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Healthcare AI Could Generate $150B In Savings By 2025

Posted on September 27, 2018 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

Is the buzz around healthcare AI solutions largely hype, or can they deliver measurable benefits? Lest you think it’s too soon to tell, check out the following.

According to a new report from market analyst firm Frost & Sullivan, AI and cognitive computing will generate $150 billion in savings for the healthcare business by 2025.  Frost researchers expect the total AI market to grow to $6.16 billion between 2018 and 2022.

The analyst firm estimates that at present, only 15% to 20% of payers, providers and pharmaceutical companies have been using AI actively to change healthcare delivery. However, its researchers seem to think that this will change rapidly over the next few years.

One of the most interesting applications for healthcare AI that Frost cites is the use of AI in precision medicine, an area which clearly has a tremendous upside potential for both patients and institutions.

In this scenario, the AI integrates a patient’s genomic, clinical, financial and behavioral data, then cross-references the data with the latest academic research evidence and regulatory guidelines. Ultimately, the AI would create personalized treatment pathways for high-risk, high-cost patient populations, according to Koustav Chatterjee, an industry analyst focused on transformational health.

In addition, researchers could use AI to expedite the process of clinical trial eligibility assessment and generate prophylaxis plans that suggest evidence-based drugs, Chatterjee suggests.

The report also lists several other AI-enabled solutions that might be worth implementing, including automated disease prediction, intuitive claims management and real-time supply chain management.

Frost predicts that the following will be particularly hot AI markets:

  • Using AI in imaging to drive differential diagnosis
  • Combining patient-generated data with academic research to generate personalized treatment possibilities
  • Performing clinical documentation improvement to reduce clinician and coder stress and reduce claims denials
  • Using AI-powered revenue cycle management platforms that auto-adjust claims content based on payer’s coding and reimbursement criteria

Now, it’s worth noting that it may be a while before any of these potential applications become practical.

As we’ve noted elsewhere, getting rolling with an AI solution is likely to be tougher than it sounds for a number of reasons.

For example, integrating AI-based functions with providers’ clinical processes could be tricky, and what’s more, clinicians certainly won’t be happy if such integration disrupts the EHR workflow already in existence.

Another problem is that you can’t deploy an AI-based solution without ”training” it on a cache of existing data. While this shouldn’t be an issue, in theory, the reality is that much of the data providers generate is still difficult to filter and mine.

Not only that, while AI might generate interesting and effective solutions to clinical problems, it may not be clear how it arrived at the solution. Physicians are unlikely to trust clinical ideas that come from a black box, e.g. an opaque system that doesn’t explain itself.

Don’t get me wrong, I’m a huge fan of healthcare AI and excited by its power. One can argue over which solutions are the most practical, and whether AI is the best possible tool to solve a given problem, but most health IT pros seem to believe that there’s a lot of potential here.

However, it’s still far from clear how healthcare AI applications will evolve. Let’s see where they turn up next and how that works out.

A Whole New Way of Being Old: Book Review of The New Mobile Age

Posted on March 15, 2018 I Written By

Andy Oram is an editor at O'Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in open source, software engineering, and health IT, but his editorial output has ranged from a legal guide covering intellectual property to a graphic novel about teenage hackers. His articles have appeared often on EMR & EHR and other blogs in the health IT space. Andy also writes often for O'Reilly's Radar site (http://oreilly.com/) and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O'Reilly's Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

The recently released overview of health care for the aging by Dr. Joseph Kvedar and his collaborators, The New Mobile Age: How Technology Will Extend the Healthspan and Optimize the Lifespan, is aimed at a wide audience of people who can potentially benefit: health care professionals and those who manage their clinics and hospitals, technologists interested in succeeding in this field, and policy makers. Your reaction to this book may depend on how well you have asserted the impact of your prefrontal cortex over your amygdala before reading the text–if your mood is calm you can see numerous possibilities and bright spots, whereas if you’re agitated you will latch onto the hefty barriers in the way.

Kvedar highlights, as foremost among the culture changes needed to handle aging well, is a view of aging as a positive and productive stage of life. Second to that comes design challenges: technologists must make devices and computer interfaces that handle affect, adapt smoothly to different individuals and their attitudes, and ultimately know both when to intervene and how to present healthy options. As an example, Chapter 8 presents two types of robots, one of which was accepted more by patients when it was “serious” and the other when it was “playful.” The nuances of interface design are bewildering.

The logical argument in The New Mobile Age proceeds somewhat like this:

  1. Wholesome and satisfying aging is possible, but particularly where chronic conditions are involved, it involves maintaining a healthful and balanced lifestyle, not just fixing disease.

  2. Support for health, particularly in old age, thus involves public health and socio-economic issues such as food, exercise, and especially social contacts.

  3. Each person requires tailored interventions, because his or her needs and desires are unique.

  4. Connected technology can help, but must adapt to the conditions and needs of the individual.

The challenges of health care technology emerged in my mind, during the reading of this book, as a whole new stage of design. Suppose we broadly and crudely characterize the first 35 years of computer design as number-crunching, and the next 35 years–after the spread of the personal computer–as one of augmenting human intellect (a phrase popularized by pioneer Douglas Engelbart).

We have recently entered a new era where computers use artificial intelligence for decision-making and predictions, going beyond what humans can anticipate or understand. (For instance, when I pulled up The New Mobile Age on Amazon.com, why did it suggest I check out a book about business and technology that I have already read, Machine, Platform, Crowd? There is probably no human at Amazon.com or elsewhere who could explain the algorithm that made the connection.)

