The health privacy summit, now in its third year, provides a crucial spotlight on the worries patients and their doctors have about their data. Did you know that two out of three doctors (and probably more–this statistic cites just the ones who admit to it on a survey) have left data out of a patient’s record upon the patient’s request? I have found that the summit reveals the most sophisticated and realistic assessment of data protection in health care available, which is why I look forward to it each year. (I’m also on the planning committee for the summit.) For instance, it took a harder look than most observers at how health care would be affected by patient access to data, and the practice of sharing selected subsets of data, called segmentation.

What effect would patient access have?

An odd perceptual discontinuity exists around patient access to health records. If you go to your doctor and ask to see your records, chances are you will be turned down outright or forced to go through expensive and frustrating magical passes. One wouldn’t know that HIPAA explicitly required doctors long ago to give patients their data, or that the most recent meaningful use rules from the Department of Health and Human Services require doctors to let patients view, download, and transmit their information within four business days of its addition to the record.

At both the Datapalooza and the privacy summit, patient access was not universally acclaimed but considered a minimum requirement for improving health care. It allows the patient to find critical errors, simplifies the sharing with other doctors who are treating the patient, and helps the patient comply with medical advice. Old complaints that patients don’t care or might not understand the contents of the record no longer hold water. Law professor Barbara Evans pointed out that, “To police privacy, you have to know what’s in your records.” In fact, four days is seen as unreasonably long to withhold information, given how quickly medical conditions can become dangerous.

Read access is one thing, but write access raises its own concerns–actually, the same old concerns with a new urgency. Doctors worry that they cannot trust data from patients, and might be held liable for decisions they make based on wrong data. The question of provenance thus requires electronic records to contain fields that indicate the source of data.

Write access by patients cannot be a matter for compromise. Fitness devices and other medical advances are opening up huge new sources of data that can save lives. For instance, one can wear a device that monitors one’s heart rate or blood pressure and sends information to the medical record at regular intervals. There are probably encryption and signing techniques that can assure doctors the patient has not falsified results. The bottleneck to deploying these technologies is the medical record itself, which is rarely set up to accept such data. The doctor must also learn how to use it.

A still higher stage of patient control would take the records away from the doctors altogether and store them with the patient. This is eminently logical–why should you have to go to a dozen different providers to gather a few years’ worth of records? Separate health information exchanges–organizations that charge doctors for exchanging data and, even so, have trouble surviving financially–would evolve into more useful services. Patient control over records would also give patients some control over the dizzying array of places their data goes, often without their consent.

Skeptical doctors point out how little interest their patients (especially the ones who need to show the most interest) take in their health. Moving data to patients will start among a small, educated cohort, but could be widespread if the medical field seriously promoted it, and if a cloud solution like Microsoft HealthVault made it easy.

Cloud computing was discussed at the summit. Lie most aspects of health care, panelists insisted on more transparency there, such as knowing what company runs the underlying storage network, but their general view seemed positive.

Segmentation–still a lot of mire

Ideally, a health provider dealing with a broken bone or a cut would not be prejudiced to learn of your sexual orientation, mental health, or drug habits. But in many places, patients have to protect themselves. They also may choose to participate in research studies without releasing certain sensitive information.

Therefore, one of phantoms being chased by privacy advocates is data segmentation, the ability of a patient to share data with certain people while withholding it from others. Segmentation even made it into a major report by the President’s Council of Advisors on Science and Technology on health data.

Scads of problems are raised by segmentation, not so much technical as procedural, with some policy thrown in.

For instance, patients have trouble telling what might reveal a condition they want hidden. If an HIV patient reveals the list of medications he’s taking, a knowledgeable outsider can easily tell he’s HIV-positive. Even a note in the record that a patient is sensitive to some medication can reveal the condition that makes him sensitive. Free text, which is a large part of medical records, is difficult to handle in segmentation.

Although we don’t seem to have corralled this beast yet, many advances were described at the summit. Pilot systems can tag data with the patient’s preferences. When requests for data come in, these systems hide any data that the patient has restricted and that the requester is not supposed to see. One such system analyzes the record to remove all information that could inadvertently reveal the hidden diagnosis. When there’s a sensitive condition, the system usually omits all free text.

As many people pointed out at the summit, segmentation is an imperfect solution to the stigma attached to many medical conditions. Different diseases have been frowned on at different stages in history, and public education has alleviated some of the shame. Such education is needed so that people don’t have to be afraid about sharing their mental health, or other such conditions.

• MedCPU checks deviations from recommended practices in real time. If a doctor orders a dose that seems unreasonably high for the particular patient, MedCPU may notice it and generate an immediate alert. The system can combine structured data and unstructured data (that is, the free text entered by the provider into the record) to come up with its judgments. Sonia Ben-Yehuda, the founder of MedCPU, says they have bypassed the commmon problem of trying to deal with multiple, incompatible EHR systems by reading what goes on directly from the EHR screen. No need for time-consuming integration!

• Silicon Valley Biosystems (SVBio) collects and collates genetic data from various public sites, dealing with sets that are inconsistent in many ways: they comes from a variety of public repositories, reflect different models of the human genome as it has evolved, and suffer from gaps because researchers withhold key pieces for competitive reasons. However, by discovering overlaps among the data sets, SVBio can turn up crucial new information, such as a genetic variation associated to a disease. The algorithms that SVBio uses to find overlaps among data evolve as it feeds back results.

• County Health Rankings & Roadmaps provides public health information on a county-by-county basis so that localities can compare their health rankings with others that have similar demographics. For instance, why do two communities in different parts of the country with similar populations and poverty rates have different rates of disease? It may turn out that one county has higher levels of education, and these are correlated with lower incidences of illness. Perhaps the other county can do more to keep its population in school. (Of course, if a county has a high educational level along with high poverty, it probably needs to find ways to generate more jobs, but that’s a separate issue.)

As with any conference, the second day of the Datapalooza had fewer people–over 2000 people registered overall–and less energy than the first. But the impressive parade of key figures in the health care field continued to light up the stage:

• Day 1: Bryan Sivak, CTO of the Department of Health and Human Services; Kathleen Sebelius, Secretary of HHS; Jeremy Hunt, Secretary f State for Health in the United Kingdom; Atul Gawande, noted New Yorker author; Jonathan Bush, co-founder of athenahealth

• Day 2: Todd Park, CTO of the United States; Farzad Mostashari, National Coordinator for Health Information Technology; Marilyn Tavener, Administrator of Centers for Medicare and Medicaid Services

Numerous other excellent speakers and deeply knowledgeable panelists joined these luminaries. And we must not forget the hilarious game show where doctors and insurance managers competed to guess what Medicare recipients care about, based on CMS surveys, in hospitals and health plans.

