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Barriers to Patient-Centered Research Aired at Harvard Symposium

Posted on July 2, 2018 I Written By

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

While writing about health IT, I routinely find myself at legal conferences. Regulatory issues about patient privacy and safety arise everywhere health IT tries to have an impact, so people promoting change must keep in touch with policy-makers and lawyers in the health care area.

Thus I went this past Friday to Harvard for a one-day symposium, “Putting Patients at the Center of Research: Opportunities and Challenges for Ethical and Regulatory Oversight,” sponsored by Harvard’s Petrie-Flom Center.

Audience at Patient-Centered conference at Harvard

*Audience at Patient-Centered conference at Harvard

Involving patients in patient care is a surprisingly recent concern. There was a time when doctors made all the decisions, delivering them as if they had come directly from the entrails of an oracular temple. Visitors were severely limited at hospitals, because family members just got in the way of the professional staff. And although the attitude toward engaging patients and their families has softened somewhat in health care, rigid boundaries still exist in research.

As project leader Joel Weissman pointed out at the beginning of the Petrie-Flom conference, patient rights weren’t considered by health care professionals until the 1980s, as outgrowths of the civil rights and women’s rights movements. Patient engagement languished still longer. It received a legal toehold in the 2010 Affordable Care Act, which set up the Patient-Centered Outcomes Research Institute. Although more researchers over the past eight years have warmed to the idea of engaging with patients in other ways than subjects of clinical trials, the Petrie-Flom conference highlighted how little progress we have made.

In a “nothing about us without us” era, it would seem odd to an outsider like me that patients should be excluded from the roles now being tentatively offered:

  • Joining the research team in some capacity
  • Recruiting subjects for trials and engaging the patient community
  • Helping disseminate results
  • Acting as consultants in some other way

But risks are certainly entailed by inserting non-professionals of any stripe into the research environment, so some criteria and processes need to be set up. Before filling non-traditional roles, patients should be required to undergo training in ethics, the science behind the study, and some of the methodology. There are particular risks when the patients have access to personally identifiable data. (I don’t see why this should ever be necessary, but the possibility was raised several times during the day.)

The panelists also cited conflicts of interest as a risk. Many researchers recruit engaged patients from the companies that make related drugs or other products, simply because those are easy places to recruit. This problem highlights the importance of casting a wide net and recruiting diverse populations as engaged patients. However, one could argue that merely suffering from the condition that the researchers are investigating leaves one with a conflict of interest: you want the research to produce a cure, so you may not be even-handed in your acceptance of negative results.

What spurred this conference? The Petrie-Flom Center and PCORI have spent the past academic year doing a study of patient-centered research, and recently published an article by a team led by Weissman. The center presented the results at Friday’s conference to an audience of some 80 members of the health care field and interested observers.

The study was narrow and intensive. It focused on the attitudes of those running Institutional Review Boards, which are notoriously conservative. Thus, in my opinion, the results focused on what was holding back patient-centered research rather than what was already working well. The process was quite drawn out: questionnaires sent to hundreds of medical schools, public health schools, and hospitals; six focus groups with an iterative process for evaluating recommendations; and a modified Delphi consensus process among 17 experts, including (of course) representative patients.

Respondents to the survey expressed strong support for patient-centered research, believing (at a rate of about 90%) that it would benefit patients and clinicians, as well as (at a rate of about 80%) researchers. Those IRBs who tried out patient-centered research were especially enthusiastic, likely to say that it improved the quality of research results.

But IRB heads also openly expressed confusion and frustration about the pressure to include patients in the “non-traditional” roles listed earlier. Some of their reactions were productive: for instance, large majorities of respondents called on the federal government to provide standards, guidelines, and training for patient engagement. But some of the immediate measures IRBs put in place were irrelevant and even counterproductive. For instance, some required patients to sign informed consent forms, even though these patients were not the subjects of trials and therefore had no reason to need to consent. As patient advocate Jane Perlmutter pointed out, patients in non-traditional roles don’t require protection but require training to ensure that they protect the subjects of the research.

Perlmutter emphasized the importance of financial compensation. Without it, researchers will recruit mostly unemployed patients with independent incomes. To reach out to multiple ethnic groups, age ranges, and economic strata, payment must be offered for the work performed.

Unfortunately, I didn’t see much at Friday’s conference about topics directly related to health IT, such as privacy and ownership of data. Researcher Luke Gelinas mentioned that patient-centered research is more likely to use sensors, networking, social media, and other modern technology than more traditional research, and that these raise issues of informed consent, privacy, and ownership of data.

On the whole, the Petrie-Flom researchers thought there was no need for a whole new approach. But they are working on several recommendations to improve the current situation. In summary, the takeaways I derived from the symposium include:

  • The value of patient-centered research is widely appreciated, and its benefits have been demonstrated where it has been tried.
  • However, progress implementing patient-centered research is slow.
  • Training for patients in non-traditional roles is required, but not so much as to be daunting and make it difficult to participate.
  • Researchers have not devoted enough effort to diversity.
  • Governments can offer support in typical ways, such as setting standards and funding programs.