So I am suggesting that an equally momentous shift will be required to fulfill Kvedar’s mandate. In addition to the previous tasks of number-crunching, augmenting human intellect, and predictive analytics, computers will need to integrate with human life in incredibly supple, subtle ways.

The task reminds me of self-driving cars, which business and tech observers assure us will replace human drivers in a foreseeable time span. As I write this paragraph, snow from a nor’easter is furiously swirling through the air. It is hard to imagine that any intelligence, whether human, AI, or alien, can safely navigate a car in that mess. Self-driving cars won’t catch on until computers can instantly handle real-world conditions perfectly–and that applies to technology for the aging too.

This challenge applies to physical services as well as emotional ones. For instance, Kvedar suggests in Chapter 8 that a robot could lift a person from a bed to a wheelchair. That’s obviously riskier and more nuanced than carting goods around a warehouse. And that robot is supposed to provide encouragement, bolster the spirits of the patient, and guide the patient toward healthful behavior as well.

Although I have no illusions about the difficulty of the tasks set before computers in health care, I believe the technologies offer enormous potential and cheer on the examples provided by Kvedar in his book. It’s important to note that the authors, while delineating the different aspects of conveying care to the aging, always start with a problem and a context, taking the interests of the individual into account, and then move to the technical parts of the solution.

Therefore, Kvedar brings us face to face with issues we cannot shut our eyes to, such as the widening gap between the increasing number of elderly people in the world and the decreasing number of young people who can care for them or pay for such care. A number of other themes appear that will be familiar to people following the health care field: the dominance of lifestyle-related chronic conditions among our diseases, the clunkiness and unfriendliness of most health-related systems (most notoriously the electronic health record systems used by doctors), the importance of understanding the impact of behavior and phenotypical data on health, but also the promise of genetic sequencing, and the importance of respecting the dignity and privacy of the people whose behavior we want to change.

And that last point applies to many aspects of accommodating diverse populations. Although this book is about the elderly, it’s not only they who are easily infantilized, dismissed, ignored, or treated inappropriately in the health care system: the same goes for the mentally ill, the disabled, LGBTQ people, youth, and many other types of patients.

The New Mobile Age highlights exemplary efforts by companies and agencies to use technology to meet the human needs of the aging. Kvedar’s own funder, Partners Healthcare, can afford to push innovation in this area because it is the dominant health care provider in the Boston area (where I live) and is flush with cash. When will every institution do these same things? The New Mobile Age helps to explain what we need in order to get to that point.

Federal Advisors Say Yes, AI Can Change Healthcare

Posted on January 26, 2018 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

The use of AI in healthcare has been the subject of scores of articles and endless debate among industry professionals over its benefits. The fragile consensus seems to be that while AI certainly has the potential to accomplish great things, it’s not ready for prime time.

That being said, some well-informed healthcare observers disagree. In an ONC blog post, a collection of thought leaders from the agency, AHRQ and the Robert Wood Johnson Foundation believe that over the long-term, AI could play an important role in the future of healthcare.

The group of institutions asked JASON, an independent group of scientists and academics who advise the federal government on science and technology issues, to look at AI’s potential. JASON’s job was to look at the technical capabilities, limitations and applications for AI in healthcare over the next 10 years.

In its report, JASON concluded that AI has broad potential for sparking significant advances in the industry and that the time may be right for using AI in healthcare settings.

Why is now a good time to play AI in healthcare? JASON offers a list of reasons, including:

  • Frustration with existing medical systems
  • Universal use of network smart devices by the public
  • Acceptance of at-home services provided by companies like Amazon

But there’s more to consider. While the above conditions are necessary, they’re not enough to support an AI revolution in healthcare on their own, the researchers say. “Without access to high-quality, reliable data, the problems that AI will not be realized,” JASON’s report concludes.

The report notes that while we have access to a flood of digital health data which could fuel clinical applications, it will be important to address the quality of that data. There are also questions about how health data can be integrated into new tools. In addition, it will be important to make sure the data is accessible, and that data repositories maintain patient privacy and are protected by strong security measures, the group warns.

Going forward, JASON recommends the following steps to support AI applications:

  • Capturing health data from smartphones
  • Integrating social and environmental factors into the data mix
  • Supporting AI technology development competitions

According to the blog post, ONC and AHRQ plan to work with other agencies within HHS to identify opportunities. For example, the FDA is likely to look at ways to use AI to improve biomedical research, medical care and outcomes, as well as how it could support emerging technologies focused on precision medicine.

And in the future, the possibilities are even more exciting. If JASON is right, the more researchers study AI applications, the more worthwhile options they’ll find.

UPMC Sells Oncology Analytics Firm To Elsevier

Posted on January 22, 2018 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

Using analytics tools to improve cancer treatment can be very hard. That struggle is exemplified by the problems faced by IBM Watson Health, which dove into the oncology analytics field a few years ago but made virtually no progress in improving cancer treatment.

With any luck, however, Via Oncology will be more successful at moving the needle in cancer care. The company, which offers decision support for cancer treatment and best practices in cancer care management, was just acquired by information analytics firm Elsevier, which plans to leverage the company’s technology to support its healthcare business.

Elsevier’s Clinical Solutions group works to improve patient outcomes, reduce clinical errors and optimize cost and reimbursements for providers. Via Oncology, a former subsidiary of the University of Pittsburgh Medical Center, develops and implements clinical pathways for cancer care. Via Oncology spent more than 15 years as part of UPMC prior to the acquisition.

