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Challenging Physicians’ Digital Health Fears

Posted on September 12, 2017 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

Like you, I thought I’d read everything about the reasons some doctors struggle with adopting digital health. Then, the following article showed up on my radar. While it covers some familiar ground, it’s a fairly nuanced take on physician objections to integrating digital health into their practice.

The article, “Top 10 Reasons Doctors Fear Digital Health,” comes from Brennan Spiegel, MD, MSHS, a gastroenterologist and co-creator of the MyGiHealth app.  Given his digital health involvement, he obviously has a dog in the fight, but to my mind, that doesn’t detract from the value of what he had to say.

All ten of his observations make sense, but in the interests of brevity I’ll pick out a few that I found particularly interesting. Below, I’ve summarized some of the concerns expressed by his colleagues, then shared a condensed version of his responses:

“Use digital health devices in my practice? How the world will I have time to check all the data?”

His response:  We need to train a new type of specialist called a “digitalist” who will monitor, interpret and act upon remote patient data. They will reside in an e-coordination facility and remotely track data from biosensors, portals, apps and social media. (EDITOR’S NOTE: To see how an e-coordination center works today, check out this piece on the Mercy Virtual Hospital.) Their job will be to combine the data with clinical parameters and knowledge about the patient’s medical history then act on what they’ve learned.

* “What is my legal liability here? What if remote data show that somebody is doing poorly, but nobody checks it? What if the patient dies when there was clear evidence something bad was going to happen?”

His response: Until you have a digitalist watching your back, you cannot take responsibility – including legal responsibility – for monitoring, interpreting and acting upon the data. As I see it, that will be the digitalist’s responsibility.

* “Digital devices are cool, but most people quit using them before long. How could digital health make any difference if our patients refuse to use the stuff?

His response: To make inroads with chronic illnesses like diabetes, heart failure or obesity, we need to change behavior. One way to achieve this comes from Joseph Kvedar at Partners HealthCare. Dr. Kvedar’s team not only personalizes its apps but hyper-personalizes them. By integrating everything from the time of day, step counts, local weather and levels of depression or anxiety, these apps can send pinpoint messages to patients at the right time and place. This approach may work to foster behavioral change.

* “How will digital health improve the value of care? Can it both improve outcomes and lower costs? Until it can prove that it can, insurance won’t pay for it.”

Proving that digital health solutions provide economic value to health systems is the toughest and yet most important obstacle to taking digital health into the mainstream. As more and more digital health solutions roll off the assembly line, we need to see them subjected to formal health-economic analysis as with any other medical innovation.

I don’t know about you, but I found this to be an intriguing discussion, especially the notion of a “digitalist” responsible for remote data management and response. I look forward to talking to Dr. Spiegel someday (perhaps at the Connected Health show!) and getting more of his insights.

New Research Identifies Game-Changing Uses For AI In Healthcare

Posted on June 27, 2017 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

In recent times, the use of artificial intelligence technology in healthcare has been a very hot topic. However, while we’ve come tantalizingly close to realizing its promise, no application that I know of has come close to transforming the industry. Moreover, as John Lynn notes, healthcare organizations will not get as much out of AI use if they are not doing a good job of working with both structured and unstructured data.

That being said, new research by Accenture suggests that those of us dismissing AI tech as immature may be behind the curve. Researchers there have concluded that when combined, key clinical health AI applications could save the US healthcare economy as much $150 billion by 2026.

Before considering the stats in this report, we should bear Accenture’s definition of healthcare AI in mind:

“AI in health presents a collection of multiple technologies enabling machines to sense, comprehend, act and learn, so they can perform administrative and clinical healthcare functions…Unlike legacy technologies that are only algorithms/tools that complement a human, health AI today can truly augment human activity.”

In other words, the consulting firm sees AI as far more than a data analytics tool. Accenture analysts envision an AI ecosystem that transforms and serves as an adjunct to the many healthcare processes. That’s a pretty ambitious take, though probably not a crazy one.

In its new report, Accenture projects that the AI health market will reach $6.6 billion by 2021, up from $600 million in 2014, fueled by the growing number of health AI acquisitions taking place. The report notes that the number of such deals has leapt from less than 20 in the year 2012 to nearly 70 by mid-2016.

Researchers predict that the following applications will generate the projected $150 billion in savings/value:

  • Robot-assisted surgery: $40 billion
  • Virtual nursing assistants: $20 billion
  • Administrative workflow assistance: $18 billion
  • Fraud detection: $17 billion
  • Dosage error reduction: $16 billion
  • Connected machines: $14 billion
  • Clinical trial participant identifier: $13 billion
  • Preliminary diagnosis: $5 billion
  • Automated image diagnosis: $3 billion
  • Cybersecurity: $2 billion

There are a lot of interesting things about this list, which goes well beyond current hot topics like the use of AI-driven chatbots.

One that stands out to me is that two of the 10 applications address security concerns, an approach which makes sense but hadn’t turned up in my research on the topic until now.

I was also intrigued to see robot-assisted surgery topping the list of high-impact health AI options. Though I’m familiar with assistive technologies like the da Vinci robot, it hadn’t occurred to me that such tools could benefit from automation and data integration.