I also predict that the growth of patient-centered research will place additional strains on IT systems. Bringing in new team members in scattered environments will require multiple systems to interact without friction. Data will need to be segmented and released carefully to just the right people. Interfaces will have to be intuitive (if such a thing exists) and easy to use without much training and without risk of errors. So the field has its work cut out.

How the Young Unity Health Score Company Handles The Dilemmas of Health IT Adoption

Posted on June 25, 2018 I Written By

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

I have been talking to a young company called Unity Health Score with big plans for improving the collection and sharing of data on patients. Their 55-page business plans covers the recruitment of individuals to share health data, the storage of that data, and services to researchers, clinicians, and insurers. Along the way, Unity Health Score tussles with many problems presented by patient data.
Unity Health Score logo
The goals articulated for this company by founder Austin Jones include getting better data to researchers and insurers so they can reduce costs and find cures, improving communications and thus care coordination among clinicians and patients, and putting patients in control of their health data so they can decide where it goes. The multi-faceted business plan covers:

  • Getting permission from patients to store data in a cloud service maintained by Unity Health Score
  • Running data by the patients’ doctors to ensure accuracy
  • Giving patients control over what researchers or other data users receive their data, in exchange for monetary rewards
  • Earning revenue for the company and the patients by selling data to researchers and insurers
  • Helping insurers adjust their plans based on analysis of incoming data

The data collected is not limited to payment data or even clinical data, but could include a grab-bag of personal data, such financial and lifestyle information. All this might yield health benefits to analytics–after all, the strategy of using powerful modern deep learning is being pursued by many other health care entities. At the same time, Jones plans to ensure might higher quality data than traditional data brokers such as Acxiom.

Now let’s see what Unity Health Score has to overcome to meet its goals. These challenges are by no means unique to these energetic entrepreneurs–they define the barriers faced by institutions throughout health care, from the smallest start-up to the Centers for Medicare & Medicaid Services.

Outreach to achieve a critical mass of patients
We can talk for weeks about quality of care and modernizing cures, but everybody who works in medicine agrees that the key problem we face is indifference. Most people don’t want to think too much about their health, are apathetic when presented with options, and stubbornly resist the simplist interventions–even taking their prescribed medication. So explaining the long-term benefits of uploading data and approving its use will be an uphill journey.

Many app developers seek adoption by major institutions, such as large insurers, hospital conglomerates, and HMOs like Kaiser. This is the smoothest path toward adoption by large numbers of consumers, and Unity Health Score includes a similar plan in its business model, According to Jones, they will require the insurance company to reduce premiums based on each patient’s health score. In return, they should be able to use the data collected to save money.

Protecting patient data
Health data is probably the most sensitive information most of us produce over our lifetimes. Financial information is important to keep safe, but you can change your bank account or credit card if your financial information is leaked–you can’t change your medical history. Security and privacy guarantees are therefore crucial for patient records. Indeed, the Unity Health Score business plan cites fears of privacy as a key risk.

Although some researchers have tried distributed patient records, stored in some repository chosen by each individual, Unith Health Score opts for central storage, like most current personal health records. This not only requires great care to secure, but places on them the burden of persuading patients that the data really will be used only for purposes chosen by the patients. Too many apps and institutions play three-card Monte with privacy policies, slipping in unauthorized uses (just think back to the recent Facebook/Cambridge Analytica scandal), so Internet users have become hypervigilant.

Unity Health Score also has to sign up physicians to check data for accuracy. This, of course, should be the priority for any data entered into any medical record. Because doctors’ time is going more and more toward the frustrating task of data entry, the company offers an enticing trade-off: the patients takes the time to enter their data, and the doctor merely verifies its accuracy. Furthermore, a consolidated medical record online can be used to speed check-in times on visits and to make data sharing on mobile devices easier.

Making the data useful
Once the patients and clinicians join Unity Health Score, the company has to follow through on its promise. This is a challenge with multiple stages.

First, much of the data will be in unstructured doctors’ notes. Jones plans to use OCR, like many other health data aggregators, to extract useful information from the notes. OCR and natural language processing may indeed be more accurate than relying on doctors to meticulously fill out dozens of structured fields in a database. But there is always room for missed diagnoses or allergies, and even for misinterpretations.

Next, data sources must be harmonized. They are likely to use different units and different lexicons. Although many parts of the medical industry are trying to standardize their codings, progress is incomplete.

The notion of a single number defining one’s health is appealing, but it might be too crude for many uses. Whether you’re making actuarial predictions (when will the individual die, or have to stop working?), estimating future health care costs, or guessing where to allocate public health resources, details about conditions may be more important than an all-encompassing number. However, many purchasers of the Unity Health Score information may still find the simplicity of a single integer useful.