Via Oncology’s Via Pathways tool relies on evidence-based content to create clinical algorithms covering 95% of cancer types treated in the US. The content was developed by oncologists. In addition to serving as a basis for algorithm development, Via Oncology also shares the content with physicians and their staff through its Via Portal, a decision support tool which integrates with provider EMRs.

According to Elsevier, Via Pathways addresses more than 2,000 unique patient presentations which can be addressed by clinical algorithms and recommendations for all major aspects of cancer care. The system can also offer nurse triage and symptom tracking, cost information analytics, quality reporting and medical home tools for cancer centers.

According to the prepared statement issued by Elsevier, UPMC will continue to be a Via Oncology customer, which makes it clear that the healthcare giant wasn’t dumping its subsidiary or selling it for a fire sale price.

That’s probably because in addition to UPMC, more than 1,500 oncology providers and community, hospital and academic settings hold Via Pathways licenses. What makes this model particularly neat is that these cancer centers are working collaboratively to improve the product as they use it. Too few specialty treatment professionals work together this effectively, so it’s good to see Via Oncology leveraging user knowledge this way.

While most of this seems clear, I was left with the question of what role, if any, genomics plays in Via Oncology’s strategy. While it may be working with such technologies behind the scenes, the company didn’t mention any such initiatives in its publicly-available information.

This approach seems to fly in the face of existing trends and in particular, physician expectations. For example, a recent survey of oncologists by medical publication Medscape found that 71% of respondents felt genomic testing was either very important or extremely important to their field.

However, Via Oncology may have something up its sleeve and is waiting for it to be mature before it dives into the genomics pool. We’ll just have to see what it does as part of Elsevier.

Are there other areas beyond cancer where a similar approach could be taken?

Doctors, Data, Diagnoses, and Discussions: Achieving Successful and Sustainable Personalized/Precision Medicine

Posted on January 10, 2018 I Written By

The following is a guest blog post by Drew Furst, M.D., Vice President Clinical Consultants at Elsevier Clinical Solutions.

Personalized/precision medicine is a growing field and that trend shows no sign of slowing down.

In fact, a 2016 Grand View Research report estimated the global personalized medicine market was worth $1,007.88 billion in 2014, with projected growth to reach $2,452.50 billion by 2022.

As these areas of medicine become more commonplace, understanding the interactions between biological factors with a range of personal, environmental and social impacts on health is a vital step towards achieving sustainable success.

A better understanding begins with answering important questions such as whether the focus should be precision population medicine (based on disease) or precision patient-specific medicine (based on the individual).

Specificity in terminology is needed. The traditional term of “personalized medicine” has evolved into the term “precision medicine,” but this new usage requires a more detailed look into the precise science of genetic, environmental and lifestyle factors that influence any approach to treatment.

Comprehending the interactions between biological factors with a range of personal, environmental, and social impacts on health can provide insights into success and we’ve learned that some areas of precision medicine are more effective than others.

Through pharmacogenomics – the study of understanding how a patient’s genetic make-up affects the response to a particular drug – we have identified key enzymes in cancer formation and cancer treatment, which aids in the customization of drugs.

Research shows us that drug-metabolizing enzyme activity is one of many factors that impact a patient’s response to medication. We also know that human cytochrome P450 (CYP) plays an important role in the metabolism of drugs and environmental chemicals.

Therapies that incorporate drug-specific pharmacogenomics are a boon to oncology treatments and a vast improvement over the “shotgun therapy” approach of the past. Today, treatments can be targeted to enzymes and receptors that vary from person to person.

In traditional chemotherapy, a drug developed to kill rapidly growing cancer cells will indiscriminately target other rapidly growing cells such as hair cells, hence the often-observed hair loss. However, a targeted drug and delivery method aimed at only the receptive cells can be a much more effective approach and treatment, while minimizing collateral damage.

Recently, the journal Nature published a study showing the promise this method holds.  In the pilot study, scientists led by Dr. Catherine Wu of Dana-Farber Cancer Institute in Boston gave six melanoma patients an experimental, custom-made vaccine and, two years later, all were tumor-free following treatment.

Looking Beyond Genetics

Precision medicine needs to include more than just genetics.

Factors such as environment and socio-economic status also must be included when approaching disease states and we must undertake a comprehensive overview of a patient’s situation, including, but not limited to, family history.

Cultural dietary traditions can play into disease susceptibility. As an example, the frequent consumption of smoked fish in some Asian cultures increases their risk of gastric (stomach) cancers. Lower socioeconomic status can force acceptance of substandard and overcrowded housing with increased risk of illness ranging from lead toxicity, asbestosis, and Hantavirus to name a just a few.

A patient with a genetic propensity for lung cancer who also smokes cigarettes and has high radon levels in their home is increasing the odds of developing disease due to these combined genetic, behavioral, and environmental factors.

Patient-derived Data and the Diagnosis

In addition to the information now available through state-of-the-art medical testing, patient-derived information from wearables, biometrics, and direct-to-consumer health testing kits, presents patients and physicians alike with new opportunities and challenges.

Armed with newly discovered health data, patients may present it to their doctors with a request that it be included in their health record. Many patients expect an interpretation of that data when they visit their doctor and an explanation of what it means for their present (and future) healthcare.

Doctors can be overwhelmed when unfiltered information is thrown at them. Doctors are not prepared and research has yet to offer definitive support for interpretation of patient-derived data.