I love the picture Accenture paints of how this might work:

“Cognitive robotics can integrate information from pre-op medical records with real-time operating metrics to physically guide and enhance the physician’s instrument precision…The technology incorporates data from actual surgical experiences to inform new, improved techniques and insights.”

When implemented properly, robot-assisted surgery will generate a 21% reduction in length of hospital stays, the researchers estimate.

Of course, even the wise thinkers at Accenture aren’t always right. Nonetheless, the broad trends report identifies seem like reasonable choices. What do you think?

And by all means check out the report – it’s short, well-argued and useful.

E-Patient Update: Doctors Need To Lead Tech Charge

Posted on April 7, 2017 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

Doctors, like any other group of people, vary in how comfortable they are with technology. Despite the fact that their job is more technology-focused than ever before, many clinicians use tech tools because they must.

As a result, they aren’t great role models when it comes to encouraging patients to engage with portals, try mobile apps or even pay their healthcare bills online. I too am frustrated when doctors can’t answer basic tech questions, despite my high comfort level with technology. I like to think that we’re on the same page, and I feel sort of alienated when my doctors don’t seem to care about the digital health advantage.

This needs to change. Given the extent to which technology permeates care delivery, physicians must become better at explaining how basic tech tools work, why they’re used and how they benefit patients.

Below, I’ve listed three tools which I consider to be critical to current medical practices, based on both my patient experiences and my ongoing research on health IT tools. To me, knowing something about each of them is unavoidable if doctors want to keep up with trends and improve patient care.

The top three tools I see as central to serving patients effectively are:

  • Patient portals: This is arguably the most important technical option doctors can share with patients. To get the most value out of portals, every doctor – especially in primary care – should be able to explain to patients why accessing their data can improve their health and lives.
  • Connected health: For a while, connected health/remote monitoring solutions were a high-end, expensive way to track patient health. But today, these options are everywhere and accessible virtually anyone. (My husband bought a connected glucose monitor for $10 a few weeks ago!) If nothing else, clinicians should be able to explain to patients how such devices can help tame chronic diseases and prevent hospitalizations.
  • Mobile apps: While few apps, if any, are universally trusted by doctors, there’s still plenty of them which can help patients log, measure and monitor important data, such as medication compliance or blood pressure levels. While they don’t need to understand how mobile apps work, they should know something of why patients can benefit from using them.

Of course, this list is brief, but it’s a decent place to start. After all, I’m not suggesting that physicians need to get a master’s in health IT to serve patients adequately; I’m just recommending that they study up and prepare to guide their patients in using helpful tools.

Ultimately, it’s not as important that clinicians use or even have a deep understanding of digital health tools, health bands, smartwatches, sensor-laden clothing or virtual reality. They don’t have to understand cybersecurity or know how to reboot a server. They just have to know how to help patients navigate the healthcare world as it is.

By this point, in fact, I’d argue that it’s irresponsible to avoid learning about technologies that can help patients manage their health. Bear in mind that even if they don’t act like it, even confident, experienced patients like me truly admire our doctors and take what they say seriously. So if I am enthusiastic about using tech tools to manage my health, but my doctor’s eyes glaze over when I talk about them, even I feel a bit discouraged. So why not learn enough to encourage me on my journey?

E-Patient Update: Give Us Patient Data Analytics

Posted on March 24, 2017 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

The other day, I sat down with my husband to check out the features of his new connected glucose monitor. My husband, a Type 2 diabetic, had purchased the Accu-Chek Aviva Connect, which when synched with a computer, displays readings data on the web.

After synching up his results with his desktop via Bluetooth, he entered a web portal and boom! There was a two-week history of his readings, with data points organized by what times they were taken. As part of its dashboard, the portal also displayed the highest and lowest readings taken during the time period, as well as citing the average difference between high and low readings (the size of the delta).

By going over this data, we were able to learn a few things about his current disease management efforts. For example, we saw that virtually all of the highest readings were taken between 6PM and 9PM, which helped him identify some behaviors that he could change.

Of course, for the professionals reading this, none of these features are all that impressive. In fact, they’re practically kid’s stuff, though I imagine his endocrinologist will get at least some benefit from the charts.

But I’m here to tell you that as patient data management goes, this is off-the-charts cool. After all, neither of us has had a chance to track key health metrics and act on them, at least not without doing our own brute number crunching with a spreadsheet. As you can imagine, we greatly prefer this approach.

Unfortunately, few patients have access to any kind of analytics tools that put our health data in context. And without such tools most of us don’t get much benefit out of accessing the data. It’s time for things to change!

Upgrade the portal

One of the most common ways patients access their health data is via a provider portal. Most commonly, portals display the results of diagnostic tests, including lab tests and the text of imaging results.

Sharing this data is a step in the right direction, but it’s not likely to empower patients on its own. After all, even an experienced clinician would find it difficult to make sense of dozens (or in the case of chronically-ill patients like me, hundreds) of test results.  Even if the portal provided educational material on each test, it may be too much information for a patient to absorb.

