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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.

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.

Scenarios for Health Care Reform (Part 2 of 2)

Posted on May 18, 2017 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 suggested two scenarios that could promote health care reform. We’ll finish off the scenarios in this part of the article.

Capitalism Disrupts Health Care

In the third scenario, reform is stimulated by an intrepid data science firm that takes on health care with greater success than most of its predecessors. After assembling an impressive analytics toolkit from open source software components–thus simplifying licensing–it approaches health care providers and offers them a deal they can’t refuse: analytics demonstrated to save them money and support their growth, all delivered for free. The data science firm asks in return only that they let it use deidentified data from their patients and practices to build an enhanced service that it will offer paying customers.

Some health care providers balk at the requirement to share data, but their legal and marketing teams explain that they have been doing it for years already with companies whose motives are less commendable. Increasingly, the providers are won over. The analytics service appeals particularly to small, rural, and safety-net providers. Hammered by payment cuts and growing needs among their populations, they are on the edge of going out of business and grasp the service as their last chance to stay in the black.

Participating in the program requires the extraction of data from electronic health records, and some EHR vendors try to stand in the way in order to protect their own monopoly on the data. Some even point to clauses in their licenses that prohibit the sharing. But they get a rude message in return: so valuable are the analytics that the providers are ready to jettison the vendors in a minute. The vendors ultimately go along and even compete on the basis of their ability to connect to the analytics.

Once stability and survival are established, the providers can use the analytics for more and more sophisticated benefits. Unlike the inadequate quality measures currently in use, the analytics provide a robust framework for assessing risk, stratifying populations, and determining how much a provider should be rewarded for treating each patient. Fee-for-outcome becomes standard.

Providers make deals to sign up patients for long-term relationships. Unlike the weak Medicare ACO model, which punishes a provider for things their patients do outside their relationship, the emerging system requires a commitment from the patient to stick with a provider. However, if the patient can demonstrate that she was neglected or failed to receive standard of care, she can switch to another provider and even require the misbehaving provider to cover costs. To hold up their end of this deal, providers find it necessary to reveal their practices and prices. Physician organizations develop quality-measurement platforms such as the recent PRIME registry in family medicine. A race to the top ensues.

What If Nothing Changes?

I’ll finish this upbeat article with a fourth scenario in which we muddle along as we have for years.

The ONC and Centers for Medicare & Medicaid Services continue to swat at waste in the health care system by pushing accountable care. But their ratings penalize safety-net providers, and payments fail to correlate with costs as hoped.

Fee-for-outcome flounders, so health care costs continue to rise to intolerable levels. Already, in Massachusetts, the US state that leads in universal health coverage, 40% of the state budget goes to Medicaid, where likely federal cuts will make it impossible to keep up coverage. Many other states and countries are witnessing the same pattern of rising costs.

The same pressures ride like a tidal wave through the rest of the health care system. Private insurers continue to withdraw from markets or lose money by staying. So either explicitly or through complex and inscrutable regulatory changes, the government allows insurers to cut sick people from their rolls and raise the cost burdens on patients and their employers. As patient rolls shrink, more hospitals close. Political rancor grows as the public watches employer money go into their health insurance instead of wages, and more of their own stagnant incomes go to health care costs, and government budgets tied up in health care instead of education and other social benefits.

Chronic diseases creep through the population, mocking crippled efforts at public health. Rampant obesity among children leads to more and earlier diabetes. Dementia also rises as the population ages, and climate change scatters its effects across all demographics.

Furthermore, when patients realize the costs they must take on to ask for health care, they delay doctor visits until their symptoms are unbearable. More people become disabled or perish, with negative impacts that spread through the economy. Output decline and more families become trapped in poverty. Self-medication for pain and mental illness becomes more popular, with predictable impacts on the opiate addiction crisis. Even our security is affected: the military finds it hard to recruit find healthy soldiers, and our foreign policy depends increasingly on drone strikes that kill civilians and inflame negative attitudes toward the US.

