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Harvard Law Conference Surveys Troubles With Health Care

Posted on March 30, 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.

It is salubrious to stretch oneself and regularly attend a conference in a related field. At the Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics, one can bask in the wisdom of experts who are truly interdisciplinary (as opposed to people like me, who is simply undisciplined). Their Tenth Anniversary Conference drew about 120 participants. The many topics–which included effects of the Supreme Court rulings on the Affordable Care Act and other cases, reasons that accountable care and other efforts haven’t lowered costs, stresses on the pharmaceutical industry, and directions in FDA regulation–contained several insights for health IT professionals.

From my perspective, the center of the conference was the panel titled “Health Innovation Policy and Regulating Novel Technology.” A better title might have been “How to Make Pharma Profitable Again,” because most of the panelists specialized in pharmaceuticals or patents. They spun out long answers to questions about how well patents can protect innovation (recognizing a controversy); the good, the bad, and the ugly of pricing; and how to streamline clinical trials, possibly adding risk. Their pulses really rose when they were asked a question about off-label drug use. But they touched on health IT and suggested many observations that could apply to it as well.

It is well known that drug development and regulatory approval take years–perhaps up to 20 years–and that high-tech companies developing fitness devices or software apps have a radically different product cycle. As one panelist pointed out, it would kill innovation to require renewed regulatory approval for each software upgrade. He suggested that the FDA define different tiers of changes, and that minor ones with little risk of disrupting care be allowed automatically.

I look even farther. It is well known also that disruptive inventions displace established technologies. Just as people with mobile devices get along without desktop computers and even TV sets, medicines have displaced many surgical procedures. Now the medicines themselves (particularly, controversial mental health medicines) can sometimes be replaced by interactive apps and online services. Although rigorous testing is still lacking for most of these alternatives, the biggest barrier to their adoption is lack of reimbursement in our antiquated health payment system.

Instead of trying to individually fix each distortion in payment, value-based care is the reformer’s solution to the field’s inefficient use of treatment options. Value-based care requires more accurate information on quality and effectiveness, as I recently pointed out. And this in turn may lead to the more flexible regulations suggested by the panelist, with a risk that is either unchanged or raised by an amount we can tolerate.

Comparisons between information and other medical materials can be revealing. For instance, as the public found out in the Henrietta Lacks controversy, biospecimens are treated as freely tradable information (so long as the specimen is de-identified) with no patient consent required. It’s assumed that we should treat de-identified patient information the same way, but in fact there’s a crucial difference. No one would expect the average patient to share and copy his own biospecimens, but doing so with information is trivially easy. Therefore, patients should have more of a say about how their information is used, even if biospecimens are owned by the clinician.

Some other insights I picked up from this conference were:

  • Regulations and policies by payers drive research more than we usually think. Companies definitely respond to what payers are interested in, not just to the needs of the patients. One panelist pointed out that the launch of Medicare Part D, covering drugs for the first time, led to big new investments in pharma.

  • Hotels and other service-oriented industries can provide a positive experience efficiently because they tightly control the activities of all the people they employ. Accountable Care Organizations, in contrast, contain loose affiliations and do not force their staff to coordinate care (even though that was the ideal behind their formation), and therefore cannot control costs.

  • Patents, which the pharma companies consider so important to their business model, are not normally available to diagnostic tests. (The attempt by Myriad Genetics to patent the BRACA1 gene in order to maintain a monopoly over testing proves this point: the Supreme Court overturned the patent.) However, as tests get more complex, the FDA has started regulating them. This has the side effect of boosting the value of tests that receive approval, an advantage over competitors.

Thanks to Petrie-Flom for generously letting the public in on events with such heft. Perhaps IT can make its way deeper into next year’s conference.

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.

How Open mHealth Designed a Popular Standard (Part 3 of 3)

Posted on December 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://oreilly.com/) and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O'Reilly's Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

The first section of this article introduced the common schemas for mobile health designed by Open mHealth, and the second section covered the first two design principles driving their schemas. We’ll finish off the design principles in this section.

Balancing permissiveness and constraints

Here, the ideal is to get accurate measurements to the precision needed by users and researchers. But many devices are known to give fuzzy results, or results that are internally consistent but out of line with absolute measurements.

The goal adopted by Open mHealth is to firm up the things that are simple to get right and also critical to accuracy, such as units of measurement discussed earlier. They also require care in reporting the time interval that a measurement covers: day, week, month. There’s no excuse if you add up the walks recorded for the day and the sum doesn’t match the total steps that the device reports for that day.

Some participants suggested putting in checks, such as whether the BMI is wildly out of range. The problem (in terms of public health as well as technology) is that there are often outlier cases in health care, and the range of what’s a “normal” BMI can change. The concept of a maximum BMI is therefore too strict and ultimately unhelpful.

Designing for data liquidity

Provenance is the big challenge here: where does data come from, how was it collected, and what algorithm was used to manipulate it? Open mHealth expects data to go far and wide among researchers and solution providers, so the schema must keep a trail of all the things done to it from its origin.