On the other hand, patients could do a lot with their data if it was displayed in a patient-friendly manner. The possibilities for improving data display are manifold. They include:

  • Displaying tests relating to specific concern (such as thyroid levels) in sequence over time
  • Offer a chart comparing related data points, such as blood pressure levels and cardiac functioning or kidney functioning paired with blood glucose levels
  • Display only outlier test values, along with expected ranges, and link to an explanation of what these values might mean
  • Have the portal auto-generate a list of questions patients should ask their doctor, based on any issues suggested by test data

By provider standards, these displays might be fairly mundane. But speaking as a patient, I think they’d be very valuable. I am compulsive enough to check all of my health data and follow up with questions, but few patients are, and any tools which helped them decide what action to take would represent a big step forward.

It would be even more useful if patients could upload results from health bands or smartwatches and cross-reference that data with testing results. But for the short term, it would be enough to help patients understand the data already in the system.

Giving patients more power

At first, some providers might object to giving patients this much information, as odd as it may sound. I’ve actually run into situations where a practice won’t share test data with a patient until the doctor has “approved” the results, apparently because they don’t want patients to be frightened by adverse information.

But if we want to engage patients, providers have to give give patients more power. If nothing else, we need a better way to look at our data, and learn how we can respond effectively.

To be fair, few providers will have the resources in-house to add patient data analytics tools to portals. Their vendors will have to add upgrades to their portal software, and that’s not likely to happen overnight. After all, while the technical challenges involved are trivial, developers will need to decide exactly how they’re going to analyze the data and what search capabilities patients should have.

But there’s no excuse for letting this issue go, either. If providers want patients to engage in their healthcare process, helping them understand their health data is one of the most important steps they can take. Expecting patients to dive in and figure it out themselves is unlikely to work.

How Twine Health Found a Successful Niche for a Software Service in Health Care

Posted on April 1, 2016 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.

Apps and software services for health care are proliferating–challenges and hackathons come up with great ideas week after week, and the app store contains hundreds of thousands of apps. The hard thing is creating a business model that sustains a good idea. To this end, health care incubators bring in clinicians to advise software developers. Numerous schemes of questionable ethics abound among apps (such as collecting data on users and their contacts). In this article, I’ll track how Twine Health tried different business models and settled on the one that is producing impressive growth for them today.

Twine Health is a comprehensive software platform where patients and their clinicians can collaborate efficiently between visits to achieve agreed-upon goals. Patients receive support in a timely manner, including motivation for lifestyle changes and expertise for medication adjustments. I covered the company in a recent article that showed how the founders ran some clinical studies demonstrating the effectiveness of their service. Validation is perhaps the first step for any developer with a service or app they think could be useful. Randomized controlled trials may not be necessary, but you need to find out from potential users what they want to see before they feel secure prescribing, paying for, and using your service. Validation will differentiate you from the hoards of look-alike competitors with whom you’ll share your market.

Dr. John Moore, co-founder of Twine Health, felt in 2013 that it was a good time to start a company, because the long-awaited switch in US medicine from fee-for-service to value-based care was starting to take root. Blue Cross and Blue Shield were encouraging providers to switch to Alternative Quality Contracts. The Affordable Care act of 2010 created the Medicare Shared Savings Program, which led to Accountable Care Organizations.

The critical role played by value-based-care has been explained frequently in the health care space. Current fee-for-service programs pay only for face-to-face visits and occasionally for telehealth visits. The routine daily interventions of connected health, such as text messages, checks of vital signs, and supportive prompts, receive no remuneration. The long-term improvements of connected health receive no support in the fee-for-value model, as much as individual clinicians may with to promote positive behavior among their patients.

Thus, Twine Health launched in 2014 with a service for clinicians. What they found, unfortunately, is that the hype about value-based care had gotten way ahead of its actual progress. The risk sharing by Accountable Care Organizations, such as under the Medicare Shared Savings Program, weren’t full commitments to delivering value, as when clinicians receive full capitation for a population and are required to deliver minimum health outcomes. Instead, the organizations were still billing fee-for-service. Medicare compared their spending to a budget at the end of the year, and, if the organization accrued less fee-for-service billing than Medicare expected, the organization got back 50-60% of the savings In the lowest track of the program, the organization didn’t even get penalized for exceeding costs–it was just rewarded for beating the estimates.

In short, Twine Health found that clinicians in ACOs in 2014 were following the old fee-for-service model and that Twine Health’s service was not optimal for their everyday practices. A recent survey from the NEJM Catalyst Insights Council showed that risk sharing and quality improvement are employed in a minority of health care settings, and are especially low in hospitals.

Collaborative care requires a complete rededication of time and resources. One must be willing to track one’s entire patient panel on a regular basis, guiding them toward ongoing behavior modification in the context of their everyday lives, with periodic office visits every few months. One also needs to go beyond treating symptoms and learn several skills of a very different type that traditional clinicians haven’t been taught: motivational interviewing, shared decision making, and patient self-management.

Over a period of months, a new model for Twine’s role in healthcare delivery started to become apparent: progressive, self-insured employers were turning their attention to value-based care and taking matters into their own hands because of escalating healthcare costs. They were moving much quicker than ACOs and taking on much greater risk.