Making the service attractive to data purchasers
The business plan points out that most rsearch depends on large data sets. During the company’s ramp-up phase–which could take years–they just won’t have enough patients suffering from a particular condition to interest many researchers, such as pharma companies looking for subjects. However, the company can start by selling data to academic researchers, who often can accomplish a lot with a relatively small sample. Biotech, pharma, and agencies can sign up later.

Clinicians may warm to the service much more quickly. They will appreciate having easy access to patient data for emergency room visits and care coordination in general. However, this is a very common use case for patient data, and one where many competing services are vying for a business niche.

Aligning goals of stakeholders
In some ways I have saved the hardest dilemma for last. Unity Health Care is trying to tie together many sets of stakeholders–patients, doctors, marketers, researchers, insurers–and between many of these stakeholders there are irreconcilable conflicts.

For instance, insurers will want the health score to adjust their clients’ payments, charging more for sick people. This will be feared and resented by people with pre-existing conditions, who will therefore withhold their information. In some cases, such insurer practices will worsen existing disparities for the poor and underpriviledged. The Unity Health Score business plan rejects redlining, but there may be subtler practices that many observers would consider unethical. Sometimes, incentives can also be counterproductive.

Also, as the business plan points out, many companies that currently purchase health data have goals that run counter to good health: they want to sell doctors or patients products that don’t actually help, and that run up health care costs. Some purchasers are even data thieves. Unity Health Score has a superior business model here to other data brokers, because it lets the patients approve each distribution of their data. But doing so greatly narrows the range of purchasers. Hopefully, there will be enough ethical health data users to support Unity Health Score!

This is an intriguing company with a sophisticated strategy–but one with obstacles to overcome. We can all learn from the challenges they face, because many others who want to succeed in the field of health care reform will come up against those challenges.

Designing for the Whole Patient Journey: Lumeon Enters the US Health Provider Market

Posted on April 23, 2018 I Written By

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

Lots of companies strive to unshackle health IT’s potential to make the health care industry more engaging, more adaptable, and more efficient. Lumeon intrigues me in this space because they have a holistic approach that seems to be producing good results in the UK and Europe–and recently they have entered the US market.

Superficially, the elements of the Lumeon platform echo advances made by many other health IT applications. Alerts and reminders? Check. Workflow automation? Check. Integration with a variety of EHRs? Of course! But there is something more to Lumeon’s approach to design that makes it a significant player. I had the opportunity to talk to Andrew Wyatt, Chief Operating Officer, to hear what he felt were Lumeon’s unique strengths.

Before discussing the platform itself, we have to understand Lumeon’s devotion to understanding the patient’s end-to-end experience, also sometimes known as the patient journey. Lumeon is not so idealistic as to ask providers to consider a patient’s needs from womb to tomb–although that would certainly help. But they ask such questions as: can the patient physically get to appointments? Can she navigate her apartment building’s stairs and her apartment after discharge from surgery? Can she get her medication?

Lumeon workflow view

*Lumeon workflow view

Such questions are the beginning of good user experience design (UX), and are critical to successful treatment. This is why I covered the HxRefactored conference in Boston in 2016 and 2017. Such questions were central to the conference.

It’s also intriguing that criminal justice reformers focus attention on the whole sequence of punishment and rehabilitation, including reentry into mainstream society.

Thinking about every step of the patient experience, before and after treatments as well as when she enters the office, is called a longitudinal view. Even in countries with national health care systems, less than half the institutions take such a view, and adoption of the view is growing only slowly.

Another trait of longitudinal thinking Wyatt looks for is coordinated care with strong involvement from the family. The main problem he ascribed to current health IT systems is that they serve the clinician. (I think many doctors would dispute this, saying that the systems serve only administrators and payers–not the clinician or the patient.)

Here are a couple success stories from Wyatt. After summarizing them, I’ll look at the platform that made them possible.

Alliance Medical, a major provider of MRI scans and other imaging services, used Lumeon to streamline the entire patient journey, from initial referral to delivery of final image and report. For instance, an online form asks patients during the intake process whether the patient has metal in his body, which would indicate the use of an alternative test instead of an MRI. The next question then becomes what test would meet the current diagnostic needs and be reimbursed by the payer. Lumeon automates these logistical tasks. After the test, automation provided by the Lumeon platform can make sure that a clinician reviews the image within the required time and that the image gets to the people who need it.

Another large provider in ophthalmology looked for a way to improve efficiency and outcomes in the common disease of glaucoma, by putting images of the eye in a cloud and providing a preliminary, automated diagnosis that the doctor would check. None of the cloud and telemedicine solutions covered ophthalmology, so the practice used the Lumeon platform to create one. The design process functioned as a discipline allowing them to put a robust process for processing patients in place, leading to better outcomes. From the patient’s point of view, the change was even more dramatic: they could come in to the office just once instead of four times to get their diagnosis.

An imaging provider found that they wasted 5 to 10 minutes each time they moved a machine between an upper body position and a lower body position. They saved many hours–and therefore millions of dollars–simply by scheduling all the upper body scans for one part of the day and all lower body scans for another. Lumeon made this planning possible.

