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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://radar.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://radar.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.

Time For A Health Tracking Car?

Posted on December 30, 2015 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.

Several years ago, I attended a conference on advanced health technologies in DC. One of the speakers was Dr. Jay Sanders, president and CEO of The Global Telemedicine Group. And he had some intriguing things to say — especially given that no one had heard of a healthcare app yet and connected health was barely a vision.

One of Dr. Sanders’ recommendations was that automobile seat belts should integrate sensors that tracked your heart rhythm. After all, he noted, many of us spend hours a day behind the wheel, often under stressful conditions — so why not see how your heart is doing along the way? After all, some dangerous arrhythmias don’t show up at the moment you’re getting a checkup.

Flash forward to late 2015, and it seems Dr. Sanders’ ideas are finally being taken seriously. In fact, Ford Motor Co. and the Henry Ford Health System are co-sponsoring a contest offering $10,000 in prize money to employees creating smartphone apps linking healthcare with vehicles. While this doesn’t (necessarily) call for sensors to be embedded in seat belts, who knows what employees will propose?

To inspire potential entrants, the Connected Health Challenge sponsors have suggested a few ideas for possible designs, including in-vehicle monitoring and warnings and records access from the road. Other suggestions included appointment check-ins and technology allowing health data to be transmitted to providers. The contest kicks off on January 20th.

In some ways, this isn’t a huge surprise. After all, connected vehicles are already a very hot sector in the automotive business. According to research firm Parks Associates, there will be 41 million active Internet connections in U.S. vehicles by the end of this year.

At present, according to Parks, the connect car applications consumers are most interested in include mapping/navigation, information about vehicle performance, Bluetooth technology and remote control of vehicles using mobile phones. But that could change quickly if someone finds a way to interest the well-off users of wearables in car-based health tracking. (A possible direction for Fitbit, perhaps?)

Ordinarily, I’d have some doubts about Henry Ford Health System employees’ ability to grasp this market. But as I’ve reported elsewhere on Healthcare Scene, Henry Ford takes employee innovation very seriously.

For example, last year HFHS awarded a total of $10,000 in prizes to employees who submitted the best ideas for clinical applications of wearable technology. Not only that, the health system offers employees a 50% share of future revenues generated by their product ideas which reach the marketplace.

Now, it’s probably worth bearing in mind that the wearables industry is far more mature than the market for connected health apps in automobiles. (In fact, as far as I can tell, it’s still effectively zero.) Employees who participate in the challenge will be swinging at a far less-defined target, with less chance of seeing their ideas be adopted by the automotive industry.

Still, it’s interesting to see Ford Motor Co. and HFHS team up on this effort. I think something intriguing will come of it.

FDA Limitations Could Endanger Growth Of mHealth

Posted on December 28, 2015 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.

mHealth technology has virtually unlimited potential. But until the FDA begins putting its stamp of approval on mHealth tools, many providers won’t take them seriously. And that could be a big problem for mHealth’s future.

Unfortunately, early signs seem to suggest that the FDA is in over its head when it comes to regulating mHealth. According to speakers at a recent FDA Law Institute conference, it could be years before the agency even has a solid idea of how to proceed, Bloomberg reports.

Jeffrey Shapiro, a member of the Washington, D.C. law firm of Hyman Phelps & McNamara P.C., told the conference the FDA just isn’t equipped to handle the flood of new mHealth approaches. “Experience has shown that the FDA’s almost 40-year-old regulatory framework is a bad fit for much of today’s health IT with its networked ecosystems, rapid iterative improvement, deep collaboration between providers and end-users and focus on clinical decision support rather than direct diagnosis or treatment,” he told the audience.

The FDA dismisses the notion that it’s not prepared to regulate mHealth technologies. Bakul Patel, the agency’s associate director for digital health, told Reuters that the agency is planning to fill three new senior health scientist positions focused on digital health soon. That’s an encouraging step, though given that there are more than 165,000 health apps on the market, probably an inadequate one.

Sure, few of those app developers will apply for FDA approval. And the agency only plans to demand approval for technologies that are designed to be used as an accessory to a regulated medical devices, or transform a mobile platform into a regulated medical device. mHealth devices it has already approved include Airstrip Remote Patient Monitoring, the AliveCor Heart Monitor for iPhone and McKesson Cardiology’s ECG Mobile.