I think that, after considering this scenario, most of us would prefer one of the previous three I laid out in this article. If health care continues to be a major political issue for the next election, experts should try to direct discussion away from the current unproductive rhetoric toward advocacy for solutions. Some who read this article will hopefully feel impelled to apply themselves to one of the positive scenarios and bring it to fruition.

Scenarios for Health Care Reform (Part 1 of 2)

Posted on May 16, 2017 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.

All reformers in health care know what the field needs to do; I laid out four years ago the consensus about patient-supplied data, widespread analytics, mHealth, and transparency. Our frustration comes in when trying to crack the current hide-bound system open and create change. Recent interventions by US Republicans to repeal the Affordable Care Act, whatever their effects on costs and insurance coverage, offer no promise to affect workflows or treatment. So this article suggests three potential scenarios where reform could succeed, along with a vision of what will happen if none of them take hold.

Patients Forge Their Own Way Forward

In the first scenario, a tiny group of selfer-trackers, athletes, and empowered patients start a movement that ultimately wins over hundreds of millions of individuals.

These scattered enthusiasts, driven to overcome debilitating health problems or achieve extraordinary athletic feats, start to pursue self-tracking with fanaticism. Consumer or medical-grade devices provide them with ongoing data about their progress, and an open source platform such as HIE of One gives them a personal health record (PHR).

They also take charge of their interactions with the health care system. They find that most primary care providers aren’t interested in the data and concerns they bring, or don’t have time to process those data and concerns in the depth they need, or don’t know how to. Therefore, while preserving standard relationships with primary care providers and specialists where appropriate, the self-trackers seek out doctors and other providers to provide consultation about their personal health programs. A small number of providers recognize an opportunity here and set up practices around these consultations. The interactions look quite different from standard doctor visits. The customers, instead of just submitting themselves to examination and gathering advice, steer the conversation and set the goals.

Power relationships between doctors and customers also start to change. Although traditional patients can (and often do) walk away and effectively boycott a practice with which they’re not comfortable, the new customers use this power to set the agenda and to sort out the health care providers they find beneficial.

The turning point probably comes when someone–probabaly a research facility, because it puts customer needs above business models–invents a cheap, comfortable, and easy-to-use device that meets the basic needs for monitoring and transmitting vital signs. It may rest on the waist or some other place where it can be hidden, so that there is no stigma to wearing it constantly and no reason to reject its use on fashion grounds. A beneficent foundation invests several million dollars to make the device available to schoolchildren or some other needy population, and suddenly the community of empowered patients leaps from a miniscule pool to a mainstream phenomenon.

Researchers join the community in search of subjects for their experiments, and patients offer data to the researchers in the hope of speeding up cures. At all times, the data is under control of the subjects, who help to direct research based on their needs. Analytics start to turn up findings that inform clinical decision support.

I haven’t mentioned the collection of genetic information so far, because it requires more expensive processes, presents numerous privacy risks, and isn’t usually useful–normally it tells you that you have something like a 2% risk of getting a disease instead of the general population’s 1% risk. But where genetic testing is useful, it can definitely fit into this system.

Ultimately, the market for consultants that started out tiny becomes the dominant model for delivering health care. Specialists and hospitals are brought in only when their specific contributions are needed. The savings that result bring down insurance costs for everyone. And chronic disease goes way down as people get quick feedback on their lifestyle choices.

Government Puts Its Foot Down

After a decade of cajoling health care providers to share data and adopt a fee-for-outcome model, only to witness progress at a snail’s pace, the federal government decides to try a totally different tack in this second scenario. As part of the Precision Medicine initiative (which originally planned to sign up one million volunteers), and leveraging the ever-growing database of Medicare data, the Office of the National Coordinator sets up a consortium and runs analytics on top of its data to be shared with all legitimate researchers. The government also promises to share the benefits of the analytics with anyone in the world who adds their data to the database.

The goals of the analytics are multi-faceted, combining fraud checks, a search for cures, and everyday recommendations about improving interventions to save money and treat patients earlier in the disease cycle. The notorious 17-year gap between research findings and widespread implementation shrinks radically. Now, best practices are available to any patient who chooses to participate.