The employers were contracting with innovative healthcare delivery organizations, which were building on-site primary care clinics (exclusive to that employer and located right at the place of work), near-site primary care clinics (shared across multiple employers), wellness and chronic disease coaching programs, etc. Unlike traditional healthcare providers, the organizations providing services to self-insured employers were taking fully capitated payments and, therefore, full risk for their set of services. Ironically, some of the self-insured employers were actually large health systems whose own business models still involved mostly fee-for-service payments.

With on-site clinics, wellness and chronic disease coaching organizations, and self-insured employers, Twine Health has found a firm and growing customer base. Dr. Moore is confident that the healthcare industry is on the cusp of broadly adopting value-based care. Twine Health and other connected health providers will be able to increase their markets vastly by working with traditional providers and insurers. But the Twine Health story is a lesson in how each software developer must find the right angle, the right time, and the right foothold to succeed.

Randomized Controlled Trials and Longitudinal Analysis for Health Apps at Twine Health (Part 2 of 2)

Posted on February 18, 2016 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 section of this article described the efforts of Dr. John Moore of Twine Health to rigorously demonstrate the effectiveness of a digital health treatment platform. As Moore puts it, Twine Health sought out two of the most effective treatment programs in the country–both Harvard’s diabetes treatment and MGH’s hypertension treatment are much more effective than the standard care found around the country–and then used their most effective programs for the control group of patients. The control group used face-to-face visits, phone calls, and text messages to keep in touch with their coaches and discuss their care plans.

The CollaboRhythm treatment worked markedly better than these exemplary programs. In the diabetes trial, they achieved a 3.2% reduction in diabetic patients’ A1C levels over three months (the control group achieved 2.0%). In the hypertension trial, 100% of patients reached a controlled blood pressure of less than 140/90 and the average reduction in blood pressure was 26mmHg (the control group had an average 16mmHg reduction and fewer than one-third of the patients went down less than 140/90).

What clinical studies can and cannot ensure

I see a few limitations with these clinical studies:

  • The digital program being tested combines several different intervention, as described before: reminders, messaging, virtual interactions, reports, and so on. Experiments show that all these things work together. But one can’t help wondering: what if you took out some time-consuming interaction? Could the platform be just as successful? But testing all the options would lead to a combinatorial explosion of tests.

    It’s important that interventions by coaches started out daily but decreased over the course of the study as the patient became more familiar and comfortable with the behavior called for in the care plans. The decrease in support required from the human coach suggests that the benefits are sustainable, because the subjects are demonstrating they can do more and more for themselves.

  • Outcomes were measured over short time frames. This is a perennial problem with clinical studies, and was noted as a problem in the papers. The researchers will contact subjects in about a year to see whether the benefits found in the studies were sustained. Even one year, although a good period to watch to see whether people bounce back to old behaviors, isn’t long enough to really tell the course of chronic illness. On the other hand, so many other life events intrude over time that it’s unfair to blame one intervention for what happens after a year.

  • Despite the short time frame for outcomes, the studies took years to set up, complete, and publish. This is another property of research practice that adds to its costs and slows down the dissemination of best practices through the medical field. The time frames involved explain why the researchers’ original Media Lab app was used for studies, even though they are now running a company on a totally different platform built on the same principles.

  • These studies also harbor all the well-known questions of external validity faced by all studies on human subjects. What if the populations at these Boston hospitals are unrepresentative of other areas? What if an element of self-selection skewed the results?

Bonnie Feldman, DDS, MBA, who went from dentistry to Wall Street and then to consulting in digital health, comments, “Creating an evidence base requires a delicate balancing act, as you describe, when technology is changing rapidly. Right now, chronic disease, especially autoimmune disease is affecting more young adults than ever before. These patients are in desperate need of new tools to support their self-care efforts. Twine’s early studies validate these important advances.”

Later research at Twine Health

Dr. Moore and his colleagues took stock of the tech landscape since the development of CollaboRhythm–for instance, the iPhone and its imitators had come out in the meantime–and developed a whole new platform on the principles of CollaboRhythm. Twine Health, of which Moore is co-founder and CEO, offers a platform based on these principles to more than 1,000 patients. The company expects to expand this number ten-fold in 2016. In addition to diabetes and hypertension, Twine Health’s platform is used for a wide range of conditions, such as depression, cholesterol control, fitness, and diet.

With a large cohort of patients to draw on, Twine Health can do more of the “big data” analysis that’s popular in the health care field. They don’t sponsor randomized trials like the two studies cited early, but they can compare patients’ progress to what they were doing before using Twine Health, as well as to patients who don’t use Twine Health. Moore says that results are positive and lasting, and that costs for treatment drop one-half to two-thirds.

Clinical studies bring the best scientific methods we know to validating health care apps. They are being found among a small but growing number of app developers. We still don’t know what the relation will be between randomized trials and the longitudinal analysis currently conducted by Twine Health; both seem of vital importance and they will probably complement each other. This is the path that developers have to take if they are to make a difference in health care.

Randomized Controlled Trials and Longitudinal Analysis for Health Apps at Twine Health (Part 1 of 2)

Posted on February 17, 2016 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.

Walking into a restaurant or a bus is enough to see that any experience delivered through a mobile device is likely to have an enthusiastic uptake. In health care, the challenge is to find experiences that make a positive difference in people’s lives–and proving it.