On the other hand, if Shapiro is right, the FDA could become a bottleneck which could severely stunt the growth of the U.S. mHealth industry. If nothing else, mHealth developers who seek FDA approval could be faced with a particularly prolonged approval process. While vendors wait for approval, they can keep innovating, but if their proposed blockbuster product is in limbo, it won’t be easy for them to stay solvent.

Not only that, if the FDA doesn’t have the institutional experience to reasonably evaluate such technologies, the calls it makes as to what is safe and efficacious may be off base. After all, apps and remote monitoring tools don’t bear much resemblance to traditional medical devices.

In theory, upstart mHealth companies which don’t have the resources to go through the FDA approval process can just proceed with their rollout. After all, the agency’s guidelines for requiring its approval are reasonably narrow.

But in reality, it seems unlikely that providers will adopt mHealth devices and apps wholesale until they get the FDA stamp of approval.  Whether they geniunely consider non-approved devices to be too lightweight for use, or fear being sued for using questionable technology, providers seem unlikely to integrate mHealth technology into their daily practice without the agency’s green light.

Given these concerns, we’d best hope that the FDA doesn’t begin requiring its approval for EMRs. Or at the very least, we should be glad that it didn’t jump in early. Who knows where EMR infrastructure would be if vendors had had to play patty-cake with the FDA from day one?

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://radar.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 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://radar.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.

Shimmer Addresses Interoperability Headaches in Fitness and Medical Devices

Posted on October 19, 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://radar.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 promise of device data pervades the health care field. It’s intrinsic to patient-centered medical homes, it beckons clinicians who are enamored with hopes for patient engagement, and it causes data analysts in health care to salivate. This promise also drives the data aggregation services offered by Validic and just recently, the Shimmer integration tool from Open mHealth. But according to David Haddad, Executive Director and Co-Founder of Open mHealth, devices resist attempts to yield up their data to programmers and automated tools.

Every device manufacturer has its own idiosyncratic way of handling data, focused on the particular uses for its own device. According to Haddad, for instance, different manufacturers provide completely different representations for the same data, leave out information like time zones and units, and can provide information as granular as once per second or as vague as once per day.. Even something as basic as secure connectivity is unstandardized. Although most vendors use the OAuth protocol that is widespread on the Web, many alter it in arbitrary ways. This puts barriers in the way of connecting to their APIs.

Validic and Shimmer have to overcome these hurdles one by one, vendor by vendor. The situation is just like the burdens facing applications that work with electronic health records. Haddad reports that the cacophony of standards among device vendors seems to come from lack of attention to the API side of their product, not deliberate obstructionism. With all the things device manufacturers have to worry about–the weight, feel, and attractiveness of the object itself, deals with payers and retailers offering the product, user interface issues, etc.–the API always seems to be an afterthought. (Apple may be an exception.)

So when Shimmer contacts the tool makers at these vendors, most respond and take suggestions in a positive manner. But they may have just one or two programmers working on the API, so progress is slow. It comes down to the old problem in health care: even with government emphasis on data sharing, there is still no strong advocate for interoperability in the field.

Why did Open mHealth take on this snake’s nest and develop Shimmer? Haddad says they figured that the advantages of open source–low cost of adoption and the ease of adding extensions–will open up new possibilities for app developers, clinical settings, and researchers. Most sites are unsure what to do with device data and are just starting to experiment with it. Being able to develop a prototype they can throw away later will foster innovation. Open mHealth has produced a detailed cost analysis in an appeal to researchers and clinicians to give Shimmer a try.

Shimmer, like the rest of the Open mHealth tools, rests on their own schemas for health data. The schemas in themselves can’t revolutionize health care. Every programmer maintains a healthy cynicism about schemas, harking back to xkcd’s cartoon about “one universal standard that covers everyone’s use cases.” But this schema took a broader view than most programs in health care, based on design principles that try to balance simplicity against usefulness and specificity. Of course, every attempt to maintain a balance comes up against complaints the the choices were too complex for some users, too simple for others. The true effects of Open mHealth appear as it is put to use–and that’s where open source tools and community efforts really can make a difference in health care. The schemas are showing value through their community adoption: they are already used by many sites, including some major commercial users, prestigious research sites, and start-ups.

A Pulse app translates between HL7 and the Open mHealth schema. This brings Open mHealth tools within easy reach of EHR vendors trying to support extensions, or users of the EHRs who consume their HL7-formatted data.

The Granola library translates between Apple’s HealthKit and JSON. Built on this library, the hipbone app takes data from an iPhone and puts it in JSON format into a Dropbox file. This makes it easier for researchers to play with HealthKit data.