In most of the US, value-based care is still in its infancy. The longitudinal view is not found widely in health care. But Wyatt says his service can help businesses stuck in the fee-for-service model too. For example, one surgical practice suffered lots of delays and cancellations because the necessary paperwork wasn’t complete the day before surgery. Lumeon helped them build a system that knew what tests were needed before each surgery and that prompted staff to get them done on time. The system required coordination of many physicians and labs.

Another example of a solution that is valuable in fee-for-service contexts is creating a reminder for calling colonoscopy patients when they need to repeat the procedure. Each patient has to be called at a different time interval, which can be years in the future.

Lumeon has been in business 12 years and serves about 60 providers in the UK and Europe, some very large. They provide the service on a SaaS basis, running on a HIPAA-compliant AWS cloud except in the UK, where they run their own data center in order to interact with legacy National Health Service systems.

The company has encountered along the way an enormous range of health care disciplines, with organizations ranging from small to huge in size, and some needing only a simple alerting service while others re-imagined the whole patient journey. Wyatt says that their design process helps the care provider articulate the care pathway they want to support and then automate it. Certainly, a powerful and flexible platform is needed to support so many services. As Wyatt said, “Health care is not linear.” He describes three key parts to the Lumeon system:

  1. Integration engine. This is what allows them to interact with the EHR, as well as with other IT systems such as Salesforce. Often, the unique workflow system developed by Lumeon for the site can pop up inside the EHR interface, which is important because doctors hate to exit a workflow and start up another.

    Any new system they encounter–for instance, some institutions have unique IT systems they created in-house–can be plugged in by developing a driver for it. Wyatt made this seem like a small job, which underscores that a lack of data exchange among hospitals is due to business and organizational factors, not technical EHR problems. Web services and a growing support for FHIR make integration easier

  2. Communications. Like the integration engine, this has a common substrate and a multiplicity of interfaces so doctors, patients, and all those involved in the health care journey can use text, email, web forms, and mobile apps as they choose.

  3. Workflow or content engine. Once they learn the system, clinicians can develop pathways without going back to Lumeon for support. The body scan solution mentioned earlier is an example of a solution designed and implemented entirely by the clinical service on its own.

  4. Transparency is another benefit of a good workflow design. In most environments, staff must remember complex sequences of events that vary from patient to patient (ordering labs, making referrals, etc.). The sequence is usually opaque to the patient herself. A typical Lumeon design will show the milestones in a visual form so everybody knows what steps took place and what remain to be done.

Wyatt describes Lumeon as a big step beyond most current workflow and messaging solutions. It will be interesting to watch the company’s growth, and to see which of its traits are adopted by other health IT firms.

Thoughts on Privacy in Health Care in the Wake of Facebook Scrutiny

Posted on April 13, 2018 I Written By

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

A lot of health IT experts are taking a fresh look at the field’s (abysmal) record in protecting patient data, following the shocking Cambridge Analytica revelations that cast a new and disturbing light on privacy practices in the computer field. Both Facebook and others in the computer field who would love to emulate its financial success are trying to look at general lessons that go beyond the oddities of the Cambridge Analytica mess. (Among other things, the mess involved a loose Facebook sharing policy that was tightened up a couple years ago, and a purported “academic researcher” who apparently violated Facebook’s terms of service.)

I will devote this article to four lessons from the Facebook scandal that apply especially to health care data–or more correctly, four ways in which Cambridge Analytica reinforces principles that privacy advocates have known for years. Everybody recognizes that the risks modern data sharing practices pose to public life are hard, even intractable, and I will have to content myself with helping to define the issues, not present solutions. The lessons are:

  • There is no such thing as health data.

  • Consent is a meaningless concept.

  • The risks of disclosure go beyond individuals to affect the whole population.

  • Discrimination doesn’t have to be explicit or conscious.

The article will now lay out each concept, how the Facebook events reinforce it, and what it means for health care.

There is no such thing as health data

To be more precise, I should say that there is no hard-and-fast distinction between health data, financial data, voting data, consumer data, or any other category you choose to define. Health care providers are enjoined by HIPAA and other laws to fiercely protect information about diagnoses, medications, and other aspects of their patients’ lives. But a Facebook posting or a receipt from the supermarket can disclose that a person has a certain condition. The compute-intensive analytics that data brokers, marketers, and insurers apply with ever-growing sophistication are aimed at revealing these things. If the greatest impact on your life is that a pop-up ad for some product appears on your browser, count yourself lucky. You don’t know what else someone is doing with the information.

I feel a bit of sympathy for Facebook’s management, because few people anticipated that routine postings could identify ripe targets for fake news and inflammatory political messaging (except for the brilliant operatives who did that messaging). On the other hand, neither Facebook nor the US government acted fast enough to shut down the behavior and tell the public about it, once it was discovered.