The refernces to the CMS surveys, which were conducted in a rigorously validated fashion, were more than fun. The surveys are invaluable for helping health institutions learn what their patients want and what matters. For instance, during the game show, the doctors guessed that a patient would consider average length of stay very important when selecting a hospital. It turns out that this level of sophistication is way beyond the ken of the average Medicare patient. Length of stay doesn’t turn up as a concern at all in their survey responses.

But this doesn’t mean patients are stupid or that their concerns shouldn’t drive health care. More on patient engagement later in this article.

Discontinuity of Care Documents

In <a href="http://thehealthcareblog.com/blog/2013/06/03/health-datapalooza-day-one-how-will-we-grow-data-for-improving-health/"yesterday's posting from the conference I bemoaned the lag in the development of standards, among other things needed to advance the use of patient data.

The state of standards in health care can be compared to the web. Widespread adherance to the HTTP standard (as well as underlying protocols such as TCP, of course) makes it easy for a programmer to download a web page. Every modern language has a simple HTTP API. But once you get the page, understanding the data is more difficult. Usually, you’re reduced to the old “scraping” model, which requires you to visually inspect the HTML, derive some general principles behind the page’s organization, and hope that it won’t change. Web programmers recognize the difficulties and have stepped up with various mini-standards, including microformats and a handful of new tags in HTML5.

We can see a need for standards all over the health care field: the sharing of research data, the recording of device output, the exchange of patient records, and more.

I discussed research data in my coverage of Sage Congress, a summit on data sharing that brings together leaders in genetics and pharmaceutical research. I pointed out that research results, like web pages, are loosely structured, and mentioned a researcher who had trouble comparing two experiments that measured everything the same way except for medication doses.

Numerous standards have been developed to allow devices to send central systems their data. But I think the spread of consumer electronics for Quantified Self, along with the patient-centered medical home, will put strain on the standards, on the systems collecting data, and most of all on the electronic health records. Coordinating devices adds an extra layer of sophistication to the simple task of accepting data from each device separately.

But the nub of patient care, in terms of data, is the individual health record, and that’s where the big challenges lie. My article yesterday contained a modest critique of the Office of the National Coordinator, vaguely claiming they have been “slow to impose order.” I managed to annoy a friend of mine at the ONC, and promised to explore the question further today. After all, didn’t the ONC promote two protocols for data exchange, CONNECT and Direct? Haven’t they been guiding the health care field toward practices that streamline secure and private exchange, such as DirectTrust and the recent implementation guidelines to assure security and interoperability over Direct?

And didn’t the Stage 2 rules Meaningful Use remove much of the mush around standards, decreeing the use of Direct for exchange and the Consolidated CDA as a format?

All those advances are worth citing. But there is much to be done.

Let’s take a few moments to look over the history of the continuity of care document (CCD), which used to be a couple pages sent with each discharged patient to his or her next institution (a rehab clinic, nursing home, or whatever) and is now in electronic form. A consortium standardized the format of this document in a sleek XML format called the CCR, standing for “continuity of care record.”

The CCR became an instant hit in the IT community, adopted by both Microsoft HealthVault and the short-lived Google Health–but with incompatible changes in each case. This early lack of consistency suggests that perhaps the CCR was not as wonderful as its designers hoped. An expert in health care records has also told me that the CCR was not rich and flexible enough to accommodate the information needed in the field.

But these weaknesses were not why the HL7 standards body decided to create their own document format in direct competition with the CCR. Rather, HL7 didn’t like the CCR because it didn’t implement HL7′s Clinical Document Architecture (CDA), a complex XML structure that HL7 had designed as a basis for its various specifications. So HL7 designed its own Continuity of Care Document (CCD) format.

The CCR committee had struck off in its own direction for good reasons. HL7 standards had evolved in an ungainly manner over the decades, and the new XML documents such as the CDA were thinly wrapped versions of earlier idiosyncratic formats. I have been told that HL7 XML documents often failed XML validation suites. The need for the HL7 CCD format to adhere to the basic CDA format introduced complexity. A lack of specificity allowed vendors to wander off in incompatible directions and produce documents that required individual programmer attention to be harmonized.

The CCR really couldn’t form the basis for a national standard, but many of us were still disappointed that the HL7 CCA was adopted as the law of the land in Meaningful Use Stage 2. I’ve put that past me now, because I realize that the whole debate has the slightly musty feel of an outdated technology, like the debate of the mid-2000s between the office document formats, ODF versus OOXML.

The continuity of care document is a relic of an age when doctors thought they could just give the next doctor down the line a diagnosis and a medication listing, and they were done. Modern accountable care requires much more. Doctors must share comprehensive access to all the patient visits, test results, and treatment outcomes.

We can’t think of data exchange as a discrete event at the seam between one treatment setting and another, but as a continuous activity spanning the patient’s life. We are on the verge of a data deluge that will render the continuity of care document nearly irrelevant. Worrying about its format is like going to a barbecue and arguing over the brand of ketchup. Instead, we must make sure standards apply to the data that really matters. The chance to start anew is what excites me about Blue Button, and why I complained in yesterday’s article about the loose approach to maintaining it as an interoperable standard.

I think Bryan Sivak and Farzad Mostashari were speaking partly of standardized records when they said the ONC efforts were still “scratching the surface” of the data gap in health care today. The interface between electronic patient records, research, and medical devices needs work. And so I stand by my mild statement about the ONC.

Although I am not a fan of Linked Data, the World Wide Web Consortium’s solution to information muddiness, I think it may be useful in the health care field because the Linked Data tools, in all their complexity, are distinctly simpler than the complexity of health data, and therefore are worth the application of good minds. Linked Data requires a lot of analysis to straighten out the chaos of everyday life, but this too is a useful exercise because of the value of health data. Two sessions on Linked Data were offered today at the Datapalooza.