Dr. Sim said the ecosystem is not yet ready to ensure quality. For instance, a small error introduced at each step of data collection and processing can add up to a yawning gap between the reported measure and the truth. This can make a difference not only to researchers, but to the device’s consumers. Think, for instance, of a payer basing the consumer’s insurance premium on analytics performed on data from the device over time.

Alignment with clinical data standards

Electronic health records are starting to accept medical device data. Eventually, all EHRs will need to do this so that monitoring and connected health can become mainstream. Open mHealth adopted widespread medical ontologies such as SNOMED, which may seem like an obvious choice but is not at all what the devices do. Luckily, Open mHealth’s schemas come pre-labelled with appropriate terminology codes, so device developers don’t need to get into the painful weeds of medical coding.

Modeling of Time

A seemingly simple matter, time is quite challenging. The Open mHealth schema can represent both points in time and time intervals. There are still subtleties that must be handled properly, as when a measurement for one day is reported on the next day because the device was offline. These concerns feed into provenance, discussed under “Designing for data liquidity.”

Preliminary adoption is looking good. The schema will certainly evolve, hopefully allowing for diversity while not splintering into incompatible standards. This is the same balance that FHIR must strike under much more difficult circumstances. From a distance, it appears like Open mHealth, by keeping a clear eye on the goal and a firm hand on the development process, have avoided some of the pitfalls that the FHIR team has encountered.

How Open mHealth Designed a Popular Standard (Part 1 of 3)

Posted on December 1, 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.

If standards have not been universally adopted in the health care field, and are often implemented incorrectly when adopted, the reason may simply be that good standards are hard to design. A recent study found that mobile health app developers would like to share data, but “Less progress has been made in enabling apps to connect and communicate with provider healthcare systems–a fundamental requirement for mHealth to realize its full value in healthcare management.”

Open mHealth faced this challenge when they decided to provide a schema to represent the health data that app developers, research teams, and other individuals want to plug into useful applications. This article is about how they mined the health community for good design decisions and decided what necessary trade-offs to make.

Designing a good schema involves intensive conversations with several communities that depend on each other but often have trouble communicating their needs to each other:

Consumers/users

They can tell you what they’re really interested in, and give you surprising insights about what a product should produce. In the fitness device space, for instance, Open mHealth was told that consumers would like time zones including with timing data–something that currently is supported rarely and poorly. Manufacturers find time zones hard to do, and feel little competitive pressure to offer them.

Vendors/developers

They can fill you in on the details of their measurements, which might be hard to discern from the documentation or the devices themselves. A simple example: APIs often retrieve weight values without units. If you’re collecting data across many people and devices for clinical or scientific purposes (e.g., across one million people for the new Precision Medicine Initiative), you can’t be guessing whether someone weighs 70 pounds or 70 kilograms.

Clinicians/Researchers

They offer insights on long-range uses of data and subtleties that aren’t noticeable in routine use by consumers. For example, in the elderly and those on some types of medications, blood pressure can be quite different standing up or lying down. Open mHealth captures this distinction.

With everybody weighing in, listening well and applying good medical principles is a must, otherwise, you get (as co-founder Ida Sim repeatedly said in our phone call) “a mess.” Over the course of many interviews, one can determine the right Pareto distribution: finding the 20% of possible items that satisfy 90% of the most central uses for mobile health data.

Open mHealth apparently made good use of these connections, because the schema is increasingly being adhered to by manufacturers and adopted by researchers as well as developers throughout the medical industry. In the next section of this article I’ll take a look at some of the legwork that that went into turning the design principles into a useful schema.

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://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.

Integrating Devices, Patients, and Doctors: HealthTap Releases an App for the Apple Watch

Posted on April 16, 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.

Doesn’t HealthTap want the same thing as all the other web sites and apps crowding into the health space? Immediate and intimate connections between doctors and patients. Accurate information at your fingertips, tailored to your particular condition. Software that supports your goals where automation makes sense and gets out of the way at other times.

HealthTap pursues this common vision in its own fashion. This week, its announcement of an app for Apple Watch pulls together the foundations HealthTap has been building and cleverly uses the visceral experience that the device on your wrist offers to meet more of the goals of modern, integrated health care.
Read more..

Annual Evaluation of Health IT: Are We Stuck in a Holding Pattern? (Part 3 of 3)

Posted on April 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.

The previous installments of this article covered major regulatory initiatives and standards projects. Some of the same questions have a direct impact on technological advances.

Medical Devices: Always With You, But Neither Here Nor There

One ad I saw compares a fitness device to a friend whispering in your ear wherever you go. Leaving aside control freak issues, what could be better for a modern patient with a condition that responds to behavior change than a personal device? Through such devices, we can implement a 24/7 cycle of medical care. We can also save enormous sums of money by treating the patient in his natural environment instead of a hospital or rehab facility.

The rapid spread of health devices was a foregone conclusion even before Apple thrust it into the mainstream with HealthKit. Last month’s launch of ResearchKit suggests that Apple will do the same for the big data revolution in health care championed by the Personal Genome Project, 23andMe (now back in the business after being reined in by the FDA), PatientsLikeMe, and other pioneer organizations. Apple Watch, an indulgence expected to grab the hearts of the affluent, might pull off the paradigm shift in how we interact digitally that Google Glass aimed at.