Of course, science has a time-tested method for demonstrating the truth of a proposition: randomized tests. Reproducibility is a big problem, admittedly, and science has been shaken by the string of errors and outright frauds perpetrated in scientific journals. Still, knowledge advances bit by bit through this process, and the goal of every responsible app developer in the health care space is the blessing offered by a successful test.

Consumer apps versus clinical apps

Most of the 165,000 health apps will probably always be labeled “consumer” apps and be sold without the expense of testing. They occupy the same place in the health care field as the thousands of untested dietary supplements and stem cell injection therapies whose promise is purely anecdotal. Consumer anger over ill-considered claims have led to lawsuits against the Fitbit device manufacturer and Lumosity mental fitness app, leading to questions about the suitability of digital fitness apps for medical care plans.

The impenetrability of consumer apps to objective judgment comes through in a recent study from the Journal of Medical Internet Research (JMIR) that asked mHealth experts to review a number of apps. The authors found very little agreement about what makes a good app, thus suggesting that quality cannot be judged reliably, a theme in another recent article of mine. One might easily anticipate that subjective measures would produce wide variations in judgment. But in fact, many subjective measures produced more agreement (although not really strong agreement) than more “objective” measures such as effectiveness. If I am reading the data right, one of the measures found to be most unreliable was one of the most “objective”: whether an app has been tested for effectiveness.

Designing studies for these apps is an uncertain art. Sometimes a study may show that you don’t know what to measure or aren’t running the study long enough. These possible explanations–gentler than the obvious concern that maybe fitness devices don’t achieve their goals–swirl about the failure of the Scripps “Wired for Health” study.

The Twine Health randomized controlled trials

I won’t talk any more about consumer apps here, though–instead I’ll concentrate on apps meant for serious clinical use. What can randomized testing do for these?

Twine Health and MIT’s Media Lab took the leap into rigorous testing with two leading Boston-area partners in the health care field: a diabetes case study with the Joslin Diabetes Center and a hypertension case study with Massachusetts General Hospital. Both studies compared a digital platform for monitoring and guiding patients with pre-existing tools such as face-to-face visits and email. Both demonstrated better results through the digital platform–but certain built-in limitations of randomized studies leave open questions.

When Dr. John Moore decided to switch fields and concentrate on the user experience, he obtained a PhD at the Media Lab and helped develop an app called CollaboRhythm. He then used it for the two studies described in the papers, while founding and becoming CEO of Twine Health. CollaboRhythm is a pretty comprehensive platform, offering:

  • The ability to store a care plan and make it clear to the user through visualizations.

  • Patient self-tracking to report taking medications and resulting changes in vital signs, such as glycemic levels.

  • Visualizations showing the patient her medication adherence.

  • Reminders when to take medication and do other aspects of treatment, such as checking blood pressure.

  • Inferences about diet and exercise patterns based on reported data, shown to the patient.

  • Support from a human coach through secure text messages and virtual visits using audio, video, and shared screen control.

  • Decision support based on reported vital statistics and behaviors. For instance, when diabetic patients reported following their regimen but their glycemic levels were getting out of control, the app could suggest medication changes to the care team.

The collection of tools is not haphazard, but closely follows the modern model of digital health laid out by the head of Partners Connected Health, Joseph Kvedar, in his book The Internet of Healthy Things (which I reviewed at length). As in Kvedar’s model, the CollaboRhythm interventions rested on convenient digital technologies, put patients’ care into their own hands, and offered positive encouragement backed up by clinical staff.

As an example of the patient empowerment, the app designers deliberately chose not to send the patient an alarm if she forgets her medication. Instead, the patient is expected to learn and adopt responsibility over time by seeing the results of her actions in the visualizations. In exit interviews, some patients expressed appreciation for being asked to take responsibility for their own health.

The papers talk of situated learning, a classic education philosophy that teaches behavior in the context where the person has to practice the behavior, instead of an artificial classroom or lab setting. Technology can bring learning into the home, making it stick.

There is also some complex talk of the relative costs and time commitments between the digital interventions and the traditional ones. One important finding is that app users expressed significantly better feelings about the digital intervention. They became more conscious of their health and appreciated being able to be part of decisions such as changing insulin levels.

So how well does this treatment work? I’ll explore that tomorrow in the next section of this article, along with strengths and weaknesses of the studies.

Significant Articles in the Health IT Community in 2015

Posted on December 15, 2015 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.

Have you kept current with changes in device connectivity, Meaningful Use, analytics in healthcare, and other health IT topics during 2015? Here are some of the articles I find significant that came out over the past year.

The year kicked off with an ominous poll about Stage 2 Meaningful Use, with implications that came to a head later with the release of Stage 3 requirements. Out of 1800 physicians polled around the beginning of the year, more than half were throwing in the towel–they were not even going to try to qualify for Stage 2 payments. Negotiations over Stage 3 of Meaningful Use were intense and fierce. A January 2015 letter from medical associations to ONC asked for more certainty around testing and certification, and mentioned the need for better data exchange (which the health field likes to call interoperability) in the C-CDA, the most popular document exchange format.