As with the personal health records in the previous scenario, the government database in this scenario creates a research platform of unprecedented size, both in the number of records and the variety of participating researchers.

To further expand the power of the analytics, the government demands exponentially greater transparency not just in medical settings but in all things that make us sick: the food we eat (reversing the rulings that protect manufacturers and restaurants from revealing what they’re putting in our bodies), the air and water that surrounds us, the effects of climate change (a major public health issue, spreading scourges such as mosquito-borne diseases and heat exhaustion), disparities in food and exercise options among neighborhoods, and more. Public awareness leads to improvements in health that lagged for decades.

In the next section of this article, I’ll present a third scenario that achieves reform from a different angle.

An Intelligent Interface for Patient Diagnosis by HealthTap

Posted on January 9, 2017 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.

HealthTap, an organization that’s hard to categorize, really should appear in more studies of modern health care. Analysts are agog over the size of the Veterans Administration’s clientele, and over a couple other major institutions such as Kaiser Permanente–but who is looking at the 104,000 physicians and the hundreds of millions of patients from 174 countries in HealthTap’s database?

HealthTap allows patients to connect with doctors online, and additionally hosts an enormous repository of doctors’ answers to health questions. In addition to its sheer size and its unique combination of services, HealthTap is ahead of most other health care institutions in its use of data.

I talked with founder and CEO Ron Gutman about a new service, Dr. AI, that triages the patient and guides her toward a treatment plan: online resources for small problems, doctors for major problems, and even a recommendation to head off to the emergency room when that is warranted. The service builds on the patient/doctor interactions HealthTap has offered over its six years of operation, but is fully automated.

Somewhat reminiscent of IBM’s Watson, Dr. AI evaluates the patient’s symptoms and searches a database for possible diagnoses. But the Dr. AI service differs from Watson in several key aspects:

  • Whereas Watson searches a huge collection of clinical research journals, HealthTap searches its own repository of doctor/patient interactions and advice given by its participating doctors. Thus, Dr. AI is more in line with modern “big data” analytics, such as PatientsLikeMe does.

  • More importantly, HealthTap potentially knows more about the patient than Watson does, because the patient can build up a history with HealthTap.

  • And most important, Dr. AI is interactive. Instead of doing a one-time search, it employs artificial intelligence techniques to generate questions. For instance, it may ask, “Did you take an airplane flight recently?” Each question arises from the totality of what HealthTap knows about the patient and the patterns found in HealthTap’s data.

The following video shows Dr. AI in action:

A well-stocked larder of artificial intelligence techniques feed Dr. AI’s interactive triage service: machine learning, natural language processing (because the doctor advice is stored in plain text), Bayesian learning, and pattern recognition. These allow a dialog tailored to each patient that is, to my knowledge, unique in the health care field.

HealthTap continues to grow as a platform for remote diagnosis and treatment. In a world with too few clinicians, it may become standard for people outside the traditional health care system.

Newly Released Open Source Libraries for Health Analytics from Health Catalyst

Posted on December 19, 2016 I Written By

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

I celebrate and try to report on each addition to the pool of open source resources for health care. Some, of course, are more significant than others, and I suspect the new healthcare.ai libraries released by the Health Catalyst organization will prove to be one of the significant offerings. One can do a search for health care software on sites such as GitHub and turn up thousands of hits (of which many are probably under open and free licenses), but for a company with the reputation and accomplishments of Health Catalyst to open up the tools it has been using internally gives healthcare.ai great legitimacy from the start.

According to Health Catalyst’s Director of Data Science Levi Thatcher, the main author of the project, these tools are tried and tested. Many of them are based on popular free software libraries in the general machine learning space: he mentions in particular the Python Scikit-learn library and the R language’s caret and and data.table libraries. The contribution of Health Catalyst is to build on these general tools to produce libraries tailored for the needs of health care facilities, with their unique populations, workflows, and billing needs. The company has used the libraries to deploy models related to operational, financial, and clinical questions. Eventually, Thatcher says, most of Health Catalyst’s applications will use predictive analytics based on healthcare.ai, and now other programmers can too.