In short, the walls separating medicine must be beaten down app by app, project by project. As researchers and clinicians release open source tools that tie different systems together, a bridge between products will emerge. Haddad hopes that more widespread adoption of the Open mHealth schema and Shimmer will increase pressure on device vendors to produce standardized data accessible to all.

Instead of a Weapon For Health Care Improvement, Monitoring Becomes Another Battleground

Posted on September 3, 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://radar.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.

If you wax enthusiastic about “patient engagement,” or work with health and fitness devices, or want to derive useful data from patient monitoring in the field, or–basically–read this blog for any reason at all, you should check out a recent study in the Journal of Medical Internet Research. It warns about psychological and logistical factors that trip us up when we try to get patients to monitor their vital signs.

The paper has a catchier title than most: “You Get Reminded You’re a Sick Person”: Personal Data Tracking and Patients With Multiple Chronic Conditions (citation: J Med Internet Res 2015;17(8):e202). The paper summarizes results of a qualitative study, focused not on the purposes or benefits of monitoring, but on how patients react to it. The messages from the patients cited are pretty eye-opening.

Doctors and public health officials know very well that most people with chronic conditions suffer from more than one. Just thinking about their meds, visits to the clinic, bills to pay, and the ways the conditions constrain their lives is more than enough effort for most of the patients. And yet on top of that we pile glucose readings, weighings, diet logs, and other measures with joyful assurances that they will lead to improvement in the patients’ lives.

Monitoring can be depressing. You can glibly say that denial and avoidance is worse in the long run, but people need to get on with their lives in the face of debilitating conditions. So it’s not surprising that many patients wait until an acute phase of an illness (feeling faint, for instance) before they use the monitoring devices.

We like to think of data as empowering, and sometimes go even further to say that it introduces objectivity into a field like health that is fraught with wrong impressions. But monitoring does not allow patients to put emotional distance between their egos and their medical problems. Quite the opposite–monitoring raises moral issues that turn patients off. They can easily feel shame or guilt for departing from their diet and exercise regimes. Because the link between behavior and vital signs is often unclear, patients have all the more reason to get frustrated and abandon monitoring.

Data can also get between the patient and doctor, whittling away the trust and empathy that’s so necessary for clinical improvement. Patients get annoyed seeing doctors putting so much stress on the numbers, and perhaps not paying attention to extenuating circumstances or important non-quantitative information reported by the patient.

Still, the study reported successes too. Some patients seem to get into the spirit of living deliberately and taking control of their devices to achieve positive change. It’s not clear from the study what makes these patients succeed.

The authors recommend that we find ways technologies can reduce burdens on patients, not increase them. (Would be nice if technologies acted the same way on clinicians, although this goes unmentioned by the authors). The paper doesn’t offer ways to achieve this desirable outcome, except to automate data capture more effectively. We can imagine some other ways as well.

Perhaps patients could be asked to treat monitoring as a personal research project. How does my glucose go up or down during the hours after a certain kind of meal? Does pulse change after exercise? If you engage patients’ curiosity, they may turn into Quantified Selfers.

Regular messaging has also been shown improve compliance–for instance, in one study about medication adherence and another about appointment scheduling. Messaging should be done intelligently and be tailored to the patient. It may convey the clinician’s concern to the patient reward her for sticking to a monitoring regimen.

The health care field is crying out for more data. To get meaningful data–and meaningful results in health care–it must have more meaning for patients. This is perhaps the leading user experience (UX) challenge in health care.

Wearables Data May Prevent Health Plan Denials

Posted on August 27, 2015 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.

This story begins, as many do, with a real-world experience. Our health plan just refused to pay for a sleep study for my husband, who suffers from severe sleep apnea, despite his being quite symptomatic. We’re following up with the Virginia Department of Insurance and fully expect to win the day, though we remain baffled as to how they could make such a decision. While beginning the complaint process, a thought occurred to me.

What if wearables were able to detect wakefulness and sleepiness, and my husband was being tracked 24 hours a day?  If so, assuming he was wearing one, wouldn’t it be harder for a health plan to deny him the test he needed? After all, it wouldn’t be the word of one doctor versus the word of another, it would be a raft of data plus his sleep doctor’s opinion going up against the health plan’s physician reviewer.

Now, I realize this is a big leap in several ways.

For one thing, today doctors are very skeptical about the value generated by patient-controlled smartphone apps and wearables. According to a recent survey by market research firm MedPanel, in fact, only 15% of doctors surveyed see wearables of health apps as tools patients can use to get better. Until more physicians get on board, it seems unlikely that device makers will take this market seriously and nudge it into full clinical respectability.

