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Open Source Software and the Path to EHR Heaven (Part 2 of 2)

Posted on September 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.

The previous segment of this article explained the challenges faced by health care organizations and suggested two ways they could be solved through free and open source software. We’ll finish the exploration in this segment of the article.

Situational awareness would reduce alert fatigue and catch errors

Difficult EHR interfaces are probably the second most frustrating aspect of being a doctor today: the first prize goes to the EHR’s inability to understand and adapt to the clinician’s workflow and environment. This is why the workplace redounds with beeps and belches from EHRs all day, causing alert fatigue and drowning out truly serious notifications. Stupid EHRs have an even subtler and often overlooked effect: when regulators or administrators require data for quality or public health purposes, the EHR is often “upgraded” with an extra field that the doctor has to fill in manually, instead of doing what computers do best and automatically replicating data that is already in the record. When doctors complain about the time they waste in the EHR, they often blame the regulators or the interface instead of placing their finger on the true culprit, which is the lack of awareness in the EHR.

Open source can ease these problems in several ways. First, the customizability outlined in the first section of this article allows savvy users to adapt it to their situations. Second, the interoperability from the previous section makes it easier to feed in information from other parts of the hospital or patient environment, and to hook in analytics that make sense of that information.

Enhancements from outside sources could be plugged in

The modularity of open source makes it easier to offer open platforms. This could lead to marketplaces for EHR enhancements, a long-time goal of the open SMART standard. Certainly, there would have to be controls for the sake of safety: an administrator, for instance, could limit downloads to carefully vetted software packages.

At best, storage and interface in an EHR would be decoupled in separate modules. Experts at storage could optimize it to improve access time and develop new options, such as new types of filtering. At the same time, developers could suggest new interfaces so that users can have any type of dashboard, alerting system, data entry forms, or other access they want.

Bugs could be fixed expeditiously

Customers of proprietary software remain at the mercy of the vendors. I worked in one computer company that depended on a very subtle feature from our supplier that turned out not to work as advertised. Our niche market, real-time computing, needed that feature to achieve the performance we promised customers, but it turned out that no other company needed it. The supplier admitted the feature was broken but told us point-blank that they had no plans to fix it. Our product failed in the marketplace, for that reason along with others.

Other software users suffer because proprietary vendors shift their market focus or for other reasons–even going out of business.

Free and open source software never ossifies, so long as users want it. Anyone can hire a developer to fix a bug. Furthermore, the company fixing it usually feeds the fix back into the core project because they want it to be propagated to future versions of the software. Thus, the fixes are tested, hardened, and offered to all users.

What free and open source tools are available?

Numerous free and open source EHRs have been developed, and some are in widespread use. Most famously is VistA, the software created at the Department of Veterans Affairs, and used also by the Indian Health Service and other government agencies, has a community chaperone and has been adopted by the country of Jordan. VistA was considered by the Department of Defense as well, but ultimately rejected because the department didn’t want to invest in adding some missing features.

Another free software EHR, OpenMRS, supports health care in Kenya, Haiti, and elsewhere. OpenEMR is also deployed internationally.

What free and open source software has accomplished in these settings is just a hint of what it can do for health care across the board. The problem holding back open source is simple neglect: as VistA’s experience with the DoD showed, institutions are unwilling to support open source, even through they will pay 10 or 100 times as much on substandard proprietary software. Open Health Tools, covered in the article I just linked to, is one of several organizations that shriveled up and disappeared for lack of support. Some organizations gladly hop on for a free ride, using the software without contributing either funds or code. Others just ignore open source software, even though that means their own death: three hospitals have recently declared bankruptcy after installing proprietary EHRs. Although the article focuses on the up-front costs of installing the EHRs, I believe the real fatal blow was the inability of the EHRs to support efficient, streamlined health care services.

We need open source EHRs not just to reduce health care costs, but to transform health. But first, we need a vision of EHR heaven. I hope this article has taken us at least into the clouds.

