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How Open mHealth Designed a Popular Standard (Part 2 of 3)

Posted on December 2, 2015 I Written By

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

The previous section of this article introduced the intensive research and consultation strategy used by Open mHealth to develop a common schema for exploiting health data by app developers, researchers, clinicians, individuals, and manufacturers of medical and fitness devices. Next we’ll go through the design principles with a look at specific choices and trade-offs.

Atomicity

Normally, one wants to break information down into chunks as small as possible. By doing this, you allow data holders to minimize the amount of data they need to send data users, and data users are free to scrutinize individual items or combine them any way they want. But some values in health need to be chunked together. When someone requests blood pressure, both the systolic and diastolic measures should be sent. The time zone should go with the time.

On the other hand, mHealth doesn’t need combinations of information that are common in medical settings. For instance, a dose may be interesting to know, but you don’t need the prescribing doctor, when the prescription was written, etc. On the other hand, some app developers have asked the prescription to include the number of refills remaining, so the app can issue reminders.

Balancing parsimony and complexity

Everybody wants all the data items they find useful, but don’t want to scroll through screenfuls of documentation for other people’s items. So how do you give a bewildering variety of consumers and researchers what they need most without overwhelming them?

An example of the process used by Open mHealth was the measurement for blood sugar. For people with Type 1 or Type 2 diabetes, the canonical measurement is fasting blood sugar first thing in the morning (the measurement can be very different at different times of the day). This helps the patients and their clinicians determine their overall blood sugar control. Measurements of blood sugar in relation to meals (e.g., two hours after lunch) or to sleep (e.g., at bedtime) is also clinically useful for both patients and clinicians.

Many of these users are curious what their blood sugar level is at other times, such as after a run. But to extend the schema this way would render it mind-boggling. And Dr. Sim says these values have far less direct clinical value for people with Type 2 diabetes, who are the majority of diabetic patients. So the schema sticks with reporting blood sugar related to meals and sleep. If users and vendors work together, they are free to extend the standard–after all, it is open source.

Another reason to avoid fine-grained options is that it leads to many values being reported inconsistently or incorrectly. This is a concern with the ICD-10 standard for diagnoses, which has been in use in europe for a long time and became a requirement for billing in the US since early October. ICD-9 is woefully outdated, but so much was dumped into ICD-10 that its implementation has left clinicians staying up nights and ignoring real opportunities for innovation. (Because ICD is aimed mostly at billing, it is not used for coding in Open mHealth schemas.)

Thanks to the Open mHealth schema, a dialog has started between users and device manufacturers about what new items to include. For instance, it could include average blood sugar over a fixed period of time, such as one month.

In the final section of this article, we’ll cover the rest of the design principles.

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.

WearDuino Shows That Open Source Devices Are a Key Plank in Personal Health

Posted on August 13, 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.

New devices are democratizing health. We see it not only in the array or wearable fitness gear that an estimated 21 percent of Americans own (and that some actually wear), but also in innovative uses for mobile phones (such as testing vision in regions that lack doctors or checking athletes for concussions) and now in low-cost devices that are often open source hardware and software. Recent examples of the latter include the eyeSelfie, which lets a non-professional take an image of his retina, and the WearDuino, a general-purpose personal device that is the focus of this article.

WearDuino is the brainchild of Mark Leavitt, a medical internist who turned to technology (as have so many doctors pursuing visions of radical reform in health care). I ran into Leavitt at the 2015 Open Source convention, where he also described his work briefly in a video interview.

Leavitt’s goal is to produce a useful platform that satisfies two key criteria for innovation: low-cost and open. Although some of the functions of the WearDuino resembles devices on the market, you can take apart the WearDuino, muck with it, and enhance it in ways those closed platforms don’t allow.

Traits and Uses of WearDuino
Technically, the device has simple components found everywhere, but is primed for expansion. A small Bluetooth radio module provides the processing, and as the device’s name indicates, it supports the Arduino programming language. To keep power consumption low there’s no WiFi, and the device can run on a cheap coin cell battery for several months under normal use.

Out of the box, the WearDuino could be an excellent fitness device. Whereas most commercial fitness wearables collect their data through an accelerometer, the WearDuino has an accelerometer (which can measure motion), a gyroscope (which is useful for more complex measurements as people twist and turn), and a magnetometer (which acts as a compass). This kind of three-part device is often called a “9-degree of freedom sensor,” because each of those three measurements is taken in three dimensions.

When you want more from the device, such as measuring heartbeat, muscle activity, joint flexing, or eye motion, a board can be added to one of the Arduino’s 7 digital I/O pins. Leavitt said that one user experimented with a device that lets a parent know when to change a baby’s diaper, through an added moisture detector.