For these devices to make the leap from digital pets to real medical intervention, including a strengthening of the bond between clinicians and patients, they must satisfy stringent requirements for safety and accuracy. Current FDA regulations distinguish (in very rough terms–I am not a lawyer) between devices that make diagnoses or recommend treatments and other devices that merely measure vital signs or deliver reminders. If you make a diagnosis or recommend a treatment, you need to undergo a complex and expensive evaluation. People can also submit problems they find about your device to FDA’s medical device database.

Safety, accuracy, and transparency are goals well worth pursuing. The problem is not the cost of certification techniques, but the vast gulf between the development model assumed by certification and the one followed by modern developers of both software and hardware.

Development methods nowadays are agile. Developers incrementally release versions of software or hardware and upgrade them every few months. But certification processes require retesting every time the smallest change is made. And that’s reasonble because any tweak (even a configuration change out in the field) can cause a working device to fail. Such certifications work well for embedded systems in airplanes and nuclear facilities, and even critical medical devices that may live in patients’ bodies for decades. But they slow innovation to a crawl and raise prices precipitously.

Oddly enough, the tension between agile development and certification affects medical devices and electronic health records (EHRs) equally, and EHRs are equally prone to errors or misleading interfaces. Yet medical devices are regulated while EHRs are not. This contradiction must be resolved–but perhaps not by dropping the anvil of safety certification on all software used in medicine. The FDA can search for a more supple regulatory process that blesses certain classes of hardware and software while allowing for variation within them, backed up by guidelines for robust development and testing.

The FDA understands that it’s in an untenable situation but doesn’t know what to do. They have shaved off certain devices and marked them for lower levels of scrutiny, such as devices that transfer or display data collected elsewhere. The FDA has also led a muddled discussion over a national “test bed” for medical devices. More regulatory clarity in the area of both devices and EHRs, along with a push by regulators and users for better development practices, could help the field take off and realize the promise of personal devices.

Conclusion

I’m excited about the possibilities of health IT, but concerned that the current environment is insufficiently friendly for its deployment. On top of all the other factors I’ve cited that hold back the field, consider the urgent shortage of health IT staff. Providers and development firms have been bidding up salaries to steal each other’s employees, and attempts to increase the pool have shown disappointing results.

What I hear is that IT experts would love to get into health care, knowing that it can help the public immensely as well as pay off financially. But they have balked at the technical and working conditions in the field: hide-bound institutions, 50-year-old standards and tools, and of course the weight of standards and regulations to study.

How many of these topics will be covered at HIMSS? FHIR will be widely considered, I know, and the buzz over Meaningful Use is always strong. The question what will prod change in the system. Ultimately, it may come from a combination of consumer demand and regulatory pressure. Progress for the sake of progress has not been a prominent trait of health IT.

Health IT Security: What Can the Association for Computing Machinery (ACM) Contribute?

Posted on February 24, 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.

A dazed awareness of security risks in health IT has bubbled up from the shop floor administrators and conformance directors (who have always worried about them) to C-suite offices and the general public, thanks to a series of oversized data breaches that recentlh peaked in the Anthem Health Insurance break-in. Now the US Senate Health Committee is taking up security, explicitly referring to Anthem. The inquiry is extremely broad, though, promising to address “electronic health records, hospital networks, insurance records, and network-connected medical devices.”
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Ten-year Vision from ONC for Health IT Brings in Data Gradually

Posted on August 25, 2014 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.

This is the summer of reformulation for national U.S. health efforts. In June, the Office of the National Coordinator (ONC) released its 10-year vision for achieving interoperability. The S&I Framework, a cooperative body set up by ONC, recently announced work on the vision’s goals and set up a comment forum. A phone call by the Health IT Standards Committeem (HITSC) on August 20, 2014 also took up the vision statement.

It’s no news to readers of this blog that interoperability is central to delivering better health care, both for individual patients who move from one facility to another and for institutions trying to accumulate the data that can reduce costs and improve treatment. But the state of data exchange among providers, as reported at these meetings, is pretty abysmal. Despite notable advances such as Blue Button and the Direct Project, only a minority of transitions are accompanied by electronic documents.

One can’t entirely blame the technology, because many providers report having data exchange available but using it on only a fraction of their patients. But an intensive study of representative documents generated by EHRs show that they make an uphill climb into a struggle for Everest. A Congressional request for ideas to improve health care has turned up similar complaints about inadequate databases and data exchange.

This is also a critical turning point for government efforts at health reform. The money appropriated by Congress for Meaningful Use is time-limited, and it’s hard to tell how the ONC and CMS can keep up their reform efforts without that considerable bribe to providers. (On the HITSC call, Beth Israel CIO John Halamka advised the callers to think about moving beyond Meaningful Use.) The ONC also has a new National Coordinator, who has announced a major reorganization and “streamlining” of its offices.

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