Open Source Software and the Path to EHR Heaven (Part 1 of 2)

Posted on September 19, 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.

Do you feel your electronic health record (EHR) is heaven or hell? The vast majority of clinicians–and many patients, too, who interact with the EHR through a web portal–see it as the latter. In this article, I’ll describe an EHR heaven and how free and open source software can contribute to it. But first an old joke (which I have adapted slightly).

A salesman for an EHR vendor dies and goes before the Pearly Gates. Saint Peter asks him, “Would you like to go to heaven or hell?”

Surprised, the salesman says, “I didn’t know I had a choice.”

Saint Peter suggests, “How about this. We’ll show you heaven and hell, and then you can decide.”

“Sounds fair,” says the EHR salesman.

First they take him to heaven. People wearing white robes are strumming harps and singing hymns, and it goes on for a long time, till they take him away.

Next they take him to hell. And it’s really cool! People are clinking wine glasses together and chatting about amusing topics around the pool.

When the EHR salesman gets back to the Pearly Gates, he says to Saint Peter, “You know, this sounds really strange, but I choose hell.”

Immediately comes a clap of thunder. The salesman is in a fiery pit being prodded with pitchforks by dreadful demons.

“Wait!” he cries out. “This is not the hell I saw!”

One of the demons answers, “They must have shown you the demo.”

Most hospitals and clinicians are currently in EHR hell–one they have freely chosen, and one paid for partly by government Meaningful Use reimbursements. So we all know what EHR hell look like. What would EHR heaven be? And how does free and open source software enable it? The following sections of this article list the traits I think clinicians would like to see.

Interfaces could be easily replaced and customized

The greatest achievement of the open source movement, in my opinion, has been to strike an ideal balance between “let a hundred flowers bloom” experimentation and choosing the best option to advance the field. A healthy open source project encourages branching, which lets any individual or team with the required expertise change a product to their heart’s content. Users can then try out different versions, and a central committee vets the changes to decide which version is most robust.

Furthermore, modularization on various levels (programming modules, hooks, compile-time options, configuration tools) allows multiple versions to co-exist, each user choosing the options right for their environment. Open source software tends to be modular for several reasons, notably because it is developed by many different individuals and teams who want control over their small parts of the system.

With easy customization, a hospital or clinic can mandate that certain items be highlighted and that safe workflow rules be followed when entering or retrieving data. But the institution can also offer leeway for individual clinicians and patients to arrange a dashboard, color scheme, or other aspect of the environment to their liking.

Many of the enablers for this kind of agile, user-friendly programming are technical. Modularity is built into programming languages, while branching is standard in version control systems. So why can’t proprietary vendors do what open source communities routinely do? A few actually do, but most are constrained in ways that prevent such flexibility, especially in electronic health records:

  • Most vendors are dragging out the lifetime of nearly 40-year old technology, with brittle languages and tools that put insurmountable barriers in the way of agile work styles. They are also stuck with monolithic systems instead of modular ones.
  • The vendors’ business model depends on this monolithic control. To unbundle components, allow mix-and-match installations, and allow third parties to plug in new features would challenge the prices they charge.
  • The vendors are fundamentally unprepared for empowered users. They may vet features with clinically trained consultants and do market research, but handling power over the system to users is not in their DNA.

Data could be exchanged in a standard format without complex transformations

Data sharing is the lifeblood of modern computing; you can’t get much done on a single computer anymore. Data sharing lies behind new technologies ranging from the Internet of Things to real-time ad generation (the reason you’ll see a link to an article about “Fourteen celebrities who passed out drunk in public” when you’re trying to read a serious article about health IT). But it’s so rare in health care–where it’s uniquely known as “interoperability”–that every year, reformers call it the most critical goal for health IT, and the Office of the National Coordinator has repeatedly narrowed its Meaningful Use and related criteria to emphasize interoperability.

