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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.
 

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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.
 

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. 
 

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.

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.
  

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.
 

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
 

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

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.
 

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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.

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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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