A number of expert panels asked ONC to cut back on some requirements, including public health measures and patient view-download-transmit. One major industry group asked for a delay of Stage 3 till 2019, essentially tolerating a lack of communication among EHRs. The final rules, absurdly described as a simplification, backed down on nothing from patient data access to quality measure reporting. Beth Israel CIO John Halamka–who has shuttled back and forth between his Massachusetts home and Washington, DC to advise ONC on how to achieve health IT reform–took aim at Meaningful Use and several other federal initiatives.

Another harbinger of emerging issues in health IT came in January with a speech about privacy risks in connected devices by the head of the Federal Trade Commission (not an organization we hear from often in the health IT space). The FTC is concerned about the security of recent trends in what industry analysts like to call the Internet of Things, and medical devices rank high in these risks. The speech was a lead-up to a major report issued by the FTC on protecting devices in the Internet of Things. Articles in WIRED and Bloomberg described serious security flaws. In August, John Halamka wrote own warning about medical devices, which have not yet started taking security really seriously. Smart watches are just as vulnerable as other devices.

Because so much medical innovation is happening in fast-moving software, and low-budget developers are hankering for quick and cheap ways to release their applications, in February, the FDA started to chip away at its bureaucratic gamut by releasing guidelines releasing developers from FDA regulation medical apps without impacts on treatment and apps used just to transfer data or do similarly non-transformative operations. They also released a rule for unique IDs on medical devices, a long-overdue measure that helps hospitals and researchers integrate devices into monitoring systems. Without clear and unambiguous IDs, one cannot trace which safety problems are associated with which devices. Other forms of automation may also now become possible. In September, the FDA announced a public advisory committee on devices.

Another FDA decision with a potential long-range impact was allowing 23andMe to market its genetic testing to consumers.

The Department of Health and Human Services has taken on exceedingly ambitious goals during 2015. In addition to the daunting Stage 3 of Meaningful Use, they announced a substantial increase in the use of fee-for-value, although they would still leave half of providers on the old system of doling out individual payments for individual procedures. In December, National Coordinator Karen DeSalvo announced that Health Information Exchanges (which limit themselves only to a small geographic area, or sometimes one state) would be able to exchange data throughout the country within one year. Observers immediately pointed out that the state of interoperability is not ready for this transition (and they could well have added the need for better analytics as well). HHS’s five-year plan includes the use of patient-generated and non-clinical data.

The poor state of interoperability was highlighted in an article about fees charged by EHR vendors just for setting up a connection and for each data transfer.

In the perennial search for why doctors are not exchanging patient information, attention has turned to rumors of deliberate information blocking. It’s a difficult accusation to pin down. Is information blocked by health care providers or by vendors? Does charging a fee, refusing to support a particular form of information exchange, or using a unique data format constitute information blocking? On the positive side, unnecessary imaging procedures can be reduced through information exchange.

Accountable Care Organizations are also having trouble, both because they are information-poor and because the CMS version of fee-for-value is too timid, along with other financial blows and perhaps an inability to retain patients. An August article analyzed the positives and negatives in a CMS announcement. On a large scale, fee-for-value may work. But a key component of improvement in chronic conditions is behavioral health which EHRs are also unsuited for.

Pricing and consumer choice have become a major battleground in the current health insurance business. The steep rise in health insurance deductibles and copays has been justified (somewhat retroactively) by claiming that patients should have more responsibility to control health care costs. But the reality of health care shopping points in the other direction. A report card on state price transparency laws found the situation “bleak.” Another article shows that efforts to list prices are hampered by interoperability and other problems. One personal account of a billing disaster shows the state of price transparency today, and may be dangerous to read because it could trigger traumatic memories of your own interactions with health providers and insurers. Narrow and confusing insurance networks as well as fragmented delivery of services hamper doctor shopping. You may go to a doctor who your insurance plan assures you is in their network, only to be charged outrageous out-of-network costs. Tools are often out of date overly simplistic.

In regard to the quality ratings that are supposed to allow intelligent choices to patients, A study found that four hospital rating sites have very different ratings for the same hospitals. The criteria used to rate them is inconsistent. Quality measures provided by government databases are marred by incorrect data. The American Medical Association, always disturbed by public ratings of doctors for obvious reasons, recently complained of incorrect numbers from the Centers for Medicare & Medicaid Services. In July, the ProPublica site offered a search service called the Surgeon Scorecard. One article summarized the many positive and negative reactions. The New England Journal of Medicine has called ratings of surgeons unreliable.

2015 was the year of the intensely watched Department of Defense upgrade to its health care system. One long article offered an in-depth examination of DoD options and their implications for the evolution of health care. Another article promoted the advantages of open-source VistA, an argument that was not persuasive enough for the DoD. Still, openness was one of the criteria sought by the DoD.

The remote delivery of information, monitoring, and treatment (which goes by the quaint term “telemedicine”) has been the subject of much discussion. Those concerned with this development can follow the links in a summary article to see the various positions of major industry players. One advocate of patient empowerment interviewed doctors to find that, contrary to common fears, they can offer email access to patients without becoming overwhelmed. In fact, they think it leads to better outcomes. (However, it still isn’t reimbursed.)