Open source software can share data with other systems as a matter of course. Data formats are simple, often text-based, and defined in the code in easy-to-find ways. Open source programmers, freed from the pressures on proprietary developers to reinvent wheels and set themselves apart from competitors, like to copy existing data formats. As a stark example of open source’s advantages, consider the most recent version of the Open Document Format, used by LibreOffice and other office suites. It defines an entire office suite in 104 pages. How big is the standards document for the Microsoft OOXML format, offering roughly equivalent functionality? Currently, 6,755 pages–and many observers say even that is incomplete. In short, open source is consistently the right choice for data exchange.

What would the adoption of open source do to improve health care, given that it would solve the interoperability problem? Records could be stored in the cloud–hopefully under patient control–and released to any facility treating the patient. Research would blossom, and researchers could share data as allowed by patients. Analytical services could be plugged in to produce new insights about disease and treatment from the records of millions of people. Perhaps interoperability could also contribute to solving the notorious patient matching problem–but that’s a complicated issue that I have discussed elsewhere, touching on privacy issues and user control outside the scope of this article.

The next segment of this article will list three more benefits of free and open source software, along with an assessment of its current and future prospects.

Lumeon Offers a Step Toward Usable Device Data in Health Care

Posted on August 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.

The health care field floats on oceans of patient data, but like the real oceans on our planet, patient data is polluted. Trying to ground evidence-based medicine on billing data is an exercise in frustration. Clinical data is hard to get access to, and has its own limitations. For instance, it is collected only when a patient visits the clinic or hospital. The FDA recently put 100 million dollars in its budget to get patient data from electronic health records (which the commissioner called “real-world experience”).

One of the paths toward better data for research and treatment lies in the data from medical devices: it’s plentiful, detailed, and accurate. But device data has mountains to climb before researchers and clinicians can use it: getting this data in the first place, normalizing and standardizing it, and integrating it with the systems used for analysis and treatment. That’s what excites me about a recent new direction taken by Lumeon, a platform for workflow management and treatment coordination in health care.

I covered Lumeon’s platform a few months ago. The company already lays out an enticing display of tools for clinicians, along with EHR integration. What’s new is the addition of medical devices, an enhancement that required nine months of working with medical device manufacturers. Recently I had another chance to talk to Rick Halton, Vice President of Marketing and Product for Lumeon.

Along with the measurements provided by devices, Lumeon has tools for patient engagement and the measurement of outcomes. These outcomes go beyond simple quantitative scores such as limb rotation. Lumeon creates for each patient a patient-specific functional score (PSFS). For one patient, it may be whether he can play outside with his kids. For another, it’s whether she can they go back to work, and for another, how far she can walk.

Lumeon asks, how can a device be used in a patient journey? It uses the routine information to help provide consistent care throughout this journey pathway, and measures outcomes throughout to generate feedback that promotes better long-term outcomes.

Device data is currently stored in a Lumeon platform that may be on the clinician’s site or in the cloud. Using an API, Lumeon’s output can be embedded within an EHR (they currently do this with Epic) so that the output can be displayed as part of the EHR display, and the clinician doesn’t even have to know that the results are being generated outside the EHR. In the future, the data may be integrated directly into the EHR. However, Lumeon’s direct customers are the providers, not the EHR vendors.

Data from devices was popular among providers at first for discharge planning and other narrow applications. Lumeon’s device integration is now getting more attention from providers who are experiencing a squeeze on reimbursements, a growing alertness among payers for outcomes, and a slow move in the industry toward fee-for-value. One leading device manufacturer is already using Lumeon for better treatment of cardiovascular care, bariatric surgery, and diabetes. Other applications include chronic disease, perioperative care (readiness for the OR and enhanced recovery), the digital patient experience on the web or in an app, and the patient centered medical home.

If Lumeon can turn device data into better treatment, other clinical institutions and health care platforms should be able to do so as well. It’s time for health care to enter the 21st century and use the Internet of Things (or Internet of Healthy Things, as termed by Dr. Joseph Kvedar) for the benefit of patients.

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.