Currently, Health Catalyst is providing libraries for R and Python. Moving them from internal projects to open source was not particularly difficult, according to Thatcher: the team mainly had to improve the documentation and broaden the range of usable data connections (ODBC and more). The packages can be installed in the manner common to free software projects in these language. The documentation includes guidelines for submitting changes, so that an ecosystem of developers can build up around the software. When I asked about RESTful APIs, Thatcher answered, “We do plan on using RESTful APIs in our work—mainly as a way of integrating these tools with ETL processes.”

I asked Thatcher one more general question: why did Health Catalyst open the tools? What benefit do they derive as a company by giving away their creative work? Thatcher answers, “We want to elevate the industry and educate it about what’s possible, because a rising tide will lift all boats. With more data publicly available each year, I’m excited to see what new and open clinical or socio-economic datasets are used to optimize decisions related to health.”

The Pain of Recording Patient Risk Factors as Illuminated by Apixio (Part 2 of 2)

Posted on October 28, 2016 I Written By

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

The previous section of this article introduced Apixio’s analytics for payers in the Medicare Advantage program. Now we’ll step through how Apixio extracts relevant diagnostic data.

The technology of PDF scraping
Providers usually submit SOAP notes to the Apixio web site in the form of PDFs. This comes to me as a surprise, after hearing about the extravagant efforts that have gone into new CCDs and other formats such as the Blue Button project launched by the VA. Normally provided in an XML format, these documents claim to adhere to standards and offer a relatively gentle face to a computer program. In contrast, a PDF is one of the most challenging formats to parse: words and other characters are reduced to graphical symbols, while layout bears little relation to the human meaning of the data.

Structured documents such as CCDs contain only about 20% of what CMS requires, and often are formatted in idiosyncratic ways so that even the best CCDs would be no more informative than a Word document or PDF. But the main barrier to getting information, according to Schneider, is that Medicare Advantage works through the payers, and providers can be reluctant to give payers direct access to their EHR data. This reluctance springs from a variety of reasons, including worries about security, the feeling of being deluged by requests from payers, and a belief that the providers’ IT infrastructure cannot handle the burden of data extraction. Their stance has nothing to do with protecting patient privacy, because HIPAA explicitly allows providers to share patient data for treatment, payment, and operations, and that is what they are doing giving sensitive data to Apixio in PDF form. Thus, Apixio had to master OCR and text processing to serve that market.

Processing a PDF requires several steps, integrated within Apixio’s platform:

  1. Optical character recognition to re-create the text from a photo of the PDF.

  2. Further structuring to recognize, for instance, when the PDF contains a table that needs to be broken up horizontally into columns, or constructs such the field name “Diagnosis” followed by the desired data.

  3. Natural language processing to find the grammatical patterns in the text. This processing naturally must understand medical terminology, common abbreviations such as CHF, and codings.

  4. Analytics that pull out the data relevant to risk and presents it in a usable format to a human coder.

Apixio can accept dozens of notes covering the patient’s history. It often turns up diagnoses that “fell through the cracks,” as Schneider puts it. The diagnostic information Apixio returns can be used by medical professionals to generate reports for Medicare, but it has other uses as well. Apixio tells providers when they are treating a patient for an illness that does not appear in their master database. Providers can use that information to deduce when patients are left out of key care programs that can help them. In this way, the information can improve patient care. One coder they followed could triple her rate of reviewing patient charts with Apixio’s service.

Caught between past and future
If the Apixio approach to culling risk factors appears round-about and overwrought, like bringing in a bulldozer to plant a rosebush, think back to the role of historical factors in health care. Given the ways doctors have been taught to record medical conditions, and available tools, Apixio does a small part in promoting the progressive role of accountable care.