We are all patient advocates

I saw only two persons identified in panels as patient advocates, and met only a few others who identified themselves that way at the Datapalooza. But the modern zeal for giving patients more respect and control ran through the onference. It came up in Todd Park’s keynote and among other speakers, and motivated many of the apps on display.

One well-attended session covered basic privacy issues. We heard some of the themes that I have reported on before, such as how doctors claim HIPAA as an excuse for denying patients access to their own data, whereas HIPAA actually requires the doctor to allow access.

Perhaps the most impressive example at the conference of a large corporation paying sincere attention to patient empowerment was Pfizer Link, a site using Blue Button to give patients in clinical trials access to their own data. In the world of clinical research, this is a radical proposition. Craig Lipset, Head of Clinical Innovation for Pfizer, explained that only 5% of all patients participate in clinical trials. The industry has long been aware that the lack of volunteers adds significant delays to almost all studies. Pfizer is hoping that opening up patient access to data, along with news and relevant information on trials, will increase participation.

MedWatcher is an example of harnassing informed patient input to improve health care. After downloading the app, a person can report symptoms and any adverse effects he feels from a medication. MedWatcher combines and de-identifies this information along with reports from social media such as Twitter and Facebook, with the goal of revealing risks in medication use.

This app had a personal resonance for me because a relative of mine had recently collapsed and spent 24 hours undergoing electrocardiograms and other intensive tests. The collapse was eventually attributed to a new drug he had started taking, and I wonder whether he could have recognized the symptoms of fainting earlier and avoided the extremely expensive trip to the emergency room. The incident is a reminder: all of us are not only patient advocates–we are also patients.

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My first reaction to the conference was to think that, if patients are going to save the health care system, we’ll need to find a new class of patients. But of course, even though patients are notoriously immune to pricing issues (they want the best of everything, and assume they deserve it because they pay their insurance premiums), they are not really to blame. And as I gathered my notes from the conference and walked back through Boston Common and the Public Garden on a glorious Spring day, I realized that several trends–including technological ones not discussed at the conference–can make hope flower in health care as well.

I should mention, for disclosure, that one of the conference sponsors was Greater Boston Interfaith Organization, of which I’m a member.

First stop: should we think of patients as consumers?

One sign of the lively discussion at the Empowering Healthcare Consumers conference was a turn to challenging the whole notion of a patient as a consumer.

On the good side: consumers have choices (which means we have to give patients choices–more on that soon). Consumers drive providers to evolve and provide more of what the consumers want. Consumers go for the best deal and promote competition.

On the bad side: consumers need money. This means that people without high incomes can’t make the choices and get the care. And how many sick people have high incomes, or have relatives who can take enough time away from caring for them to have high incomes?

Consumers also stand alone; they act as individuals rather than members of a community. But health is a community issue (think of people influencing each other to eat or smoke, and of environmental health issues such as chemical poisening and asthma). Dr. Eugene Lindsey, President and CEO of Atrius Health, said it would take a long time to move from the I-focus of consumer empowerment to the we-focus of community.

Luckily, the emergence of social media might break through the isolation of consumers. But then we face another problem: consumers of health care don’t always know what to consider when they rate doctors for quality. A doctor may have a great bed-side manner but forget to order a critical test. A patient may come out of surgery feeling relieved and rate the hospital highly, then relapse a few months later because the staff didn’t follow up well.

A consumer has other weaknesses: she often views events in isolation rather than as part of a lifelong process (which health must be), and looks only at the most visible outcomes instead of underlying system dynamics.

Thus, many of us are afraid that treating the patient as a consumer will exacerbate class differences, with the best treatment going to those with money, education, and a sense of confidence built by social reinforcement.

The ideal held in mind by reformers–the ideal of patients carefully comparing doctors on price and quality measures and asking tough questions–also runs up against numerous psychological realities: we are afraid to challenge authority figures who potentially hold our lives in their hands, feel gratitude toward them for the care they’ve already given in making diagnoses, feel risk-averse when our bodies or family members’ bodies lie on the line, and lack the energy to do research in a tense situation where we may feel sick and perhaps feel an intense urgency to choose a course of action.

Hurdle: patients aren’t given information

A true consumer knows how much he’s paying and where he could use the money if he rejected the product currently on offer. Health care is nowhere near providing such transparency. Costs are notoriously obscure; they’re like reserving an airplane flight, except even airlines don’t submit a dozen different bills from people you’ve never heard of, or make you choose a deductible and a cap years in advance of flying. And even when doctors present alternatives to patients (include the alternative of “wait and see”), few patients can grasp the consequences.

Quality is also unmeasurable, despite recent efforts by governments and insurance companies to release certain important data sets such as deaths and readmissions in hospitals. These are invaluable for hospital efforts at quality improvement and accountability, but are too hard to connect to an individual event or treatment to be of much use to patients.

Lynn Nicholas, President and CEO of the Massachusetts Hospital Association, boasted of the association’s reports on quality, which they started in 2005 and now offers 37 quality and patient safety measures. She admitted, however, that it’s aimed more at hospitals than patients because it’s hard to compare hospitals. They are working on making it more consumer-friendly.

Hurdle: the problem is not in the procedure but in the system

But to me, one of the highpoints of the day was Nicholas’s response to a patient advocate who had laid out some of the horrors low-income patients have to go through trying to determine their out-of-pocket liabilities. Nicholas responded that this was a symptom of a dysfunctional system and that we must totally abandon the fee-for-service approach (which of course, hospitals have been exploiting for years) toward “soup to nuts” coverage for the whole patient. This goal–not just finding a hospital where a knee surgery is two thousand dollars cheaper–is the true goal of consumer empowerment as well.

Along those lines, Nicholas also said that evaluating a doctor’s or hospital’s performance on a single procedure or event was ultimately fruitless. The success of an intervention is a long-term process. We can measure and make judgements on quality measures collected by hospitals, such as falls, but not so easily on outcomes.

The idea of evaluating a system rather than an individual procedure is intellectually satisfying, but Martha Bebinger, the WBUR reporter who chaired the day’s panel, pointed out that when we shop for cars or TVs we are not comparing systems. The transition would be a hard one for health consumers to make. At the same time, physicians are offended at being compared like toasters in Consumer Reports.

Advances in self-care

Technology can help reach the vision that we all have for better health care, if the technology is developed from the angle of making people live better rather than just providing more expensive tests and procedures to fix people up when they stumble. For instance, the Quantified Self movement is still mostly a playground for slightly obsessive folks who already to all the right things to take care of their bodies and mental states. But new devices and apps that can turn cell phones into data collectors may change that.