Benefits of an Open Architecture
Proprietary device manufacturers often cite safety reasons for keeping their devices closed. But Leavitt believes that openness is quite safe through most phases of data use in health. Throughout the stages of collecting data, visualizing the relationships, and drawing insights, Leavitt believes people should be trusted with any technologies they want. (I am not sure these activities are so benign–if one comes up with an incorrect insight it could lead you to dangerous behavior.) It is only when you get to giving drugs or other medical treatments that the normal restrictions to professional clinicians makes sense.

Whatever safety may adhere to keeping devices closed, there can be no justification on the side of the user for keeping the data closed. And yet proprietary device manufacturers play games with the user’s data (and not just games for health). Leavitt, for instance, who wears a fitness monitor, says he can programmatically download a daily summary of his footsteps, but not the exact amounts taken at different parts of the day.

The game is that device manufacturers cannot recoup the costs of making and selling the devices through the price of the device alone. Therefore, they keep hold of users’ data and monetize it through marketing, special services, and other uses.

Leavitt doesn’t have a business plan yet. Instead, in classic open source practice, he is building community. Where he lives in Portland, Oregon a number of programmers and medical personnel have shown interest. The key to the WearDuino project is not the features of the device, but whether it succeeds in encouraging an ecosystem of useful personal monitors around it.

If Employers Can’t Improve Individuals’ Health, How Can Accountable Care Organizations?

Posted on June 11, 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.

There seems to be a healthy competition among companies to pump up their staff’s participation in “wellness” programs. One recent poll of employers found that 60% offer incentives of some type for healthy behavior. But beyond anecdotal evidence, rigorous studies are casting doubt on the effectiveness of wellness programs. And that makes me wonder: will doctors face the same problems while trying to bend the health cost curve?

Talk to employers, and most will gush with enthusiasm over their wellness programs, which are spreading far and wide in the U.S. But at last week’s Health Privacy Summit, law professor Lindsay Wiley reviewed the literature and found a very different picture. A clinical study of a weight loss program found that the average weight loss it produced was…one pound. Other studies found that if people lost weight or stopped tobacco use to earn financial incentives, they “bounced back” and regained the weight or resumed smoking. Often, weight gains were more than what had been lost.

We should acknowledge that wellness programs take many forms, some showing more promise than others. General initiatives to put healthier food in cafeterias and vending machines seem benign. Incentives for employees to join a gym and get regular medical check-ups also seem successful in meeting those goals. But what health reformers really hope for–whether in the workplace or through clinical efforts such as Accountable Care Organizations–is thorough-going behavior change. Long-term lifestyle practices that reduce the incidence of diabetes, heart failure, etc. are what both employers and insurers seek in order to reduce the costs of health care plans. And that’s just where the wellness programs haven’t demonstrated results.

The panel in which Wiley participated also covered other weaknesses of fitness programs. Some 18% tie incentives to the output of fitness devices or other biomarkers, and nearly 50% intend to move in that direction in the future. But many consumer devices are inaccurate. Furthermore, many are easy to deceive by employees determined to game the system. There are even videos online that tell people how to manipulate results.

Concerns over privacy and discrimination run through these systems. Any device given by an employer to an employee–a Fitbit just as much as a laptop–remains the property of the employer, and so does the data on that device. Do we want employers to have unrestricted access to everything a device says about us? Walls keeping personal data in fitness programs away from human resource departments are insubstantial and easy to penetrate.

It’s disappointing that we lack firm evidence that wellness programs can lead to life-enhancing behavioral changes. But the lessons we learned from employees also have upsetting implications for the rest of the health care system. The whole premise of risk sharing (such as in ACOs) is that clinicians can persuade their patients to reject a lifetime of unhealthy eating, smoking, and sedentary habits. What if they can’t? Are clinicians being set up to fail? Bob Kocher suggested as much in a panel at the recent Health Datapalooza, when he pointed out that many patients have serious and multiple chronic conditions that are hard to turn around.

Some ACOs are demonstrating cost savings through low-hanging fruit, such as calling up patients to remind them to come to appointments or take their medications. These can make a big difference, but after we’ve exhausted the benefits of these simple interventions, how can we move to the next level of changing lifestyles?

I do have hope for lifestyle change. It will involve a lot more than crude incentives. It will require a clinician or other professional to form a trust relationship with a patient. It will require a lot of education. And technology can probably help. If integrated into a clear, individualized plan that the patient buys into, communications technologies and sensors can help patients stick to their commitments.

Health is also a community effort–a key point lost in the rush to wellness programs. Setting up such programs implies that each person is responsible for his or her own health. It moves the blame from social trends in food, transportation, air quality, etc. to individuals.

The workplace is where we spend an increasing number of our waking hours, so it makes sense to put the workplace in the spotlight of health care efforts. But we must let wellness programs whitewash employers’ responsibility for increasing stress, providing ergonomically destructive environments, or disrupting employees’ sleep. A recent Dilbert cartoon is relevant here. Let’s each take responsibility–as employers, as clinicians, as public health officials, and as individuals–for the things over which we have control.