Hopefully, changes to the health care field will permit more direct ways to deliver accountable care in the future. Medical schools will convey the requirements of accountable care to their students and teach them how to record data that satisfies these requirements. Technologies will make it easier to record risk factors the first time around. Quality measures and the data needed by policy-makers will be clarified. And most of all, the advantages of collaboration will lead providers and payers to form business agreements or even merge, at which point the EHR data will be opened to the payer. The contortions providers currently need to go through, in trying to achieve 21st-century quality, reminds us of where the field needs to go.

The Pain of Recording Patient Risk Factors as Illuminated by Apixio (Part 1 of 2)

Posted on October 27, 2016 I Written By

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

Many of us strain against the bonds of tradition in our workplace, harboring a secret dream that the industry could start afresh, streamlined and free of hampering traditions. But history weighs on nearly every field, including my own (publishing) and the one I cover in this blog (health care). Applying technology in such a field often involves the legerdemain of extracting new value from the imperfect records and processes with deep roots.

Along these lines, when Apixio aimed machine learning and data analytics at health care, they unveiled a business model based on measuring risk more accurately so that Medicare Advantage payments to health care payers and providers reflect their patient populations more appropriately. Apixio’s tools permit improvements to patient care, as we shall see. But the core of the platform they offer involves uploading SOAP notes, usually in PDF form, and extracting diagnostic codes that coders may have missed or that may not be supportable. Machine learning techniques extract the diagnostic codes for each patient over the entire history provided.

Many questions jostled in my mind as I talked to Apixio CTO John Schneider. Why are these particular notes so important to the Centers for Medicare & Medicaid Services (CMS)? Why don’t doctors keep track of relevant diagnoses as they go along in an easy-to-retrieve manner that could be pipelined straight to Medicare? Can’t modern EHRs, after seven years of Meaningful Use, provide better formats than PDFs? I asked him these things.

A mini-seminar ensued on the evolution of health care and its documentation. A combination of policy changes and persistent cultural habits have tangled up the various sources of information over many years. In the following sections, I’ll look at each aspect of the documentation bouillabaisse.

The financial role of diagnosis and risk
Accountable care, in varying degrees of sophistication, calculates the risk of patient populations in order to gradually replace fee-for-service with payments that reflect how adeptly the health care provider has treated the patient. Accountable care lay behind the Affordable Care Act and got an extra boost at the beginning of 2016 when CMS took on the “goal of tying 30 percent of traditional, or fee-for-service, Medicare payments to alternative payment models, such as ACOs, by the end of 2016 — and 50 percent by the end of 2018.

Although many accountable care contracts–like those of the much-maligned 1970s Managed Care era–ignore differences between patients, more thoughtful programs recognize that accurate and fair payments require measurement of how much risk the health care provider is taking on–that is, how sick their patients are. Thus, providers benefit from scrupulously complete documentation (having learned that upcoding and sloppiness will no longer be tolerated and will lead to significant fines, according to Schneider). And this would seem to provide an incentive for the provider to capture every nuance of a patient’s condition in a clearly code, structured way.

But this is not how doctors operate, according to Schneider. They rebel when presented with dozens of boxes to check off, as crude EHRs tend to present things. They stick to the free-text SOAP note (fields for subjective observations, objective observations, assessment, and plan) that has been taught for decades. It’s often up to post-processing tools to code exactly what’s wrong with the patient. Sometimes the SOAP notes don’t even distinguish the four parts in electronic form, but exist as free-flowing Word documents.

A number of key diagnoses come from doctors who have privileges at the hospital but come in only sporadically to do consultations, and who therefore don’t understand the layout of the EHR or make attempts to use what little structure it provides. Another reason codes get missed or don’t easily surface is that doctors are overwhelmed, so that accurately recording diagnostic information in a structured way is a significant extra burden, an essentially clerical function loaded onto these highly skilled healthcare professionals. Thus, extracting diagnostic information many times involves “reading between the lines,” as Schneider puts it.

For Medicare Advantage payments, CMS wants a precise delineation of properly coded diagnoses in order to discern the risk presented by each patient. This is where Apixio come in: by mining the free-text SOAP notes for information that can enhance such coding. We’ll see what they do in the next section of this article.