Also, data generated by apps and wearables is seldom organized in a form that can be accessed easily by clinicians, much less uploaded to EMRs or shared with health insurers. Tools like Apple HealthKit, which can move such data into EMRs, should address this issue over time, but at present a lack of wearable/app data interoperability is a major stumbling block to leveraging that data.

And then there’s the tech issues. In the world I’m envisioning, wearables and health apps would merge with remote monitoring technologies, with the data they generate becoming as important to doctors as it is to patients. But neither smartphone apps nor wearables are equipped for this task as things stand.

And finally, even if you have what passes for proof, sometimes health plans don’t care how right you are. (That, of course, is a story for another day!)

Ultimately, though, new data generates new ways of doing business. I believe that when doctors fully adapt to using wearable and app data in clinical practice, it will change the dynamics of their relationship with health plans. While sleep tracking may not be available in the near future, other types of sophisticated sensor-based monitoring are just about to emerge, and their impact could be explosive.

True, there’s no guarantee that health insurers will change their ways. But my guess is that if doctors have more data to back up their requests, health plans won’t be able to tune it out completely, even if their tactics issuing denials aren’t transformed. Moreover, as wearables and apps get FDA approval, they’ll have an even harder time ignoring the data they generate.

With any luck, a greater use of up-to-the-minute patient monitoring data will benefit every stakeholder in the healthcare system, including insurers. After all, not to be cliched about it, but knowledge is power. I choose to believe that if wearables and apps data are put into play, that power will be put to good use.

Accenture: “Zombie” Digital Health Startups Won’t Die In Vain

Posted on August 24, 2015 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.

I don’t know about you, but I’ve been screaming for a while about how VCs are blowing their money on questionable digital health ventures. To my mind, their investment patterns suggest that the smart money really isn’t that smart. I admit that sorting out what works in digital health/mHealth/connected health is very challenging, but it’s far from impossible if you immerse yourself in the industry. And given how much difference carefully-thought out digital health tools can make, it’s exasperating to watch failing digital health startups burn through money.

That being said, maybe all of those dollars won’t be wasted. According to no less an eminence grise than Accenture, failing digital health ventures will feed the stronger ones and make their success more likely. A new report from Accenture predicts that these “zombie” startups — half of which will die within two years, it says — will provide talent and technology to their surviving rivals. (OK, I agree, the zombie image is a bit unsettling, isn’t it?)

To bring us their horror movie metaphor, Accenture analyzed the status of 900 healthcare IT startups, concluding that 51% were likely to collapse within 20 months.  The study looked at ventures cutting across social, mobile, analytics, cloud and sensors technologies, which include wearables, telehealth and remote monitoring.

While most researchers try to predict who the winners will be in a given market, Accenture had a few words to say about the zombie also-rans. And what they found was that the zombies have taken in enough cash to have done some useful things, collecting nearly $4 billion in funding between 2008 and 2013.

The investments are part of an ongoing funding trend. In fact, digital health dollars are likely to pour in over the next two years as well, with healthcare IT startups poised to take in $2.5 billion more over the next two years, Accenture estimates. Funding should focus on four segments, including engagement (25%), treatment (25%), diagnosis (21%) and infrastructure (29%), the study found.

So what use are the dying companies that will soon litter the digital health landscape? According to Accenture, more-successful firms can reap big benefits by acquiring the failing startups. For example, healthcare players can do “acqui-hiring” deals with struggling digital health startups to pick up a deep bench of qualified tech staffers. They can pick up unique technologies (the 900 firms analyzed, collectively, had 1,700 patents). And acquiring firms can harvest the startups’ technology to improve their products and services lineups.

Not only that — and this is Anne, not Accenture talking — acquiring healthcare firms get a wonderful infusion of entrepreneurial energy, regardless of whether the acquired firm was booking big bucks or not. And I speak from long experience. I’ve known the leaders of countless tech startups, and there’s very little difference between those who make a gazillion dollars and those whose ventures die. Generally speaking, anyone who makes a tech startup work for even a year or two is incredibly insightful, creative, and extremely dedicated, and they bring a kind of excitement to any company that hires them.

So, backed by the corporate wisdom of Accenture, I’ve come to praise zombies, not to bury them. While they may give their corporate lives, their visions won’t be wasted. With any luck, the next generation of digital health companies will appreciate the zombies’ hard work and initiative, even if they’re no longer with us.