Gradually, doctors will advise their patients to monitor themselves and use devices in the home (or even in their heads) for positive feedback that improves behavior. Such devices will have to be idiot-proof, and the suggestions they give extremely easy to work into a patient’s lifestyle. The devices can educate the patient as time goes by to understand what is being monitored and how valuable it is to health. One form of education must be done up front, though: the provider must help each patient decide how much data he or she is comfortable sharing in order to provide the input to the decision machines that, in turn, will help researchers discover new cures.

What we can learn

Ultimately, all the hurdles we run up against in trying to empower the consumer–cloudy quality measures, difficulties comparing providers, a lack of feedback that can drive behavior change–come down to missing data. If we can collect the right kinds of data, feed them into systems that process it intelligently, and return it to the doctor and the patient in a form they can understand, we can transform health care.

Health care costs have moderated a bit in Massachusetts and across the nation, but the slowdown has been attributed more to the poor economy than to health care reform. According to Barbara Anthony, undersecretary of the Office of Consumer Affairs and Business Regulation, surveys suggest that consumers in the state are feeling the pinch of health costs more this year than before.

The government plans to hold more forums like this one around the state. I bet a forum in a depressed area such as Fall River or Springfield will draw different attendees and impressions from the one we had today, which was dominated by people who either work in the health professions or deal with it in some capacity as lobbyists and patient advocates. (Yes, I had a health care lobbyist right at my table.) And I don’t know how many ordinary citizens are going to take a morning off from work to discuss health care costs and consumer empowerment in health care. But in that, as in so many other issues affecting health care costs, I’m hoping for rapid change.

Because readers of this blog have been grappling with these concerns for some time. I’ll provide the barest summary of topics covered in Mayer-Schönberger and Cukier’s extensive overview, then provide some complementary ideas of my own.

Summary of book topics

Some of the themes of Big Data that grabbed my interest include:

• New tools for measuring the world and people’s activities provide data sets that are many orders of magnitude higher than we are used to having, and computers tied together in clusters are run novel techniques to find insights never available to us before.
• Data crunchers are finding correlations that provide useful guidance for actions. Mere correlation cannot tell us why something is happening, but often it doesn’t matter. The authors cite numerous examples where correlations by themselves suggested valuable actions.
• Because big data opens up efficiencies to those savvy enough to use it, the future of business belongs to huge organizations (including middlemen and aggregators) with the resources to collect both data and experts to manipulate it, or to smaller organizations who are nimble enough to make hay from open or cheaply available data sets.
• Control over our own lives may slip more and more from our hands as institutions use statistical insights to determine not only what we are doing, but what we are likely to do in the future. One chapter in Big Data is devoted to policy-related remedies.
• Old-timers’ intuitions are challenged by the findings of big data, just as Deep Blue’s brute-force processing of chess moves can overcome the world’s best human chess masters. Nevertheless, the authors end affirming the importance of human insight and choice.

These represents a grand agenda for one book (nor have I exhausted all its topics), but I’d like to jump ahead to ideas that the Big Data stimulated for me, leaving it up to readers to get the book for themselves if they want to study all its conclusions. In the interest of full disclosure, I’ll mention that one author– Cukier, the data editor of The Economist–helped me get an article published several years ago.

Other aspects of big data

Mayer-Schönberger and Cukier’s view of traditional statistical techniques deserves a bit of examination. They tend to place these in opposition to newer techniques of crunching big data. According to their thesis, the old techniques were developed to deal with small samples and all the uncertainties they presented about representing the whole population. Those outdated assumptions compromise their applicability to a new age, where computers just iterate over the whole population. The authors even recount a suspicion of traditional statisticians made by one of their big data experts, New York City’s Mike Flowers, who was put off by statisticians’ interest in “arcane concerns about mathematical models” (p. 186).

Certainly, the authors say, there is a place for traditional statistics. It can even be used to run traditional experiments in order to validate suggestions made by big data crunching. But I think the relationship between old and new techniques is much tighter. This question has an important bearing on the power exerted by big data, because I believe proper techniques will be harder to learn and accurately apply than Mayer-Schönberger and Cukier suggest. While they expect the skills soon to become “commonplace” (pp. 125-126), I think there will be a crying shortage for some time, allowing a few large institutions with deep pockets to corner the market.

Let’s take the common big-data task of clustering, which might help in such situations as an art dealer trying to determine that Leonardo da Vinci is closer to El Greco in style than to Andy Warhol. Clustering algorithms can take a very long time to run, and choosing good starting points is important to reduce compute time. In fact, characteristics of the data can help a data scientist choose which algorithm to run in the first place. So what can provide with a starting point for the big data venture? A traditional statistical analysis of a random sample could be a good choice.

This extends throughout the field of data. Even the choice of the best sorting technique–a common exercise in the first classes for programming students–can vary depending on the characteristics of the data being sorted.

Big Data is not oriented toward this sort of technical discussion. The authors chose quite reasonably to avoid equations or other accoutrements of a mathematical explanation, which I’m sure would have scared off readers. And yet without some such background (to be sure, I’m no mathematician or statistician), one can’t determine the real strengths and weaknesses of the big data movement.

Let’s turn to the critical question of transparency, which Mayer-Schönberger and Cukier consider a necessity to help people challenge the decisions that others derive from data analysis. Transparency is no panacea, in my view. First, algorithms are incredibly complex. Second, as we’ve seen, the choice of algorithm (as well as the data to be analyzed) requires some subjective judgment, which is hard to challenge.

Worse still, any calculation affecting humans has winners and losers, and therefore makes some people eager to game the system. Big Data mentions the trivial example of orange used cars being found to be in better shape than other used cars (pp. 66-67), and points out how ridiculous it would be for car owners to paint the cars orange before selling them. These kind of dueling incentives apply across the board. It’s one reason Google doesn’t publish its search rank algorithms–and in fact, one reason it changes that algorithm on a daily basis.

Thus, instead of asking banks or insurers to reveal their decision-making processes, it may be better to give individuals access to data about themselves, and a process such as the “external algorithmist” proposed by Mayer-Schönberger and Cukier (p. 181) to allow individuals to present exculpatory evidence.