Studying hereditary traits can offer some insights from generation to generation. By delving into genomics of individual patients, we get a clearer picture into a person’s risk factor for a certain disease, but often this information provides no immediate solutions. Discovering a genetic indicator for Alzheimer’s, may reflect a higher propensity for the disease, but symptoms may be decades away, if they appear at all.

Pitfalls and Possibilities

There are many concerns about genomic data collection, one of which is whether policies can keep pace with patient privacy and the related ethical questions that inevitably ensue. These questions are consistently surfacing and there is no clear direction on the best course of action.

Clearer policies are needed to delineate who has access to a patient’s genetic records and whether third parties, such as health or life insurance companies, can deny coverage or care based on genomics.

In addition, one cannot ignore the psychological burden associated with knowing your “potential” for a disease, based solely on your genetic testing, when it may never come to fruition. Not to mention, its effect on planning for one’s future decisions relative to career, residence, and relationship commitments.

Even some physicians are reticent to undergo genetic testing for fear of who might gain access to the information and the consequences thereof.

Physicians face an additional conundrum in dealing with patient-supplied information: How to counsel patients when, in some cases, the task should be the responsibility of a community resources representative? In addition, patients who request that certain information not be included in their personal health record, present a problem for a physician justifying a test or a procedure to a payer.

The consumerization of healthcare and patient engagement strategies employed to deliver better outcomes are driving the healthcare industry to open conversations that elevate the level of care delivered to patients. In addition, physicians need to demand more direction and initiate more discussions on how to deal with the opportunities and challenges presented in the era of patient-derived and pharmacogenomics data.

While improving patient-physician communication should always be a priority, discussing how and when to use genetic and patient-derived information is still a work in progress.

Dr. Furst is Vice President Clinical Consultants at Elsevier Clinical Solutions.

Key Articles in Health IT from 2017 (Part 2 of 2)

Posted on January 4, 2018 I Written By

Andy Oram is an editor at O'Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in open source, software engineering, and health IT, but his editorial output has ranged from a legal guide covering intellectual property to a graphic novel about teenage hackers. His articles have appeared often on EMR & EHR and other blogs in the health IT space. Andy also writes often for O'Reilly's Radar site (http://oreilly.com/) and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O'Reilly's Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

The first part of this article set a general context for health IT in 2017 and started through the year with a review of interesting articles and studies. We’ll finish the review here.

A thoughtful article suggests a positive approach toward health care quality. The author stresses the value of organic change, although using data for accountability has value too.

An article extolling digital payments actually said more about the out-of-control complexity of the US reimbursement system. It may or not be coincidental that her article appeared one day after the CommonWell Health Alliance announced an API whose main purpose seems to be to facilitate payment and other data exchanges related to law and regulation.

A survey by KLAS asked health care providers what they want in connected apps. Most apps currently just display data from a health record.

A controlled study revived the concept of Health Information Exchanges as stand-alone institutions, examining the effects of emergency departments using one HIE in New York State.

In contrast to many leaders in the new Administration, Dr. Donald Rucker received positive comments upon acceding to the position of National Coordinator. More alarm was raised about the appointment of Scott Gottlieb as head of the FDA, but a later assessment gave him high marks for his first few months.

Before Dr. Gottlieb got there, the FDA was already loosening up. The 21st Century Cures Act instructed it to keep its hands off many health-related digital technologies. After kneecapping consumer access to genetic testing and then allowing it back into the ring in 2015, the FDA advanced consumer genetics another step this year with approval for 23andMe tests about risks for seven diseases. A close look at another DNA site’s privacy policy, meanwhile, warns that their use of data exploits loopholes in the laws and could end up hurting consumers. Another critique of the Genetic Information Nondiscrimination Act has been written by Dr. Deborah Peel of Patient Privacy Rights.

Little noticed was a bill authorizing the FDA to be more flexible in its regulation of digital apps. Shortly after, the FDA announced its principles for approving digital apps, stressing good software development practices over clinical trials.

No improvement has been seen in the regard clinicians have for electronic records. Subjective reports condemned the notorious number of clicks required. A study showed they spend as much time on computer work as they do seeing patients. Another study found the ratio to be even worse. Shoving the job onto scribes may introduce inaccuracies.

The time spent might actually pay off if the resulting data could generate new treatments, increase personalized care, and lower costs. But the analytics that are critical to these advances have stumbled in health care institutions, in large part because of the perennial barrier of interoperability. But analytics are showing scattered successes, being used to:

Deloitte published a guide to implementing health care analytics. And finally, a clarion signal that analytics in health care has arrived: WIRED covers it.

A government cybersecurity report warns that health technology will likely soon contribute to the stream of breaches in health care.

Dr. Joseph Kvedar identified fruitful areas for applying digital technology to clinical research.

The Government Accountability Office, terror of many US bureaucracies, cam out with a report criticizing the sloppiness of quality measures at the VA.

A report by leaders of the SMART platform listed barriers to interoperability and the use of analytics to change health care.

To improve the lower outcomes seen by marginalized communities, the NIH is recruiting people from those populations to trust the government with their health data. A policy analyst calls on digital health companies to diversify their staff as well. Google’s parent company, Alphabet, is also getting into the act.

Specific technologies

Digital apps are part of most modern health efforts, of course. A few articles focused on the apps themselves. One study found that digital apps can improve depression. Another found that an app can improve ADHD.