HIPAA itself is notoriously limited. If someone can escape being classified as a health care provider or a provider’s business associate, they can collect data with abandon and do whatever they like (except in places such as the European Union, where laws hopefully require them to use the data for the purpose they cited while collecting it). App developers consciously strive to define their products in such a way that they sidestep the dreaded HIPAA coverage. (I won’t even go into the weaknesses of HIPAA and subsequent laws, which fail to take modern data analysis into account.)

Consent is a meaningless concept

Even the European Union’s new regulations (the much-publicized General Data Protection Regulation or GDPR) allows data collection to proceed after user consent. Of course, data must be collected for many purposes, such as payment and shipping at retail web sites. And the GDPR–following a long-established principle of consumer rights–requires further consent if the site collecting the data wants to use it beyond its original purpose. But it’s hard to imagine what use data will be put to, especially a couple years in the future.

Privacy advocates have known from the beginning of the ubiquitous “terms of service” that few people read before the press the Accept button. And this is a rational ignorance. Even if you read the tiresome and legalistic terms of service (I always do), you are unlikely to understand their implications. So the problem lies deeper than tedious verbiage: even the most sophisticated user cannot predict what’s going to happen to the data she consented to share.

The health care field has advanced farther than most by installing legal and regulatory barriers to sharing. We could do even better by storing all health data in a Personal Health Record (PHR) for each individual instead of at the various doctors, pharmacies, and other institutions where it can be used for dubious purposes. But all use requires consent, and consent is always on shaky grounds. There is also a risk (although I think it is exaggerated) that patients can be re-identified from de-identified data. But both data sharing and the uses of data must be more strictly regulated.

The risks of disclosure go beyond individuals to affect the whole population

The illusion that an individual can offer informed consent is matched by an even more dangerous illusion that the harm caused by a breach is limited to the individual affected, or even to his family. In fact, data collected legally and pervasively is used daily to make decisions about demographic groups, as I explained back in 1998. Democracy itself took a bullet when Russian political agents used data to influence the British EU referendum and the US presidential election.

Thus, privacy is not the concern of individuals making supposedly rational decisions about how much to protect their own data. It is a social issue, requiring a coordinated regulatory response.

Discrimination doesn’t have to be explicit or conscious

We have seen that data can be used to draw virtual red lines around entire groups of people. Data analytics, unless strictly monitored, reproduce society’s prejudices in software. This has a particular meaning in health care.

Discrimination against many demographic groups (African-Americans, immigrants, LGBTQ people) has been repeatedly documented. Very few doctors would consciously aver that they wish people harm in these groups, or even that they dismiss their concerns. Yet it happens over and over. The same unconscious or systemic discrimination will affect analytics and the application of its findings in health care.

A final dilemma

Much has been made of Facebook’s policy of collecting data about “friends of friends,” which draws a wide circle around the person giving consent and infringes on the privacy of people who never consented. Facebook did end the practice that allowed Global Science Research to collect data on an estimated 87 million people. But the dilemma behind the “friends of friends” policy is how inextricably it embodies the premise behind social media.

Lots of people like to condemn today’s web sites (not just social media, but news sites and many others–even health sites) for collecting data for marketing purposes. But as I understand it, the “friends of friends” phenomenon lies deeper. Finding connections and building weak networks out of extended relationships is the underpinning of social networking. It’s not just how networks such as Facebook can display to you the names of people they think you should connect with. It underlies everything about bringing you in contact with information about people you care about, or might care about. Take away “friends of friends” and you take away social networking, which has been the most powerful force for connecting people around mutual interests the world has ever developed.

The health care field is currently struggling with a similar demonic trade-off. We desperately hope to cut costs and tame chronic illness through data collection. The more data we scoop up and the more zealously we subject it to analysis, the more we can draw useful conclusions that create better care. But bad actors can use the same techniques to deny insurance, withhold needed care, or exploit trusting patients and sell them bogus treatments. The ethics of data analysis and data sharing in health care require an open, and open-eyed, debate before we go further.

Hopes for Big Impact from Validic: Making Use of Consumer Device Data

Posted on March 20, 2018 I Written By

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

Validic, a company that provides solutions in data connectivity to health care organizations, came to HIMMS this year with a new platform called Impact that takes a big step toward turning raw data into actionable alerts. I talked to Brian Carter, senior vice president of product at Validic, about the key contributions of Impact.

Routinely, I find companies that allow health-related monitoring in the home. Each one has a solution it’s marketing to doctors: a solution reminding patients to take their meds, monitoring vital signs for diabetes, monitoring vital signs for congestive heart failure, or something else fairly specific. These are usually integrated solutions that provide their own devices. The achievement of Validic, built through years of painstakingly learning the details of almost 400 different devices and how to extract their data, is to give the provider control over which device to use. Now a provider can contract with some application developer to create a monitoring solution for diabetes or whatever the provider is tracking, and then choose a device based on cost, quality, and suitability.

Validic’s Impact platform actually does many of the things that a third-party monitoring solution can do. But rather than trying to become a full solutions provider for such things as hospital readmissions, Validic augments existing care management systems by integrating its platform directly into the clinical workflow. With Impact, clinicians can draw conclusions directly from the data they collect to generate intelligent alerts.