Laws permitting reimbursement for telemedicine continued to spread among the states. But a major battle shaped up around a ruling in Texas that doctors have a pre-existing face-to-face meeting with any patient whom they want to treat remotely. The spread of telemedicine depends also on reform of state licensing laws to permit practices across state lines.

Much wailing and tears welled up over the required transition from ICD-9 to ICD-10. The AMA, with some good arguments, suggested just waiting for ICD-11. But the transition cost much less than anticipated, making ICD-10 much less of a hot button, although it may be harmful to diagnosis.

Formal studies of EHR strengths and weaknesses are rare, so I’ll mention this survey finding that EHRs aid with public health but are ungainly for the sophisticated uses required for long-term, accountable patient care. Meanwhile, half of hospitals surveyed are unhappy with their EHRs’ usability and functionality and doctors are increasingly frustrated with EHRs. Nurses complained about technologies’s time demands and the eternal lack of interoperability. A HIMSS survey turned up somewhat more postive feelings.

EHRs are also expensive enough to hurt hospital balance sheets and force them to forgo other important expenditures.

Electronic health records also took a hit from ONC’s Sentinel Events program. To err, it seems, is not only human but now computer-aided. A Sentinel Event Alert indicated that more errors in health IT products should be reported, claiming that many go unreported because patient harm was avoided. The FDA started checking self-reported problems on PatientsLikeMe for adverse drug events.

The ONC reported gains in patient ability to view, download, and transmit their health information online, but found patient portals still limited. Although one article praised patient portals by Epic, Allscripts, and NextGen, an overview of studies found that patient portals are disappointing, partly because elderly patients have trouble with them. A literature review highlighted where patient portals fall short. In contrast, giving patients full access to doctors’ notes increases compliance and reduces errors. HHS’s Office of Civil Rights released rules underlining patients’ rights to access their data.

While we’re wallowing in downers, review a study questioning the value of patient-centered medical homes.

Reuters published a warning about employee wellness programs, which are nowhere near as fair or accurate as they claim to be. They are turning into just another expression of unequal power between employer and employee, with tendencies to punish sick people.

An interesting article questioned the industry narrative about the medical device tax in the Affordable Care Act, saying that the industry is expanding robustly in the face of the tax. However, this tax is still a hot political issue.

Does anyone remember that Republican congressmen published an alternative health care reform plan to replace the ACA? An analysis finds both good and bad points in its approach to mandates, malpractice, and insurance coverage.

Early reports on use of Apple’s open ResearchKit suggested problems with selection bias and diversity.

An in-depth look at the use of devices to enhance mental activity examined where they might be useful or harmful.

A major genetic data mining effort by pharma companies and Britain’s National Health Service was announced. The FDA announced a site called precisionFDA for sharing resources related to genetic testing. A recent site invites people to upload health and fitness data to support research.

As data becomes more liquid and is collected by more entities, patient privacy suffers. An analysis of web sites turned up shocking practices in , even at supposedly reputable sites like WebMD. Lax security in health care networks was addressed in a Forbes article.

Of minor interest to health IT workers, but eagerly awaited by doctors, was Congress’s “doc fix” to Medicare’s sustainable growth rate formula. The bill did contain additional clauses that were called significant by a number of observers, including former National Coordinator Farzad Mostashari no less, for opening up new initiatives in interoperability, telehealth, patient monitoring, and especially fee-for-value.

Connected health took a step forward when CMS issued reimbursement guidelines for patient monitoring in the community.

A wonky but important dispute concerned whether self-insured employers should be required to report public health measures, because public health by definition needs to draw information from as wide a population as possible.

Data breaches always make lurid news, sometimes under surprising circumstances, and not always caused by health care providers. The 2015 security news was dominated by a massive breach at the Anthem health insurer.

Along with great fanfare in Scientific American for “precision medicine,” another Scientific American article covered its privacy risks.

A blog posting promoted early and intensive interactions with end users during app design.

A study found that HIT implementations hamper clinicians, but could not identify the reasons.

Natural language processing was praised for its potential for simplifying data entry, and to discover useful side effects and treatment issues.

CVS’s refusal to stock tobacco products was called “a major sea-change for public health” and part of a general trend of pharmacies toward whole care of the patient.

A long interview with FHIR leader Grahame Grieve described the progress of the project, and its the need for clinicians to take data exchange seriously. A quiet milestone was reached in October with a a production version from Cerner.

Given the frequent invocation of Uber (even more than the Cheesecake Factory) as a model for health IT innovation, it’s worth seeing the reasons that model is inapplicable.

A number of hot new sensors and devices were announced, including a tiny sensor from Intel, a device from Google to measure blood sugar and another for multiple vital signs, enhancements to Microsoft products, a temperature monitor for babies, a headset for detecting epilepsy, cheap cameras from New Zealand and MIT for doing retinal scans, a smart phone app for recognizing respiratory illnesses, a smart-phone connected device for detecting brain injuries and one for detecting cancer, a sleep-tracking ring, bed sensors, ultrasound-guided needle placement, a device for detecting pneumonia, and a pill that can track heartbeats.