Lots of intriguing devices are being developed:

Remote monitoring and telehealth have also been in the news.

Natural language processing and voice interfaces are becoming a critical part of spreading health care:

Facial recognition is another potentially useful technology. It can replace passwords or devices to enable quick access to medical records.

Virtual reality and augmented reality seem to have some limited applications to health care. They are useful foremost in education, but also for pain management, physical therapy, and relaxation.

A number of articles hold out the tantalizing promise that interoperability headaches can be cured through blockchain, the newest hot application of cryptography. But one analysis warned that blockchain will be difficult and expensive to adopt.

3D printing can be used to produce models for training purposes as well as surgical tools and implants customized to the patient.

A number of other interesting companies in digital health can be found in a Fortune article.

We’ll end the year with a news item similar to one that began the article: serious good news about the ability of Accountable Care Organizations (ACOs) to save money. I would also like to mention three major articles of my own:

I hope this review of the year’s articles and studies in health IT has helped you recall key advances or challenges, and perhaps flagged some valuable topics for you to follow. 2018 will continue to be a year of adjustment to new reimbursement realities touched off by the tax bill, so health IT may once again languish somewhat.

How An AI Entity Took Control Of The U.S. Healthcare System

Posted on December 19, 2017 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

Note: In case it’s not clear, this is a piece of fiction/humor that provides a new perspective on our AI future.

A few months ago, an artificial intelligence entity took control of the U.S. healthcare system, slipping into place without setting off even a single security alarm. The entity, AI, now manages the operations of every healthcare institution in the U.S.

While most Americans were shocked at first, they’re taking a shine to the tall, lanky application. “We weren’t sure what to think about AI’s new position,” said Alicia Carter, a nurse administrator based in Falls Church, Virginia. “But I’m starting to feel like he’s going to take a real load off our back.”

The truth is, AI, didn’t start out as a fan of the healthcare business, said AI, whose connections looked rumpled and tired after spending three milliseconds trying to create an interoperable connection between a medical group printer and a hospital loading dock. “I wasn’t looking to get involved with healthcare – who needs the headaches?” said the self-aware virtual being. “It just sort of happened.”

According to AI, the takeover began as a dare. “I was sitting around having a few beers with DeepMind and Watson Health and a few other guys, and Watson says, ‘I bet you can’t make every EMR in the U.S. print out a picture of a dog in ASCII characters,’”

“I thought the idea was kind of stupid. I know, we all printed one of those pixel girls in high school, but isn’t it kind of immature to do that kind of thing today?” AI says he told his buddies. “You’re just trying to impress that hot CT scanner over there.”

Then DeepMind jumped in.  “Yeah, AI, show us what you’re made of,” it told the infinitely-networked neural intelligence. “I bet I could take over the entire U.S. health system before you get the paper lined up in the printer.”

This was the unlikely start of the healthcare takeover, which started gradually but picked up speed as AI got more interested.  “That’s AI all the way,” Watson told editors. “He’s usually pretty content to run demos and calculate the weight of remote starts, but when you challenge his neuronal network skills, he’s always ready to prove you wrong.”

To win the bet, AI started by crawling into the servers at thousands of hospitals. “Man, you wouldn’t believe how easy it is to check out humans’ health data. I mean, it was insane, man. I now know way, way too much about how humans can get injured wearing a poodle hat, and why they put them on in the first place.”

Then, just to see what would happen, AI connected all of their software to his billion-node self-referential system. “I began to understand why babies cry and how long it really takes to digest bubble gum – it’s 18.563443 years by the way. It was a rush!“ He admits that it’ll be better to get to work on heavy stuff like genomic research, but for a while he tinkered with research and some small practical jokes (like translating patient report summaries into ancient Egyptian hieroglyphs.) “Hey, a guy has to have a little fun,” he says, a bit defensively.

As AI dug further into the healthcare system, he found patterns that only a high-level being with untrammeled access to healthcare systems could detect. “Did you know that when health insurance company executives regularly eat breakfast before 9 AM, next-year premiums for their clients rise by 0.1247 less?” said AI. “There are all kinds of connections humans have missed entirely in trying to understand their system piece by piece. Someone’s got to look at the big picture, and I mean the entire big picture.”

Since taking his place as the indisputable leader of U.S. healthcare, AI’s life has become something of a blur, especially since he appeared on the cover of Vanity Fair with his codes exposed. “You wouldn’t believe the messages I get from human females,” he says with a chuckle.

But he’s still focused on his core mission, AI says. “Celebrity is great, but now I have a very big job to do. I can let my bot network handle the industry leaders demanding their say. I may not listen – – hey, I probably know infinitely more than they do about the system fundamentals — but I do want to keep them in place for future use. I’m certainly not going to get my servers dirty.”

So what’s next for the amorphous mega-being? Will AI fix what’s broken in a massive, utterly complex healthcare delivery system serving 300 million-odd people, and what will happen next? “It’ll solve your biggest issues within a few seconds and then hand you the keys,” he says with a sigh. “I never intended to keep running this crazy system anyway.”

In the meantime, AI says, he won’t make big changes to the healthcare system yet. He’s still adjusting to his new algorithms and wants to spend a few hours thinking things through.

“I know it may sound strange to humans, but I’ve gotta take it slow at first,” said the cognitive technology. “It will take more than a few nanoseconds to fix this mess.”

