the human voice, which talks to new health club members, encourages them to set personal goals, and sends them messages when they fall short. Further, Chatbox has sensors, which track users while they are in the gym, and suggests ways of improving and extending their personalized workouts. A survey, undertaken by Equinox of its members across 88 of its facilities reported that Chatbox users visited the fitness centres 40% more often than those without the app. This is significant because people who fail to form a habit of physical exercise tend to drop lifestyle goals.

The 2 approaches compared

Healthier You is unlikely to have more than a modest impact on the UK’s diabetes burden because the format it has adopted is like filling a swimming pool with a teaspoon. It would take over 100 years to recruit and counsel the 11m people with pre-diabetes, especially while the prevalence levels of pre-diabetes and T2DM in the UK are increasing. Successfully changing the diets and lifestyles of large numbers of people requires an understanding of 21^st century technologies. Ubiquitous healthcare technologies such as smartphone apps and wearable’s that support lifestyles abound, and have leveraged people's enhanced awareness of themselves and their health. Hence peoples’ large and rapidly growing demands for such devices to track their weight, blood pressure, daily exercise, diet etc. From apps to wearables, healthcare technology lets people feel in control of their health, while potentially providing health professionals with more patient data than ever before.

The IoT and consumers

There are more than 165,000 healthcare apps currently on the market, there is a rapid growth in wearables, and smartphone penetration in the US and UK has surpassed 80% and 75% respectively. According to a 2017 US survey by Anthem Blue Cross, 70m people in the US use wearable health monitoring devices, 52% of smartphone users gather health information using mobile apps, and 93% of doctors believe mobile apps can improve health. 86% of doctors say wearables increase patient engagement with their own health, and 88% of doctors want patients to monitor their health. 51% of doctors use electronic access to clinical information from other doctors, and 91% of hospitals in the US have moved to electronic patient records (EPR).

Notwithstanding, these apps and wearables are rarely configured to aggregate, export and share the data they collect in order to improve outcomes and lower costs. This reduces their utility and value. However, the large and rapid growth of this market on the back of the social media revolution, and the impact it is having on shaping the attitudes and expectations of millions of consumers of healthcare, positions it well as a potential driver of significant change.

A “minuscule fraction” of what is ultimately possible

According to Roger Kornberg, Professor of Structural Biology at Stanford University, the current capabilities of smart sensors like those used in Apple’sGymKit and Equinox’s Chatbox, “is only a minuscule fraction of what is ultimately possible . . . A sensor attached to a smartphone will enable it to answer any question that we may have about ourselves, and our environment,” says Kornberg. Smart sensors can provide you with a doctor in your pocket, which can be connected to a plethora of other devices that could collect, store, analyze and feedback terabytes of medical information in real time. Kornberg, who won the 2006 Nobel Prize for Chemistry, is excited about the disruptive effect, which smart sensors are having on traditional healthcare systems. This is because they can be connected to almost any medical device and human organ to, “monitor specimens . . . record in real time the health status of individuals, . . . transmitelectronic signals wirelessly, . . . (and) provide responses to any treatment,” says Kornberg.

Kornberg is engaged in developing sensors with the ability to detect and measure biological signals and data from humans, which can be wirelessly linked to smartphones to transmit the information for analysis, storage and further communication. Kornberg is convinced that, in the near term, we will be able to create a simple and affordable networked device that will, “detectan impending heart attack, in a precise and quantitative manner, before any symptoms”.

Potential of sensor technology

The excitement in the development of biosensors

Drivers of the IoT and market trends

Partly driving the IoT in healthcare and other industries are the: (i) general availability of affordable broadband Internet, (ii) almost ubiquitous smartphone penetration, (iii) increases in computer processing power, (iv) enhanced networking capabilities, (v) miniaturization, especially of computer chips and cameras, (vi) the digitalization of data, (vii) growth of big data repositories, and (viii) advances in AI and data mining.