Both the external algorithmist and the internal algorithmist (similar to an ombudsman) envisioned by the authors are good additions to an organization. Dr. Brigitte Piniewski (whose work with an NIH-funded experiment in the community collection of health data, was covered in a Strata article) suggests that the role of an algorithmist is a very difficult one, and in fact too big for a single person to fill. The algorithmist, in her assessment, must have some basic knowledge not only of statistics but of real-world disciplines such as physics and biology, in order to have a sense of what is possible and what analytic results are absurd on their face.

Even more important for algorithmists is a correct attitude: an inate skepticism that may be in-born trait rather than something teachable. And she says this willingness to constantly challenge accepted beliefs must run throughout the organization, which cannot rely on a single expert to provide this corrective.

Because many institutional decisions take place in the background where the affected individuals never find out they took place at all (when have you ever learned that a marketer decided not to offer you a great deal?), the reactive approach has grave limitations. Furthermore, it’s hard to trust institutions to take self-corrective actions to preserve privacy and individual automomy at this historical moment when we’re reeling from revelations that the IRS targeted institutions based on their political positions and the Justice Department gathered phone records across the board from Associated Press reporters.

My last cluster of concerns relates to the role of human intuition and creativity in an age of big data, where the authors end their book on a high note. It’s important to recognize that big data analysis consists of applying what happened in the past to what will happen in the future. Had there been no recent influenza outbreaks, Google could not have run the tests that produced their famous flu prediction algorithm.

And thus the data we collect in the past hangs over us. Suppose we analyze arrest and conviction records to determine whom we should target most heavily for policing? Guess what? African-Americans and Hispanics will get the bulk of policy scrutiny, because the police have targeted them disproportionately for generations. In short correlations could turn into textbook cases of self-fulfilling prophecies. As Mayer-Schönberger and Cukier say, we still need human intervention to think outside the box.

I think big data will accentuate today’s trend to differentiate between the commoditized and the innovative. Like manufacturing, we will see more and more decision-making calling on big data–but with crucial human correctives, as noted before.

The relation between invention and standardization is a bit like the promise of 3D printers such as the Makerbot. On the one hand, they allow flights of invention by clever tinkerers like never before. But the printers depend on microchips made in sterile labs by the millions, and other materials from large manufacturers. (The biggest US producer of polylactide filament is a subsidiary of Cargill.) Used properly, big data could similarly be the greatest contributor in history to personal innovation.

There about 7,000 rare diseases, fewer than 5% of which have cures. The number of people currently engaged in making drug discoveries is by no means adequate to study all these diseases. A recent gift to Harvard shows the importance that medical researchers attach to filling the gap. As an alternative approach, abstracting the drug discovery process into a game could empower thousands, if not millions, of people to contribute to this process and make discoveries in diseases that get little attention to scientists or pharmaceutical companies.

The biological concept behind Fit2Cure is that medicines have specific shapes that fit into the proteins of the victim’s biological structures like jig-saw puzzle pieces (but more rounded). Many cures require finding a drug that has the same jig-saw shape and can fit into the target protein molecule, thus preventing it from functioning normally.

But there are millions of possible medications, and it’s hard computationally to figure out which medication can disable which target protein. The way forward may be to tap the human ability to solve (and enjoy solving) jig-saw puzzles.

Fit2Cure therefore presents the user with the shape of the target protein (the common representation called the van der Waals surface) and the shape of a medication. The player can easily and quickly rotate the two molecules and search for pl.aces where they fit. The molecules can be rendered partly transparent to help the player see the internal shape he or she is trying to fit.

The game was developed by a team led by Geoffrey Siwo, a PhD student at University of Notre Dame and an IBM PhD scholarship award recipient. It is one of the efforts of Sayansia, which Siwo founded together with Ian Sander and Victoria Lam, also PhD students at the University of Notre Dame. Sayansia is derived from the Swahili word sayansi, which means science, and their motto is “Scientific discovery through gaming.” The game development was done in conjunction with the serious games company DynamoidApps , based in Seattle, USA. To play the game, you need to download the Unity Web player.

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more.

Data sharing is not an unfamiliar practice in genetics. Plenty of cell lines and other data stores are publicly available from such places as the TCGA data set from the National Cancer Institute, Gene Expression Omnibus (GEO), and Array Expression (all of which can be accessed through Synapse). So to some extent the current revolution in sharing lies not in the data itself but in critical related areas.

First, many of the data sets are weakened by metadata problems. A Sage programmer told me that the famous TCGA set is enormous but poorly curated. For instance, different data sets in TCGA may refer to the same drug by different names, generic versus brand name. Provenance–a clear description of how the data was collected and prepared for use–is also weak in TCGA.

In contrast, GEO records tend to contain good provenance information (see an example), but only as free-form text, which presents the same barriers to searching and aggregation as free-form text in medical records. Synapse is developing a structured format for presenting provenance based on the W3C’s PROV standard. One researcher told me this was the most promising contribution of Synapse toward the shared used of genetic information.

Data can also be inaccessible to researchers because it reflects the diversity of patient experiences. One organizer of Army of Women, an organization that collects information from breast cancer patients, say it’s one of the largest available data repositories for this disease, but is rarely used because researchers cannot organize it.

Fragmentation in the field of genetics extends to nearly everything that characterizes data. One researcher told me about his difficulties combining the results of two studies, each comparing responses of the same genetic markers to the same medications, because the doses they compared were different.

The very size of data is a barrier. One speaker surveyed all the genotypic information that we know plays a role in creating disease. This includes not only the patient’s genome–already many gigabytes of information–but other material in the cell and even the parasitic bacteria that occupy our bodies. All told, he estimated that a complete record of our bodies would require a yottabyte of data, far beyond the capacity of any organization to store.

Synapse tries to make data easier to reuse by encouraging researchers to upload the code they use to manipulate the data. Still, this code may be hard to understand and adapt to new research. Most researchers learn a single programming language such as R or MATLAB and want only code in that language, which in turn restricts the data sets they’re willing to use.

Sage has clearly made a strategic choice here to gather as much data and code as possible by minimizing the burden on the researcher when uploading these goods. That puts more burden on the user of the data and code to understand what’s on Synapse. A Sage programmer told me that many sites with expert genetics researchers lack programming knowledge. This has got to change.