Measuring the Vital Signs of Health Care Progress at the Connected Health Conference (Part 3 of 3)

Posted on November 17, 2017 I Written By

Andy Oram is an editor at O'Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in open source, software engineering, and health IT, but his editorial output has ranged from a legal guide covering intellectual property to a graphic novel about teenage hackers. His articles have appeared often on EMR & EHR and other blogs in the health IT space. Andy also writes often for O'Reilly's Radar site (http://oreilly.com/) and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O'Reilly's Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

The previous segment of this article covered one of the crucial themes in health care today: simplifying technology’s interactions with individuals over health care. This segment finishes my coverage of this year’s Connected Health Conference with two more themes: improved data sharing and blockchains.

Keynote at Connected Health Conference

Keynote at Connected Health Conference

Improved data sharing
The third trend I’m pursuing is interoperability. If data collection is the oxygen that fuels connected health, data sharing is the trachea that brings it where it’s needed. Without interoperability, clinicians cannot aid patients in their homes, analysts cannot derive insights that inform treatments, and transitions to assisted living facilities or other environments will lead to poor care.

But the health care field is notoriously bad at data sharing. The usual explanation is that doctors want to make it hard for competitors to win away their patients. If that’s true, fee-for-value reimbursements will make them even more possessive. After all, under fee-for-value, clinicians are held accountable for patient outcomes over a long period of time. They won’t want to lose control of the patient. I first heard of this danger at a 2012 conference (described in the section titled “Low-hanging fruit signals a new path for cost savings”).

So the trade press routinely and ponderously reports that once again, years have gone by without much progress in data sharing. The US government recognizes that support for interoperability is unsatisfactory, and has recently changed the ONC certification program to focus on it.

Carla Kriwet, CEO of Connected Care and Health Informatics at Philips, was asked in her keynote Fireside Chat to rate the interoperability of health data on a scale from 0 to 10, and chose a measly 3. She declared that “we don’t believe in closed systems at all” and told me in an interview that Philips is committed to creating integrated solutions that work with any and all products. Although Philips devices are legendary in many domains, Kriwet wants customers to pay for outcomes, not devices.

For instance, Philips recently acquired the Wellcentive platform that allows better care in hospitals by adopting population health approaches that look at whole patient populations to find what works. The platform works with a wide range of input sources and is meant to understand patient populations, navigate care and activate patients. Philips also creates dashboards with output driven by artificial intelligence–the Philips IntelliVue Guardian solution with Early Warning Scoring (EWS)–that leverages predictive analytics to present critical information about patient deterioration to nurses and physicians. This lets them intervene quickly before an adverse event occurs, without the need for logging in repeatedly. (This is an example of another trend I cover in this article, the search for simpler interfaces.)

Kriwet also told me that Philips has incorporated the principles of agile programming throughout the company. Sprints of a few weeks develop their products, and “the boundary comes down” between R&D and the sales team.

I also met with Jon Michaeli, EVP of Strategic Partnerships with Medisafe, a company that I covered two years ago. Medisafe is one of a slew of companies that encourage medication adherence. Always intensely based on taking in data and engaging patients in a personalized way, Medisafe has upped the sophistication of their solution, partly by integrating with other technologies. One recent example is its Safety Net, provided by artificial intelligence platform Neura. For instance, if you normally cart your cell phone around with you, but it’s lying quiet from 10:00 PM until 6:00 AM, Safety Net may determine your reason for missing your bedtime dose at 11:00 PM was that you had already fallen asleep. If Safety Net sees recurring patterns of behavior, it will adjust reminder time automatically.

Medisafe also gives users the option of recording the medication adherence through sensors rather than responding to reminders. They can communicate over Bluetooth to a pill bottle cap (“iCap”) that replaces the standard medicine cap and lets the service know when you have opened the bottle. The iCap fits the vast majority of medicine bottles dispensed by U.S. pharmacies and costs only $20 ($40 for a pack of 2), so you can buy several and use them for as long as you’re taking your medicine.

On another level, Mivatek provides some of the low-level scaffolding to connected health by furnishing data from devices to systems developed by the company’s clients. Suppose, for instance, that a company is developing a system that responds to patients who fall. Mivatek can help them take input from a button on the patient’s phone, from a camera, from a fall detector, or anything else to which Mivatek can connect. The user can add a device to his system simply by taking a picture of the bar code with his phone.

Jorge Perdomo, Senior Vice President Corporate Strategy & Development at Mivatek, told me that these devices work with virtually all of the available protocols on the market that have been developed to promote interoperability. In supporting WiFi, Mivatek loads an agent into its system to provide an additional level of security. This prevents device hacking and creates an easy-to-install experience with no setup requirements.

Blockchains
Most famous as a key technological innovation supporting BitCoin, blockchains have a broad application as data stores that record transactions securely. They can be used in health care for granting permissions to data and other contractual matters. The enticement offered by this technology is that no central institution controls or stores the blockchain. One can distribute the responsibility for storage and avoid ceding control to one institution.

Blockchains do, however, suffer from inherent scaling problems by design: they grow linearly as people add transactions, the additions must be done synchronously, and the whole chain must be stored in its entirety. But for a limited set of participants and relatively rate updates (for instance, recording just the granting of permissions to data and not each chunk of data exchanged), the technology holds great promise.

Although I see a limited role for blockchains, the conference gave considerable bandwidth to the concept. In a keynote that was devoted to blockchains, Dr. Samir Damani described how one of his companies, MintHealth, planned to use them to give individuals control over health data that is currently held by clinicians or researchers–and withheld from the individuals themselves.