For instance, a doctor can request that Impact sample data from a sensor at certain intervals and define a threshold (such as blood sugar levels) at which Impact contacts the doctor. Carter defines this service more as descriptive analytics than predictive analytics. However, Validic plans to increase the sophistication of its analysis to move more toward predictive analytics. Thus, they hope in the future not just to report when blood sugar hits a dangerous threshold, but to analyze a patient’s data over time and compare it to other patients to predict if and when his blood sugar will rise. They also hope to track the all too common tendency to abandon the use of consumer devices, and predict when a patient is likely to do so, allowing the doctor to intervene and offer encouragement to keep using the device.

Validic has evolved far beyond its original mission of connecting devices to health care providers and wellness organizations. This mission is still important, because device manufacturers are slow to adopt standards that would make such connections trivial to implement. Most devices still offer proprietary APIs, and even if they all settled on something such as FHIR, Carter says that the task of connecting each device would still require manual programming effort. “Instead of setting up connections to ten different devices, a hospital can connect to Validic once and get access to all ten.”

However, interconnection is slowly progressing, so Validic needs to move up the value chain. Furthermore, clinicians are slow to use the valuable information that devices in the home can offer, because they produce a flood of data that is hard to interpret. With Impact, they can derive some immediate benefit from device data, as the critical information is elevated above the noise while still being integrated into their health records. They can contract further with other application developers to run analytical services and integrate with their health records.

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

Posted on March 15, 2018 I Written By

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Small Grounds for Celebration and Many Lurking Risks in HIMSS Survey

Posted on March 12, 2018 I Written By

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

When trying to bypass the breathless enthusiasm of press releases and determine where health IT is really headed, we can benefit from a recent HIMMS survey, released around the time of their main annual conference. They managed to get responses from 224 managers of health care facilities–which range from hospitals and clinics to nursing homes–and 145 high-tech developers that fall into the large categories of “vendors” and “consultants.” What we learn is that vendors are preparing for major advances in health IT, but that clinicians are less ready for them.

On the positive side, both the clinicians and the vendors assign fairly high priority to data analytics and to human factors and design (page 7). In fact, data analytics have come to be much more appreciated by clinicians in the past year (page 9). This may reflect the astonishing successes of deep learning artificial intelligence reported recently in the general press, and herald a willingness to invest in these technologies to improve health care. As for human factors and design, the importance of these disciplines has been repeatedly shown in HxRefactored conferences.

Genomics ranks fairly low for both sides, which I think is reasonable given that there are still relatively few insights we can gain from genetics to change our treatments. Numerous studies have turned up disappointing results: genetic testing doesn’t work very well yet, and tends to lead only to temporary improvements. In fact, both clinicians and vendors show a big drop in interest in precision medicine and genetics (pages 9 and 10). The drop in precision medicine, in particular, may be related to the strong association the term has with Vice President Joe Biden in the previous administration, although NIH seems to still be committed to it. Everybody knows that these research efforts will sprout big payoffs someday–but probably not soon enough for the business models of most companies.

But much more of the HIMSS report is given over to disturbing perception gaps between the clinicians and vendors. For instance, clinicians hold patient safety in higher regard than vendors (page 7). I view this concern cynically. Privacy and safety have often been invoked to hold back data exchange. I cannot believe that vendors in the health care space treat patient safety or privacy carelessly. I think it more likely that clinicians are using it as a shield to hide their refusal to try valuable new technologies.

In turn, vendors are much more interested in data exchange and integration than clinicians (page 7). This may just reflect a different level of appreciation for the effects of technology on outcomes. That is, data exchange and integration may be complex and abstract concepts, so perhaps the vendors are in a better position to understand that it ultimately determines whether a patient gets the treatment her condition demands. But really, how difficult can it be to be to understand data exchange? It seems like the clinicians are undermining the path to better care through coordination.

I have trouble explaining the big drops in interest in care coordination and public health (pages 9 and 10), which is worrisome because these things will probably do more than anything to produce healthier populations. The problem, I think, is probably that there’s no reimbursement for taking on these big, hairy problems. HIMMS explains the drop as a shift of attention to data analytics, which should ultimately help achieve the broader goals (page 11).

HIMSS found that clinicians expect to decrease their investments in health IT over the upcoming year, or at least to keep the amount steady (page 14). I suspect this is because they realize they’ve been soaked by suppliers and vendors. Since Meaningful Use was instituted in 2009, clinicians have poured billions of dollars and countless staff time into new EHRs, reaping mostly revenue-threatening costs and physician burn-out. However, as HIMSS points out, vendors expect clinicians to increase their investments in health IT–and may be sorely disappointed, especially as they enter a robust hiring phase (page 15).

Reading the report, I come away feeling that the future of health care may be bright–but that the glow you see comes from far over the horizon.