Measure your words

Standardized data can transform research far beyond the lab, including the critical areas of publication and attribution. Current scientific papers bear large strings of authors–what did each author actually contribute? The last author is often a chief scientist who did none of the experimentation or writing on the paper, but organized and directed the team. There are also analysts with valuable skills that indirectly make the research successful.

Publishers are therefore creating forms for entering author information that specifies the role each author played, called multidimensional author descriptions. Data mining can produce measures of how many papers each author has worked on and the relative influence of each. Universities and companies can use these insights to hire good candidates to fill the particular skills they need.

One of the first steps to data sharing is simply to identify and label it, at the relevant granularity. For scientific data, one linchpin is the Digital Object Identifier (DOI), which uniquely identifies each data set. When creating a DOI, a researcher provides critical metainformation such as contact information and when the data was created. Other researchers can then retrieve this information and use it when determining whether to use the data set, as well as to cite the original researcher. Metrics can determine the “impact factor” of a data set, as the now do for journals.

Sage supports DOIs and is working on a version layer, so that if data changes, a researcher can gain access both to the original data set and the newer ones. Clearly, it’s important to get the original data set if one wants to reproduce an experiment’s result. Versioning allows a data set to keep up with advances, just as it does for source code.

Stephen Friend, founder of Sage, said in his opening remarks that the field needs to move from hypothesis-driven data analysis to data-driven data analysis. He highlighted funders as the key force who can drive this change, which affects the recruitment of patients, the collection and storage of data, and collaboration of teams around the globe. Meanwhile, Sage has intervened surgically to provide tools and bring together the people that can make this shift happen.

Related Resources:

• Data sharing drives diagnoses and cures, if we can get there (part 1)
• Parts of Sage Congress were videotaped and posted online.
• Broadening consults and narrowing queries: HealthTap’s social network
• Open source developers combine efforts in the health care field
• Open Research For Open Cures: A Report From Sage Congress

Strata Rx Heath Data Conference — Strata Rx brings
together the diverse communities driving innovations in big data analytics for health care. Learn about the transformation of health care through big data and how to position your company to benefit from these trends. Learn more.

Sage Congress, held this past week in San Francisco, investigated the multiple facets of data in these field: gene sequences, models for finding pathways, patient behavior and symptoms (known as phenotypic data), and code to process all these inputs. A survey of efforts by the organizers, Sage Bionetworks, and other innovations in genetic data handling can show how genetics resembles and differs from other disciplines.

An intense lesson in code sharing

At last year’s Congress, Sage announced a challenge, together with the DREAM project, intended to galvanize researchers in genetics while showing off the growing capabilities of Sage’s Synapse platform. Synapse ties together a number of data sets in genetics and provides tools for researchers to upload new data, while searching other researchers’ data sets. Its challenge highlighted the industry’s need for better data sharing, and some ways to get there.

The Sage Bionetworks/DREAM Breast Cancer Prognosis Challenge was cleverly designed to demonstrate both Synapse’s capabilities and the value of sharing. The goal was to find a better way to predict the chances of survival among victims of breast cancer. This is done through computational models that search for patterns in genetic material.

To participate, competing teams had to upload models to Synapse, where they were immediately evaluated against a set of test data and ranked in their success in predicting outcomes. Each team could go online at any time to see who was ahead and examine the code used by the front-runners. Thus, teams could benefit from their competitors’ work. The value of Synapse as a cloud service was also manifest. The process is reminiscent of the collaboration among teams to solve the Netflix predication challenge.

Although this ability to steal freely from competing teams would seem to be a disincentive to participation, more than 1400 models were submitted, and the winning model (which was chosen by testing the front-runners against another data set assembled by a different research team in a different time and place) seems to work better then existing models, although it will still have to be tested in practice.

The winner’s prize was a gold coin in the currency recognized by researchers: publication in the prestigious journal Science Translational Medicine, which agreed in advance to recognize the competition as proof of the value of the work (although the article also went through traditional peer review). Supplementary materials were also posted online to fulfill the Sage mission of promoting reproducibility as well as reuse in new experiments.

Synapse as a platform

Synapse is a cloud-based service, but is open source so that any organization can store its own data on servers of its choice and provide Synapse-like access. This is important because genetic data sets tend to be huge, and therefore hard to copy. On its own cloud servers Synapse stores metadata, such as data annotations and provenance information, on data objects that can be located anywhere. This allows organizations to store data on their own servers, while still using the Synapse services. Of course, because Synapse is open source, an organization could also chose to create their own instance, but this would eliminate some of the cross-fertilization across people and projects that has made the code-hosting site GitHub so successful.

Sage rents space on Amazon Web Services, so it looks for AWS solutions, such as DynamoDB for its non-relational storage area, to fashion each element of Synapse’s solution. More detail about Synapse’s purpose and goals can be found in my report from last year’s Congress.

A follow-up to this posting will summarize and compare some ways that the field of genetics is sharing data, and how it is being used both within research and to measure the researchers’ own value.

Related resources:

• Parts of Sage Congress were videotaped and posted online.
• Broadening consults and narrowing queries: HealthTap’s social network
• Open source developers combine efforts in the health care field
• Open Research For Open Cures: A Report From Sage Congress

Strata Rx Heath Data Conference — Strata Rx brings
together the diverse communities driving innovations in big data analytics for health care. Learn about the transformation of health care through big data and how to position your company to benefit from these trends. Learn more.

Studies and casual observations show that all sorts of mobile and messaging services are on the increase among doctors, but this in itself doesn’t constitute the kind of diverse, group problem solving that social media implies. One of the new initiatives at HealthTap is called “Curbside Consult,” and represents in my mind a big step toward the supple information sharing suggested in the book #SOCIALQI, which I reviewed last month.

In a Curbside Consult, a physician uses the HealthTap network to broadcast a question to other physicians. This will hopefully produce more options, quickly, than the traditional process of contacting individual colleagues known to the physician. The asking doctor can restrict the distribution of the question to particular specialties. By default, HealthTap directs questions to the doctors that have the best and deepest expertise on the subject matter, using an ontology and a set of proprietary algorithms. CEO Ron Gutman says, “The huge amount of data we’ve collected by triaging many millions of user questions to doctors and processing peer reviews of the doctors’ answers, together with the data we gathered from our PubMed integration and from the referral network data we’ve amassed, help us hone these algorithms to provide the best matches to these doctor to doctor questions.”