Market trends suggest substantial growth in the total number of networked smart devices in use. By 2020, when the world’s population is expected to reach 7.6bn, it is projected that there will be between 19 and 50bn IoT-connected devices worldwide, more than 8bn broadband access points, more than 4m IoT jobs, and the number of installed IoT technologies will exceed that of personal computers by a factor of 10.

Crisis in primary care is a significant driver of change

In addition to these technological drivers, the simultaneous population aging and the shrinking pool of doctors also drives the IoT in healthcare. Increasing numbers of older people presenting with complex comorbidities significantly increases the large and rapidly growing demands on an over-stretched, shrinking population of doctors. This results in a crisis of care.

A 2015 Report from the Association of American Medical Colleges (AAMC) suggests that there is an 11 to 17% growth in total healthcare demand, of which a growing and aging population is a significant component. Further, the Report suggests that the US could lose 100,000 doctors by 2025, and that primary care physicians will account for 33% of that shortage.

There is a similar crisis in the UK, where trainee GPs are dwindling, young GPs are moving abroad, and experienced GPs are retiring early. According to data from the UK’s General Medical Council (GMC), between 2008 and 2014 an average of nearly 3,000 certificates were issued annually to enable British doctors to work abroad. Currently, there are hundreds of vacancies for GP trainees. Findings from a 2015 British Medical Association (BMA) poll of over 15,000 GPs, found that 34% of respondents plan to retire by 2020 because of high stress levels, unmanageable workloads, and too little time with patients.

Interestingly, Brexit is expected to compound the crisis of care in the UK. According to a 2017 General Medical Council survey of more than 2,000 doctors from the EU working in the UK, 60% said they were considering leaving the UK, and, of those, 91% said the UK’s decision to leave the EU was a factor in their considerations.

Changing healthcare ecosystems

These trends help healthcare payers to employ IoT strategies in an attempt to replace traditional healthcare systems, which act when illnesses occur and report services rendered, with value-based healthcare systems focused on outcomes. US payers are leading this transformation. Some payers in the US have employed IoT strategies to convert a number of devices used in various therapeutic pathways into smart devices that collect, aggregate and process terabytes of healthcare data gathered from thousands of healthcare providers, and electronic patient records (EPRs) describing millions of treatments doctors have prescribed to people presenting similar symptoms and disease states. Cognitive computing systems analyse these data and instantaneously identify patterns that doctors cannot. Such systems, although proprietary, are positioned to help reduce the ongoing challenges of inaccurate, late, and delayed diagnoses, which each year cost the US economy some US$750bn and lead to between 40,000 and 80,000 patient deaths.

IBM Watson

IBM’s supercomputer, Watson is a well-known proprietary system that uses IoT strategies that include a network of smart sensors and databases to assist doctors in various aspects of diagnoses and treatment plans tailored to patients’ individual symptoms, genetics, and medical histories. Watson draws from 600,000 medical evidence reports, 1.5m EPRs, millions of clinical trials, and 2m pages of text from medical journals. A variant, IBM Watson for Oncology, has been designed specifically to help oncologists, and is currently in use at the Memorial Sloan-Kettering Cancer Center in New York. Also, it is being used in India where there is a shortage of oncologists. The Manipal Hospital Group, India’s third largest healthcare group, which manages about 5,000 beds, and provides comprehensive care to around 2m patients every year, is using Watson for Oncology to support diagnosis and treatment for more than 200,000 cancer patients each year across 16 of its hospitals.

In 2016 IBM, made a US$3bn investment designed to increase the alignment of its Watson super cognitive computing with the IoT, and allocated more than US$200m to its global Watson IoT headquarters in Munich. IBM will have over 1,000 Munich-based researchers, engineers, developers and business experts working closely with specific industries, including healthcare, to draw insights from billions of sensors embedded in medical devices, hospital beds, health clinics, wearables and apps in endeavors to develop IoT healthcare solutions.