The medical field isn’t making extensive use yet of data collection and analysis–or uses analytics for financial gain rather than patient care–the potential is demonstrated by many isolated success stories, including one from Johns Hopkins study using 25 patient measures to study sepsis and another from an Ontario hospital. In an intriguing peek at our possible future, IBM Watson has started to integrate patient data with its base of clinical research studies.

Frustrated enough with 2015? To end on an upbeat note, envision a future made bright by predictive analytics.

We’re Just Getting Started with an Internet of Healthy Things (Part 3 of 3)

Posted on November 27, 2015 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 sections of this article described the state of health care today and some of the impressive advances described in Joseph Kvedar’s new book, The Internet of Healthy Things. Now we’ll look at the possibilities for advancing further, and what stands in the way.

Futures postponed

Later in the book, Kvedar explores the promise of analytics. On a small scale, analytics can tie the results of traditional clinical research to recommendations for individuals. For instance, if the A1C hemoglobin of a person with diabetes hits a certain level that clinical research has established as dangerous, she can be notified. We also know what heart rates are best for exercising and other useful statistics. Walgreens and CVS also use data at this level to market their products to consumers who sign up for their fitness tracking programs.
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We’re Just Getting Started with an Internet of Healthy Things (Part 2 of 3)

Posted on November 25, 2015 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 section of this article described the dire condition of health care today. So where does Kvedar’s book, The Internet of Healthy Things,fit into all this? It encapsulates all those years of learning at his Center for Connected Health, set up by the Boston-area giant Partners HealthCare and now renamed Partners Connected Health. From these insights, the book pinpoints the areas where innovators can make headway. He shows the gap between how we approach chronic health conditions now–even among the companies experimenting with mobile health and patient engagement–and the ideal for which the Partners Connected Health is striving. In reviewing his suggestions, I’ll try also to shine lights into passageways he did not explore.

Lessons from the field

Kvedar divides the evolution of connected health into three broad phases. Most companies are now in the first phase of simply reporting statistics back to patients and doctors. You can find out from a mobile app what your blood sugar is, and from your fitness bracelet how far you’ve walked during the day. This phase can have some benefits on athletes and the small set of Quantified Selfers who love data, but has absolutely no appeal for the vast mass of people who most need support.

Partners Connected Health has entered the second phase and has its own data to show the great strides it has made. In this phase, you engage the patients by connecting him to his providers, family, and friends, making him feel watched (the Sentinel Effect) and therefore extracting healthier behavior. This starts to achieve the changes we want, but is still limited in the people we reach.

The third stage is to fit the intervention directly to the lifestyle and needs of the individual, a process Kvedar calls “hyperpersonalization.” If walking your dog is an important part of your life, the system should feed you messages encouraging you to do things that improve your endurance and walking ability. If you want to fit into smaller clothing for an upcoming wedding, focus on everything that can get your waistline down.

Kvedar’s vision does not seem to be the automated-intelligence utopia laid out by Vinod Khosla and others, where patients get automated diagnoses and treatment recommendations from the “cloud” and avoid physicians for most ailments. Rather, technology for Kvedar supports a strong relationship between patient and clinician. At the same time, the technology extends the clinician’s reach–and allows her to treat many more people with greater effectiveness–by bringing the treatment plan into the patient’s everyday life, throughout the day.

The first chapter of the book lays out a fantasy scenario for an automated coach that follows the individual around and sends messages right before he reaches for a cookie or is about to stay up too late at night. Kvedar unveiled the same scenario, which was quite amusing, in his introduction to the Connected Health conference. I covered the major aspects of this hyperpersonalization–automated, contextual, motivational, empowering, and incentivizing–in another article. It has to be done very careful in order not to appear intrusive and annoying, but it offers a greater promise to change behavior than anything else we know.

I already see one difficulty with organizations aiming at this vision of health care. Kvedar talks a great deal about apps–the little agents you download from the Apple Store or Google Play. But hyperpersonalization is not an app. It’s a whole environment for dealing with personal lifestyle–aided by apps, to be sure, but requiring a deep investigation into the patients’ needs and interests. What Kvedar is really calling for is not a prize-winning app, but a reconfiguration of our health system.

In the face of such a challenge, several organizations are stepping up. Among their ranks are scattered a few traditional health care organizations (providers such as Kaiser Permanente and Kvedar’s own Partners HealthCare, insurers such as Aetna) but most come from the outside. Kvedar concentrates on the clinics and wellness programs set up by Walgreens pharmacy. Their integration of convenience and support for ongoing behavior change is much more thorough than most people realize.

Another example of an integrated strategy is provided by a single teenager whose caretakers are monitoring his diabetes remotely. The process brings the teen’s doctor and mother into the picture with technologies that include an unusual skin sensor, Apple HealthKit, and an Epic health record. The solution is not an open one.

It’s great for Walgreen’s to fix sore throats and minor cuts, and even to start offering primary care. But people with serious health needs will eventually need to interact with a traditional clinic or hospital. If these institutions still can’t accept data from the urgent health clinic (some already can), the same old inefficiencies and errors will re-emerge. And this failure to evolve with the times is a danger even though, as Dr. Kvedar repeatedly warns, it threatens the continued existence of the traditional hospitals.

The final section of this article will look at the gap between where we are now and where The Internet of Healthy Things would like us to be.