Today HealthTap also announced a new tagging feature for questions from patients. HealthTap already perform some semantic analysis on patient questions and returns possible results, which turn out to satisfy the patient about 96% or 97% of the time. Clearly, by now, the most common questions have been answered and the network is ready to move to a new stage. They boast of having provided more than 610 million answers and are receiving an average of 50 thank-you notes each day reporting that an answer on HealthTap helped to save a life. What’s next?

Now patients can add some personal metadata to their question (while remaining anonymous). The list of available attributes is drawn from the entire HealthTap ontology, and is contextual to the question itself. Examples include age, gender, prior conditions, current medications, procedures, allergies, risk factors, and medical devices in use.

This should help doctors provide more relevant answers. Incidentally, it helps keep the doctors returning to the site, because it opens up a whole new world of questions to answer.

Both the personal characteristics for patient questions and the curbside consults for doctors leverage a network in ways that can bring faster relief to patients (and thereby, hopefully, reduce costs in the health care system) without risking the privacy of patients. Kudos to HealthTap.

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The big HIMSS conference in New Orleans, a crossroads for doctors, technologists, and policy-makers, provided a one-stop check-in. I already covered several aspects of the conference in two earlier postings, Singin’ the Blues: visions deferred at HIMSS health IT conference and Slow & Steady: looking toward a better health IT future at HIMSS. Here I’ll summarize a couple more trends in data exchange and basic functions of health IT systems.

I Ain’t Gonna Tell Nobody: the drawbacks of trying to do it all yourself

In the 1980s, as computing burst out of isolated data centers and became part of all modern commerce (except health care and education, which suffer from a number of similar constraints), computer companies offered “all in one” solutions that attempted to keep the customer within the provider’s walls. In fact, Digital Equipment Corporation, the towering success of the era, named one of their products ALL-IN-1.

Most customers chose flexibility over the convenience of the “all in one” products, and companies with open strategies such as Sun Microsystems came to dominate. Later, Microsoft adopted the integrated approach with much success, but that epoch also is coming to an end. In the health care field, however, “all in one” thrives. I found some examples at HIMSS this year.

There are certainly attractions to choosing a unified solution. A contract with EMC, for instance, covers you all the way from virtual computing power (provided by their subsidiary VMware and available as private or public clouds) and information storage (tapping into EMC’s high availability, security and backup) to clinical applications and analytics provided by another famous subsidiary, Greenplum.

I also swung by Optum, which is really a collection of companies, having bought hundreds in the health care space. This was explained to me by developer Vaibhav Bhandari (author of O’Reilly’s Enabling Programmable Self with HealthVault), who has helped implement the Direct protocol at Optum to tie these different products together, just as much as to enable patient data exchange with outsiders. By using Direct exchanges instead of older methods (such as fax and even paper) for conducting transactions between doctors and payers, Optum drastically cuts costs, which could be up to $25 per transaction. In addition to email and web interfaces, Optum’s Direct implementation offers access through Java and .NET APIs. The project shows that a comprehensive proprietary solution can coexist with open standards.

Another integrated approach involves mobile app development. Diversinet provides a platform with a number of presentation-layer services needed by mobile apps, such as logging for security and auditing purposes. So the programmer is relieved from reinventing a number of wheels, and enjoys easy integration with other apps based on Diversinet.

What will happen, though, when doctors or patients want access to the myriad of outside health apps being developed? How many app developers are interested in data exchange? The problems we’ve had with EHRs for years could be repeated at the new level.

This was the useful lesson I took from an article by a doctor who was critical of patient access to records. For the most part, Dr. Fisher indicted himself with the accusations aired by Dr. Eric Topol at his HIMSS keynote. His article recycled old, discredited arguments about patients who can’t understand their records or will be more likely to sue. But it’s worth noting some real problems with the app he reviewed.

The app has the potential to add great value to doctor/patient interactions and patient self-help. But (here I am just drawing conclusions from my reading of the article), because the doctors’ own medical systems and EHRs have no incentive to incorporate the app, its business model rests on patient installation. Along with some other design choices (perhaps related to legitimate privacy worries), the app is hard to use.

At HIMSS, I talked to people from a small analytics firm called Valence Health, which siphons up data from numerous EHRs (using the ad hoc methods I described in my first posting from this year’s conference. Valence Health can then provide lots of useful quality measures and population health goals. For instance, a practice can see which of its physicians are failing to prescribe the commonly recommended treatments for medical conditions. The practice can determine which patients are in high-risk groups and whether they’re coming to their appointments. Or whether the practice’s rates of readmissions are higher or lower than the norm. To provide comparative data, Valence Health contracts with numerous insurance companies. This would be good data to have in the public domain.

Reflecting the ubiquity of mobile phones–even a doctor who professes to hate electronic records and health applications will have one–a number of companies selling apps came to HIMSS. One of them, docBeat, sponsored a social media party set up by health care leader and blogger John Lynn. CEO Sunny Tara told me that the app, which permits secure messaging (voice, and text, HIPAA compliant) and data transfers for doctors, was influenced by work in the game industry. Sunny has leveraged his experience with gaming to geofence all the hospitals in the U.S. so you can track when a physician is at hospital to improve quality of care outcomes.

MTBC offers a patient portal somewhat like Diversinet, but its focus is on an “all-in-one” experience backed up by a centralized data store. Patients can quickly view their records, get reminders and alerts for disease management, order prescriptions, and even conduct video interviews with their doctors. The cloud storage enforces the security that allows patients to view data on their devices without risk of having it exposed on the Internet in unencrypted form.

Many of these services are routine at large practices but are rarely available at the one- and two-physician shops that still predominate in the US, and rarely with an interface as spiffy as the one I saw at the MTBC booth. MTBC appeals to this customer base by offering even its own cloud-based EHR.

Any system aimed at small practices needs to support data exchange, and MTBC President Stephen Snyder assured me that this was designed into their platform. “We have more than 40 interfaces with other EMRs, hospitals, and vendors. Also, we are committed to developing HIE connections to our EHR to support our national client base and are currently developing these connections in New Jersey and New York.”

One more area where a comprehensive solution may do the job best is security, the biggest headache on the mind of hospital administrators. Comprehensive expertise is effective for security because it is inherently holistic: there is no point to triple-locking your front door and leaving a side window open. CynergisTek is a consulting firm in that area with a leading figure in health security, Mac McMillian, as CEO. They look at everything for IDS and encryption to logging, where McMillian says they recently purchased the first company to offer application-level logging for health applications.

