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2 years, 7 months ago

A prescription for an AI inspired MedTech industry

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AI
Artificial Intelligence
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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.
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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5 years, 5 months ago

WeDoctor’s impact on global healthcare

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Medical Technology
Tags:
affordable quality healthcare
AI
Artificial Intelligence
big data
commentary
global healthcare systems
smart devices
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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6 years, 10 months ago

The IoT and healthcare

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big data
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diagnostic strategies
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Internet of Things
IoT
preventative strategies
smartphone technology

  • 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 21st 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.
 
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8 years ago

Can AI reduce medical misdiagnosis?

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misdiagnosis
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  • Misdiagnosis means unnecessary suffering and the loss of life
  • 15% of all medical cases are misdiagnosed
  • 44% of some types of cancers are misdiagnosed
  • Misdiagnosis results from the way doctors are trained

Can AI reduce medical misdiagnosis?
 
Inaccurate or delayed medical diagnosis is more widespread than often thought, and results in a staggering toll of harm and patients’ deaths.
 
Unnecessary suffering
Each year, in the US an estimated five per cent of all medical cases are misdiagnosed. ‘Not bad’, some might say given the millions of Americans who visit their doctors’ each year presenting thousands of different disease states each with multiple symptoms. But five per cent translates to 12 million annual misdiagnoses in the US alone, which is, “the tip of the iceberg” according to Professor Graham Neale, an expert in misdiagnosis from the Centre for Patient Safety and Service Quality at Imperial College London.
 
A 2012 study reported in The American Journal of Medicine suggests that 15% of all medical cases in developed economies are misdiagnosed. Professor Neale suggests that 15% of all UK cases are also misdiagnosed. The Mayo Clinic Proceedings suggest that misdiagnosis could be as high as 26%, and according to The Journal of Clinical Oncology, a staggering 44% of some types of cancers are misdiagnosed.
 
Misdiagnosis means unnecessary suffering, the loss of life, and unnecessary costs. For example, 33% of the $3trillion spent each year on healthcare in the US is considered “wasted” because of medical misdiagnoses. And data released in 2015 by NHS England’s Litigation Authority in response to a Freedom of Information request show compensation paid to people misdiagnosed rose from £56 million in 2009-10 to more than £194 million in 2013-14.

According to Sebastian Lucas, former Professor of Clinical Histopathology at King’s College London, the most common misdiagnosis found through post-mortem examinations are the over diagnosis of cardiac disease, the under diagnosis of pulmonary-embolism, the over and under diagnosis of cancer, and the under diagnosis of significant infections.
 

What are the most common misdiagnosis found through autopsy? By Sebastian Lucas
 

Medical misdiagnosis occurs when either a condition is undiagnosed, or where an incorrect diagnosis is made. An example of the former is when a patient with a health problem has visited their doctor over a period, and the doctor fails to diagnose the illness.  An example of the latter is when, say, a fracture is diagnosed as a sprain.

 

Why misdiagnosis occurs
Reasons given for misdiagnosis include the fragmented nature of healthcare systems, and the over burdened, demoralised and scarce supply of primary care doctors. See, Curing the Problems of General Practice. In 2008 Eta Berner and Mark Graber published a paper in the American Journal of Medicine entitled, ‘Diagnostic Error: Is Overconfidence the Problem?’ which suggests that both intrinsic and systemically reinforced factors lead doctors to be over confident in their ability to diagnose, and once a diagnosis is made and a treatment pathway started, a momentum occurs, which is difficult to change.
 
Doctors trained to take short cuts
At the root of misdiagnosis is the way that doctors are trained, says Jerome Groopman, Professor of Medicine at the Harvard Medical School, and Chief of Experimental Medicine at Beth Israel Deaconess Medical Center.
 
Groopman’s thesis is predicated on the concept of the availability heuristic developed by Nobel Laureate Daniel Kahneman, notable for his work on the psychology of judgment and decision-making. In his book How Doctors Think, Groopman suggests that doctors are trained to recall similar recent cases when making a diagnosis. For example, common infections picked up by children at school often affect entire communities. Once a doctor has seen, say, nine such cases, the information about them is immediately available in his subconscious, and creates a tendency for the tenth patient presenting similar symptoms to be diagnosed the same although the actual illness might be different.
 
Such mental shortcuts are indispensible in a medical setting. In A&E, for example, doctors are encouraged to use mental shortcuts to help them make rapid decisions often on incomplete information; failure to do so could mean the difference between life and death.
 
Will misdiagnosis increase?
Structural reasons suggest that misdiagnosis will not be reduced in the near term. According to the Royal College of General Practitioners the shortage of doctors in the UK is the worst it has been for 40 years. Established GPs are retiring early, and a significant proportion of newly qualified GPs are moving abroad where pay and working conditions are better. One hundred primary care practices, serving 700,000 patients across Britain, are facing closure, and the number of doctor-patient consultations is estimated to rise from 338 million in 2013 to 441 million by 2017.