Patient Access to Health Data: The AHA Doesn’t Really Want to Know

Posted on March 8, 2018 I Written By

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

As Spring holds off a bit longer this March in New England, it’s certainly pleasant to read a sunny assessment of patient access to records, based on a survey by the American Hospital Association. Clearly, a lot of progress has been made toward the requirement that doctors have been on the hook for during the past decade: giving patients access to their own health data. We can also go online to accomplish many of the same tasks with our doctors as we’re used to doing with restaurants, banks, or auto repair shops. But the researchers did not dig very deep. This report may stand as a model for how to cover up problems by asking superficial questions.

I don’t want to denigrate a leap from 27% to 93%, over a four to five year period, in the hospitals who provide patients with their health data through portals. Even more impressive is the leap in the number of hospitals who provide data to patient caregivers (from zero to 83%). In this case, a “caregiver” appears to be a family member or other non-professional advocate, not a member of a health team–a crucial distinction I’ll return to later.

I’m disappointed that only 50% of health systems allow patients to reorder prescriptions online, but that’s still a big improvement over 22% in 2012. A smaller increase (from 55% to 68%) is seen in the number of providers who allow patients to send secure online messages, a recalcitrance that we might guess is related to the lack of reimbursement for time spent reading messages.

That gives you a flavor of the types of questions answered by the survey–you can easily read all four pages for yourself. The report ends with four questions about promoting more patient engagement through IT. The questions stay at the same superficial level as the rest of the report, however. My questions would probe a little more uncomfortably. These questions are:

  • How much of the record is available to the patient?
  • How speedily is it provided?
  • Is it in standard formats and units?
  • Does it facilitate a team approach?

The rest of this article looks at why I’d like to ask providers these questions.

How much of the record is available to the patient?

I base this question on personal interactions with my primary care physician. A few years ago he installed a patient portal based on the eClinicalWorks electronic health record system used at the hospital with which he is affiliated. When I pointed out that it contained hardly any information, he admitted that the practice had contracted with a consultant who charges a significant fee for every field of the record exposed to patients. The portal didn’t even show my diagnoses.

Recently the affiliated hospital (and therefore my PCP) joined the industry rush to Epic, and I ended up with Epic’s hugely ballyhooed MyChart portal. It is much richer than the old one. For a while, it had a bug in the prescription ordering process that would take too long to describe here–an interesting case study in computer-driven disambiguation. My online chart shows a lot of key facts, such as diagnoses, allergies, and medications. But it lacks much more than it has. For instance:

  • There are none of the crucial lab notes my doctors have diligently typed into my record over multiple visits.
  • It doesn’t indicate my surgical history, because the surgeries I’ve had took place before I joined the current practice.
  • Its immunization record doesn’t show childhood immunizations, or long-lasting shots I got in order to travel to Brazil many years ago.

Clearly, this record would be useless for serious medical interventions. A doctor treating me in an emergency room wouldn’t know a childhood injury I got, or might think I was suffering from a tropical disease against which I got an inoculation. She wouldn’t know about questions I asked over the years, or whether and why the doctor told me not to worry about those things. My doctor and his Epic-embracing hospital are still hoarding the data needed for my treatment.

How speedily is it provided?

Timeliness matters. My lab results are shown quickly in MyChart, and it seems like other updates take place expeditiously. But I want to hear whether other practices can provide information fast enough for patients and caregivers to take useful steps, and show relevant facts to specialists they visit.

Is it in standard formats and units?

Although high-level exchange is getting better with the adoption of the FHIR specification, many EHRs still refuse to conform to existing standards. A 2016 survey from Minnesota says, “Most clinics do not incorporate electronic information from other providers into their EHRs as standardized data. Only 31 percent of clinics integrated data in standardized format for immunization, 25 percent for medication history, 19 percent for lab results, and just 12 percent for summary-of-care records.”

The paragraph goes on to say, “The vast majority said they fax/scan/PDF the data to and from outside sources.” So FHIR may lead to a quick improvement in those shockingly low percentages.

Labs also fail to cooperate in using standards.

Does it facilitate a team approach?

This is really the bottom line, isn’t it–what we’re all aiming at? We want the PCP, the specialist, the visiting nurse, the physical therapist and occupational therapist, the rehab facility staff, and every random caregiver who comes along to work hand-in-latex-glove as a team. The previous sections of this article indicate that the patient portal doesn’t foster such collaboration. Will the American Hospital Association be able to tell me it does? And if not, when will they get to the position where they can care collaboratively for our needy populations?

A Learning EHR for a Learning Healthcare System

Posted on January 24, 2018 I Written By

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

Can the health care system survive the adoption of electronic health records? When the HITECH act mandated the installation of EHRs in 2009, we all hoped they would propel hospitals and clinics into a 21st-century consciousness. Instead, EHRs threaten to destroy those who have adopted them: the doctors whose work environment they degrade and the hospitals that they are pushing into bankruptcy. But the revolution in artificial intelligence that’s injecting new insights into many industries could also create radically different EHRs.