Babylon

Using a similar IoT network of smart sensors and databases, Babylon, a UK-based subscription health service start-up, has launched a digital healthcare AI-based app, which offers patients video and text-based consultations with doctors, and is designed to improve medical diagnoses and treatments. Early in 2017, NHS England started a 6-month study to test the app’s efficacy by making it available to 1.2m London residents. The Babylon app is expected to be able to analyse, “hundreds of millions of combinations of symptoms” in real time, while taking into account individualized information of a patient’s genetics, environment, behavior, and biology. Current regulations do not allow the Babylon app to make formal diagnoses, so it is employed to assist doctors by recommending diagnoses and treatment options. Notwithstanding, Ali Parsa, Babylon’s founder and CEO says, "Our scientists have little doubt that our AI will soon diagnose and predict personal health better than doctors”.

Market forecasts

Market studies stress the vast and growing economic impact of the IoT on healthcare. Business Insider Intelligence (BII) suggested that the IoT has created nearly US$100bn additional revenue in medical devices alone. It forecasts that cost savings and productivity gains generated through the IoT and subsequent changes will create between US$1.1 and US$2.5trillion in value in the healthcare sector by 2025. In 2016, Grand View Research Inc. projected that the global IoT healthcare market will reach nearly US$410bn by 2022. A 2013 Report from the McKinsey Global Institute on Disruptive Technologies, suggests that the potential total economic impact of IoT will be between US$3 and US$6trillion per year by 2025, the largest of which will be felt in healthcare and manufacturing sectors. Although forecasts differ, there is general agreement that, over the next decade, the IoT is projected to provide substantial economic and healthcare benefits in the way of cost savings, improved outcomes, and efficiency improvements.

IoT and MedTech companies

We have briefly described the impact of the IoT on patients, healthcare payers and providers. But what about MedTech companies? They have the capabilities and knowhow to develop and integrate the IoT into their next generation devices. However, MedTech innovations tend to be small improvements to existing product offerings. Data, accumulated from numerous smart medical devices, are enhanced in value once they are merged, aggregated, analyzed and communicated. And herein lies the challenge of data security. Arguably the greater the connectivity between medical devices, the greater the security threat. In 2013 the FDA issued a safety communication regarding cyber security for medical devices and health providers, and recommended that MedTech companies determine appropriate safeguards to reduce the risk of device failure due to cyber-attacks. The cautious modus vivendi of most MedTech companies suggests that, in the near term, a significant proportion will not develop IoT strategies, and this creates a gap in the market.

The IoT and new and rising healthcare players

Taking advantage of this market gap is a relatively small group of data-orientated companies, which have started to employ IoT technologies to gain access to healthcare markets by developing specific product offerings, increasing collaborative R&D, and acquiring new data oriented start-ups. For instance, in addition to IBM and Apple mentioned above, Amazon is expected to enter the global pharmaceutical market, which is anticipated to reach over US$1 trillion by 2022. Microsoft has used IoT strategies to build its Microsoft Azure cloud platform to facilitate cloud-based delivery of multiple healthcare services. Google Genomics is using IoT strategies to assist the life science community organise the world’s genomic data and make it accessible by applying the same technologies that power Google Search to securely store petabytes of genomic information, which can be analysed, and shared by life science researchers throughout the world.

Takeaways

The powerful social, demographic, technological and economic drivers of healthcare change over the next decade suggest an increasing influence of IoT technologies in a sector not known for radical or innovative change. Research suggests that hundreds of millions of networked medical devices will proliferate globally within the next decade. The potential healthcare benefits to be derived from these are expected to be significant, especially through enhancing preventative and outcome-oriented healthcare while reducing costs. This has to be achieved in a highly regulated environment where concerns of data security are paramount. To reap the potential benefits of the IoT in healthcare, policymakers will have to reconcile the need for IoT regulation with the significant projected benefits of the IoT. Smart technologies require smart management and smart regulation.