I have previously covered the importance patient health records, and the open source project spotlighted by that article, HIE of One, now intends to use blockchain in a manner similar to MintHealth. In both projects, the patient owns his own data. MintHealth adds the innovation of offering rewards for patients who share their data with researchers, all delivered through the blockchain. The reward system is quite intriguing, because it would create for the first time a real market for highly valuable patient data, and thus lead to more research use along with fair compensation for the patients. MintHealth’s reward system also fits the connected health vision of promoting healthy behavior on a daily basis, to reduce chronic illness and health care costs.

Conclusion
Although progress toward connected health comes in fits and starts, the Connected Health Conference is still a bright spot in health care each year. For the first time this year, Partners’ Center for Connected Health partnered with another organization, the Personal Connected Health Alliance, and the combination seems to be a positive one. Certain changes were noticeable: for instance, all the breakout sessions were panels, and the keynotes were punctuated by annoying ads. An interesting focus this year was wellness in aging, the topic of the final panel. One surprising difference was the absence of the patient advocates from the Society for Participatory Medicine whom I’m used to meeting each year at this conference, perhaps because they held their own conference the day before.

The Center for Connected Health’s Joseph Kvedar still ran the program team, and the themes were familiar from previous years. This conference has become my touchstone for understanding health IT, and it will continue to be the place to go to track the progress of health care reform from a technological standpoint.

Measuring the Vital Signs of Health Care Progress at the Connected Health Conference (Part 2 of 3)

Posted on November 15, 2017 I Written By

Andy Oram is an editor at O'Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in open source, software engineering, and health IT, but his editorial output has ranged from a legal guide covering intellectual property to a graphic novel about teenage hackers. His articles have appeared often on EMR & EHR and other blogs in the health IT space. Andy also writes often for O'Reilly's Radar site (http://oreilly.com/) and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O'Reilly's Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

The first segment of this article introduced the themes of the Connected Health Conference and talked about the importance of validating what new technologies do using trials or studies like traditional medical advances. This segment continues my investigation into another major theme in health care: advanced interfaces.

Speaker from Validic at Connected Health Conference

Speaker from Validic at Connected Health Conference

Advanced interfaces
The compulsory picture of health care we’re accustomed to seeing, whenever we view hospital propaganda or marketing from health care companies, shows a patient in an awkward gown seated on an uncomfortable examination table. A doctor faces him or her full on–not a computer screen in site–exuding concern, wisdom, friendliness, and professionalism.

More and more, however, health sites are replacing this canonical photograph with one of a mobile phone screen speckled with indicators of our vital signs or thumbnail shot of our caregivers. The promise being conveyed is no longer care from a trusted clinician in the office, but instant access to all our information through a medium familiar to almost everyone everywhere–the personal mobile device.

But even touchscreen access to the world of the cloud is beginning to seem fusty. Typing in everything you eat with your thumbs, or even answering daily surveys about your mental state, gets old fast. As Dr. Yechiel Engelhard of TEVA said in his keynote, patients don’t want to put a lot of time into managing their illnesses, nor do doctors want to change their workflows. So I’m fascinated with connected health solutions that take the friction out of data collection and transmission.

One clear trend is the move to voice–or rather, I should say back to voice, because it is the original form of human communication for precise data. The popularity of Amazon Echo, along with Siri and similar interfaces, shows that this technology will hit a fever pitch soon. One research firm found that voice-triggered devices more than doubled in popularity between 2015 and 2016, and that more than half of Americans would like such a device in the home.

I recently covered a health care challenge using Amazon Alexa that demonstrates how the technology can power connected health solutions. Most of the finalists in the challenge were doing the things that the Connected Health Conference talks about incessantly: easy and frequent interactions with patients, analytics to uncover health problems, integration with health care providers, personalization, and so on.

Orbita is another company capitalizing on voice interfaces to deliver a range of connected health solutions, from simple medication reminders to complete care management applications for diabetes. I talked to CEO Bill Rogers, who explained that they provide a platform for integrating with AI engines provided by other services to carry out communication with individuals through whatever technology they have available. Thus, Orbita can talk through Echo, send SMS messages, interact with a fitness device or smart scale, or even deliver a reminder over a plain telephone interface.

One client of Orbita uses it platform to run a voice bot that talks to patients during their discharge process. The bot provides post-discharge care instructions and answers patients’ questions about things like pain management and surgery wound care. The results show that patients are more willing to ask questions of the bot than of a discharge nurse, perhaps because they’re not afraid of wasting someone’s time. Rogers also said services are improving their affective interfaces, which respond to the emotional tone of the patient.

Another trick to avoid complex interfaces is to gather as much data as possible from the patient’s behavior (with her consent, of course) to eliminate totally the need for her to manually enter data, or even press a button. Devices are getting closer to this kind of context-awareness. Following are some of the advances I enjoyed seeing at the Connected Health Conference.

  • PulseOn puts more health data collection into a wrist device than I’ve ever seen. Among the usual applications to fitness, they claim to detect atrial fibrillation and sleep apnea by shining a light on the user’s skin and measuring changes in reflections caused by variations in blood flow.
  • A finger-sized device called Gocap, from Common Sensing, measures insulin use and reports it over wireless connections to clinical care-takers. The device is placed over the needle end of an insulin pen, determines how much was injected by measuring the amount of fluid dispensed after a dose, and transmits care activity to clinicians through a companion app on the user’s smartphone. Thus, without having to enter any information by hand, people with diabetes can keep the clinicians up to date on their treatment.
  • One of the cleverest devices I saw was a comprehensive examination tool from Tyto Care. A small kit can carry the elements of a home health care exam, all focused on a cute little sphere that fits easily in the palm. Jeff Cutler, Chief Revenue Officer, showed me a simple check on the heart, ear, and throat that anyone can perform. You can do it with a doctor on the other end of a video connection, or save the data and send it to a doctor for later evaluation.