Here I define AI as software that, instead of dictating what a computer system should do, undergoes a process of experimentation and observation that creates a model to control the system, hopefully with far greater sophistication, personalization, and adaptability. Breakthroughs achieved in AI over the past decade now enable things that seemed impossible a bit earlier, such as voice interfaces that can both recognize and produce speech.

AI has famously been used by IBM Watson to make treatment recommendations. Analyses of big data (which may or may not qualify as AI) have saved hospitals large sums of money and even–finally, what we’ve been waiting for!–make patients healthier. But I’m talking in this article about a particular focus: the potential for changing the much-derided EHR. As many observers have pointed out, current EHRs are mostly billion-dollar file cabinets in electronic form. That epithet doesn’t even characterize them well enough–imagine instead a file cabinet that repeatedly screamed at you to check what you’re doing as you thumb through the papers.

How can AI create a new electronic health record? Major vendors have announced virtual assistants (See also John’s recent interview with MEDITECH which mentions their interest in virtual assistants) to make their interfaces more intuitive and responsive, so there is hope that they’re watching other industries and learning from machine learning. I don’t know what the vendors basing these assistants on, but in this article I’ll describe how some vanilla AI techniques could be applied to the EHR.

How a Learning EHR Would Work

An AI-based health record would start with the usual dashboard-like interface. Each record consists of hundreds of discrete pieces of data, such as age, latest blood pressure reading, a diagnosis of chronic heart failure, and even ZIP code and family status–important public health indicators. Each field of data would be called a feature in traditional AI. The goal is to find which combination of features–and their values, such as 75 for age–most accurately predict what a clinician does with the EHR. With each click or character typed, the AI model looks at all the features, discards the bulk of them that are not useful, and uses the rest to present the doctor with fields and information likely to be of value.

The EHR will probably learn that the forms pulled up by a doctor for a heart patient differ from those pulled up for a cancer patient. One case might focus on behavior, another on surgery and medication. Clinicians certainly behave differently in the hospital from how they behave in their home offices, or even how they behave in another hospital across town with different patient demographics. A learning EHR will discover and adapt to these differences, while also capitalizing on the commonalities in the doctor’s behavior across all settings, as well as how other doctors in the practice behave.

Clinicians like to say that every patient is different: well, with AI tracking behavior, the interface can adapt to every patient.

AI can also make use of messy and incomplete data, the well-known weaknesses of health care. But it’s crucial, to maximize predictive accuracy, for the AI system to have access to as many fields as possible. Privacy rules, however, dictate that certain fields be masked and others made fuzzy (for instance, specifying age as a range from 70 to 80 instead of precisely 75). Although AI can still make use of such data, it might be possible to provide more precise values through data sharing agreements strictly stipulating that the data be used only to improve the EHR–not for competitive strategizing, marketing, or other frowned-on exploitation.

A learning EHR would also be integrated with other innovations that increase available data and reduce labor–for instance, devices worn by patients to collect vital signs and exercise habits. This could free up doctors do less time collecting statistics and more time treating the patient.

Potential Impacts of AI-Based Records

What we hope for is interfaces that give the doctor just what she needs, when she needs it. A helpful interface includes autocompletion for data she enters (one feature of a mobile solution called Modernizing Medicine, which I profiled in an earlier article), clear and consistent displays, and prompts that are useful instead of distracting.

Abrupt and arbitrary changes to interfaces can be disorienting and create errors. So perhaps the EHR will keep the same basic interface but use cues such as changes in color or highlighted borders to suggest to the doctor what she should pay attention to. Or it could occasionally display a dialog box asking the clinician whether she would like the EHR to upgrade and streamline its interface based on its knowledge of her behavior. This intervention might be welcome because a learning EHR should be able to drastically reduce the number of alerts that interrupt the doctors’ work.

Doctors’ burdens should be reduced in other ways too. Current blind and dumb EHRs require doctors to enter the same information over and over, and even to resort to the dangerous practice of copy and paste. Naturally, observers who write about this problem take the burden off of the inflexible and poorly designed computer systems, and blame the doctors instead. But doing repetitive work for humans is the original purpose of computers, and what they’re best at doing. Better design will make dual entries (and inconsistent records) a thing of the past.

Liability

Current computer vendors disclaim responsibility for errors, leaving it up the busy doctor to verify that the system carried out the doctor’s intentions accurately. Unfortunately, it will be a long time (if ever) before AI-driven systems are accurate enough to give vendors the confidence to take on risk. However, AI systems have an advantage over conventional ones by assigning a confidence level to each decision they make. Therefore, they could show the doctor how much the system trusts itself, and a high degree of doubt could let the doctor know she should take a closer look.

One of the popular terms that have sprung up over the past decade to describe health care reform is the “learning healthcare system.” A learning system requires learning on every level and at every stage. Because nobody likes the designs of current EHRs, they should be happy to try a new EHR with a design based directly on their behavior.

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

Posted on January 4, 2018 I Written By

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Specific technologies

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

Lots of intriguing devices are being developed:

Remote monitoring and telehealth have also been in the news.

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

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

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

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

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

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

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

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