Similarly in the US, the Association of American Medical Colleges predicts increasing shortages of doctors: 130,600 by 2025. One reason for the shortage is the aging of both doctors and their patients. According to a 2012 Physicians Foundation survey, nearly half of the 830,000 doctors in the US are over 50, and approaching retirement.

Thus, fewer doctors in both the UK and US face having to diagnose an increasing number of aging patients presenting complex conditions, at a time when the volume of medical data are doubling every 73 days. Under such conditions it seems reasonable to assume that the incidence of misdiagnosis will not decrease.

Increased role for cognitive computers in medicine
Will the increased pressure on doctors to diagnose more accurately be helped by artificial intelligence (AI)? Although there are some challenges for AI in a medical setting, it is well positioned to play an increased role in diagnosis. This is confirmed by Google’s DeepMind AlphaGo computer’s landmark defeat of Lee Sedol, a 33-year-old grandmaster of the ancient Asian game GO in March 2016. Let us explain.
 
AI: the complex algorithms that analyze and transform electronic medical data, into clinically relevant medical opinions for health professionals has developed significantly as the demand for healthcare increased, healthcare costs escalated, and the supply of doctors decreased.
 

What is the next "big thing" in healthcare? By Devi Shetty
 
The relationship between the game GO and medical diagnosis
For some time, cognitive computers have been able to defeat the world’s best human players of games such as draughts and backgammon by enumerating every possible move, and drawing up rules for how to guarantee that a computer will be able to play to at least a draw. Although more complex, chess computers rely on a modified version of the same tactic. In 1997 for example, when IBM’s Deep Blue computer defeated former world chess champion Garry Kasparov, it could evaluate 200 million possible moves in a second.
 
But GO is different: its simplicity belies its astonishing complexity. There are more legal board states for a game of GO than there are atoms in the universe, and just like in medical diagnoses, reaction and intuition are important. These intangible aspects of the game GO, and diagnosis, make them resistant to the tactic by which games in the past have been “solved” by computers. Experts predicted that it would take another 10 years before a computer program would stand a chance even against a weak GO player. This is why a computer’s defeat of Lee Sedol, signaled a landmark moment for AI, and has implications for medical diagnosis.
 

GOis played by two people on a 19-by-19 grid-board, with 361 black and white stones, 181 black and 180 white. Each player takes turns placing their stones in an attempt to surround and capture their opponent’s pieces. The player who controls more territory is the winner. The first move of a game of chess offers 28 possibilities; the first move of a game of GO can involve placing the stone in one of 361 positions. An average game of chess lasts around 80 turns, while on average GO game lasts for some 150 turns, which leads to a staggering number of possibilities.


Cognitive computing and diagnosis
Cognitive computing systems that understand, reason, and learn, also are able to see health data that were previously hidden, and do more than we ever thought possible. Doctors have access to such computers, which provide them with collective knowledge gathered from thousands of healthcare providers, millions of patients’ records, and millions of treatments other doctors have prescribed to people presenting similar symptoms and disease states. Such computers are capable of analyzing in seconds these data and identifying patterns that humans cannot.

Further, unlike doctors, computers work 24-7, 365 days a year, they never get tired or demoralised, and they never leave. Also, computers are faster and more thorough than doctors, and can analyse vast amounts of patient data, identify trends in seconds and consistently make more accurate diagnoses. One example is IBM’s Watson, a computer, already being used in medicine, which can attain high levels of cognitive behaviour. Watson uses natural language processing to analyse structured and unstructured data common in clinical notes and reports, and can read 40 million medical documents in 15 seconds, understand complex questions, and identify and present evidence based solutions and treatment options. In the US similar computer programs have stopped making clinical recommendations based on the most popular therapies prescribed by its users, to providing doctors with clinical recommendations based on patient outcomes.
 
Challenges for AI in medical diagnosis
Despite the fact that AI systems are getting smarter there are still significant challenges associated with the compatibility of computer systems, the integrity of medical data; and data security and access. Further, as AI systems get smarter so the line between computers recommending and deciding becomes blurred. Healthcare providers are wary not to allow their AI systems to make clinical decisions because this would mean that they would be viewed as “medical devices”, and require FDA approval, which can be a costly and lengthy process to obtain.
 
Doctor’s resistance to AI systems
A doctor’s raison d'être has been to diagnose and treat illnesses, which ordinary people cannot do because it requires expertise, intuition and interpersonal skills. Some doctors argue that computers will never be able to provide such skills. But medical knowledge, which previously resided in the minds of the few doctors, has become readily available to everyone over the Internet, and doctors have changed from being the sole processors of that knowledge, to being the interpreters of such knowledge; in this scenario AI has an important role.

Takeaway
Professor Stephen Hawking and other leading scientists have warned of the dangers of AI becoming “too clever”. There are also concerns about data security and privacy, and some doctor’s fear cognitive computers could diminish their role. However, the defeat of Lee Sedol by AlphaGo has demonstrated that computers can attain high levels of intelligent behavior, and this has significant implications for medicine in general and diagnoses in particular.
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