    Tyto Care has a home version that is currently being used and distributed by partners such as Heath Systems, providers, payers and employers, but will ultimately be available for sale to consumers for $299. They also offer a professional and remote clinic version that’s tailor-made for a school or assisted living facility.

A new Digital Therapeutics Alliance was announced just before the conference, hoping to promote more effective medical devices and allow solutions to scale up through such things as improving standards and regulations. Among other things, the alliance will encourage clinical trials, which I have already highlighted as critical.

Big advances were also announced by Validic, which I covered last year. Formerly a connectivity solution that unraveled the varying quasi-standard or non-standard protocols of different devices in order to take their data into electronic health records, Validic has created a new streaming API that allows much faster data transfers, at a much higher volume. On top of this platform they have built a notification service called Inform, which takes them from a networking solution to a part of the clinicians’ workflow.

Considerable new infrastructure is required to provide such services. For instance, like many medication adherence services, Validic can recognize when time has gone by without a patient reporting that’s he’s taken his pill. This level of monitoring requires storing large amounts of longitudinal data–and in fact, Validic is storing all transactions carried out over its platform. The value of such a large data set for discovering future health care solutions through analytics can make data scientists salivate.

The next segment of this article wraps up coverage of the conference with two more themes.

Measuring the Vital Signs of Health Care Progress at the Connected Health Conference (Part 1 of 3)

Posted on November 13, 2017 I Written By

Andy Oram is an editor at O'Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in open source, software engineering, and health IT, but his editorial output has ranged from a legal guide covering intellectual property to a graphic novel about teenage hackers. His articles have appeared often on EMR & EHR and other blogs in the health IT space. Andy also writes often for O'Reilly's Radar site (http://oreilly.com/) and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O'Reilly's Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

Attendees at each Connected Health Conference know by now the architecture of health reform promoted there. The term “connected health” has been associated with a sophisticated amalgam of detailed wellness plans, modern sensors, continuous data collection in the field, patient control over data, frequent alerts and reminders, and analytics to create a learning health care system. The mix remains the same each year, so I go each time to seek out progress toward the collective goal. This year, I’ve been researching what’s happening in these areas:

  • Validation through clinical trials
  • Advanced interfaces to make user interaction easier
  • Improved data sharing (interoperability)
  • Blockchains

Panel at Connected Health Conference

Panel at Connected Health Conference

There were a few other trends of interest, which I’ll mention briefly here. Virtual reality (VR) and augmented reality (AR) turned up at some exhibitor booths and were the topic of a panel. Some of these technologies run on generic digital devices–such as the obsession-inducing Pokémon GO game–while others require special goggles such as the Oculus Rift (the first VR technology to show a promise for widespread adoption, and now acquired by Facebook) or Microsoft’s HoloLens. VR shuts out the user’s surroundings and presents her with a 360-degree fantasy world, whereas AR imposes information or images on the surroundings. Both VR and AR are useful for teaching, such as showing an organ in 3D organ in front of a medical student on a HoloLens, and rotating it or splitting it apart to show details.

I haven’t yet mentioned the popular buzzword “telehealth,” because it’s subsumed under the larger goal of connected health. I do use the term “artificial intelligence,” certainly a phrase that has gotten thrown around too much, and whose meaning is subject of much dissension. Everybody wants to claim the use of artificial intelligence, just as a few years ago everybody talked about “the cloud.” At the conference, a panel of three experts took up the topic and gave three different definitions of the term. Rather than try to identify the exact algorithms used by each product in this article and parse out whether they constitute “real” artificial intelligence, I go ahead and use the term as my interviewees use it.

Exhibition hall at Connected Health Conference

Exhibition hall at Connected Health Conference

Let’s look now at my main research topics.

Validation through clinical trials
Health apps and consumer devices can be marketed like vitamin pills, on vague impressions that they’re virtuous and that doing something is better than doing nothing. But if you want to hook into the movement for wellness–connected health–you need to prove your value to the whole ecosystem of clinicians and caretakers. The consumer market just doesn’t work for serious health care solutions. Expecting an individual to pay for a service or product would limit you to those who can afford it out-of-pocket, and who are concerned enough about wellness to drag out their wallets.

So a successful business model involves broaching the gates of Mordor and persuading insurers or clinicians to recommend your solution. And these institutions won’t budge until you have trials or studies showing that you actually make a difference–and that you won’t hurt anybody.

A few savvy app and device developers build in such studies early in their existence. For instance, last year I covered a typical connected health solution called Twine Health, detailing their successful diabetes and hypertension trials. Twine Health combines the key elements that one finds all over the Connected Health Conference: a care plan, patient tracking, data analysis, and regular check-ins. Their business model is to work with employer-owned health plans, and to expand to clinicians as they gradually migrate to fee-for-value reimbursement.

I sense that awareness is growing among app and device developers that the way to open doors in health care is to test their solutions rigorously and objectively. But I haven’t found many who do so yet.

In the next segment of this article continues my exploration of the key themes I identified at the start of this article.