Hotter Than That: the open source future peeps through

Everything that makes modern health software hard to interconnect, hard to adapt, and hard to extend would be ameliorated by open source software. There are almost too many open source solutions already, and developers might be more successful if they converged on a few systems with modern interfaces, based on a common format. Still, encouraging evidence of open source’s advance turned up at HIMSS this year.

The humming center of the HIMSS conference this year was its Interoperability Showcase, located to encourage foot traffic through it on the way to or from other events. I saw some good talks there and visited several booths. But Will Ross of Redwood Mednet told me many of the kiosks in the Showcase are still showing Stage 1 of Meaningful Use.” The final Stage 2 rules were released toward the end of 2012, and vendors are still implementing them. Furthermore, Meaningful Use offers a menu of features for both vendors and health care providers to support, so you may have a system that–for instance–shares radiology images but your health care partner may not. And of course, a provider can’t demonstrate interoperability at a Stage 2 level if the partner is still at Stage 1. But Ross also said that some vendors and organizations were ahead of the curve, and even had some Stage 3 features.

On the open source front, MITRE has created an implementation of the S&I Framework’s RHEX protocol for the RESTful exchange of health data over the web. I talked to some people in Maine who were using it to connect geographically disconnected communities, including islands. The patient access component built on RHEX is called hReader. Its web site boasts of it being “provider-agnostic while other mobile solutions are currently proprietary, walled gardens.”

The central question of any health record is who it describes–was it actually generated for the patient I think it was? Patient identification is a fiendishly tough problem, given that the American public loathes centralized identification systems (antipathy to “show me your papers” has been a constant across time). A speaker at HIMSS reported that European countries are dealing with the same problem, even though some countries have national identifiers, because patients cross borders so much.

A range of proposals have been aired, from a Voluntary Universal Healthcare Identifier that essentially tries to do what government is not allowed to do, to using email addresses generated through the Direct.

The first part of the open source OpenHIE project, which runs under the Open Health Tools umbrella, is OpenEMPI, an open source patient identifier system. OpenHIE is currently used throughout Rwanda, in tandem with the OpenMRS record system. In Rwanda, OpenEMPI relies on a national identifier. An OpenEMPI developer told me it has also been put to work in the United States by maintaining a master index that maps the unique ID maintained by one health provider to the unique ID maintained by another. The OpenEMPI web site indicates that it accepts the user’s choice of matching algorithm, so it potentially provides a home for any patient identification system. OpenEMPI has a RESTful interface.

In the absence of portable IDs, health exchanges and providers cobble together heuristics. A couple years ago, an HIE told me they check a selection of identifying characteristics, with 17 different ones to choose from. On Monday I heard from Gevik Nalbandian of NextGate that they also have 17 characteristics to choose from. I have no idea whether the characteristics have stayed the same over time.

Nalbandian said that in some organizations, as much as 23 percent of their records are duplicates. The need to find all the data about a patient provides a reason for using NextGate. HIE obviously is another. NextGate uses fuzzy pattern matching to discover matches even when letters have been omitted or transposed in a name. They are currently intrigued with the popularity of OpenID and OAuth, and would like to interact with the popular web sites that use those for single sign-on and data exchange.

New Orleans–that long-suffering city so rich in spirit–proved to be an inconsistent venue. As we started, a fire disrupted water distribution and forced everyone to stop drinking tap water for a couple days. There were reports of difficulty getting flights and finding hotels. Temperatures were also 20 degrees below normal. The entertainment and restaurant scene was in good form, however.

Mayor Mitch Landrieu gave a keynote inn which he pointed to the opening of 103 primary care clinics about the city, where people could go for routine care instead of packing the emergency rooms of hospitals. He was even prouder of an innovation center New Orleans is opening to develop new health care applications. While details were sparse, Landrieu’s emphasis on reaching the individual sounded promising.

The conference organizers failed to provide enough overflow rooms for all the attendees who wanted to hear Bill Clinton’s speech, and there were numerous audiovisual problems, of which the worst was playing the audio and video out of sync. The Clinton Global Initiative and HIMSS had just announced a partnership that morning. But oddly enough, Clinton made no mention of it in his speech, which focused on the economic and political aspects of health care reform along with an appeal for the private sector to take responsibility for public health.

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Nobody Knows The Way I Feel This Morning: patient engagement and all that jazz

Yesterday I spoke of the gap between the reform-minded leaders of health care and the institutions that mostly take care of us. The latest battleground between these peaks of care is the movement variously called patient engagement, patient empowerment, and participatory medicine.

There’s nothing new about this concept. Desperate patients have been self-educating, negotiating with health care systems, and creating advocacy groups forever. On the self-help front, Prevention Magazine began (according to Wikipedia) in 1950. The Society for Participatory Medicine was founded in the mid 2000′s, around the time e-Patient Dave made the concept into a meme through his brave online sharing of his care.

HIMSS has thrown its support behind the Society for Participatory Medicine, which had a lunchtime meeting at the conference yesterday to discuss increasing membership and grass-roots promotional activities. (Folks, consider yourselves promoted.) HIMSS also invited teh popular author Eric Topol to deliver yesterday’s keynote. And the first statement offered by Topol was praise for Regina Holliday, a consumately self-educated patient advocate and creator of the famous artwork and painted jackets in the Walking Gallery.

Perhaps the most encouraging observation I made yesterday was that Holliday (who I’m told had to leave the conference early) generated more buzz than any other person, even Bill Clinton, who will be delivering a keynote today.

Topol’s talk was strong on messages but miserly in the way of guidance. Yes, the range of cheap devices that let us self-diagnose are impressive. Forums for sharing information are also powerful. The missing link, in my opinion, is a respected authority that can persuade the patients who most need these devices and networks–the ones with the most health problems, the most financial and social hurdles to care, the least motivation–to join up.

Topol knows well the problems we face. He excoriated doctors for refusing to email patients or offer easy access to medical records, and he threw in some broadsides against EHR vendors as well. My question is what forces will lead to change, which is the main question I asked in my articles Five Tough Lessons I Had to Learn About Health Care and Five elements of reform that health providers would rather not hear about.

The achievement gap left open by Topol is partly filled by the book Engage!, edited by Jan Oldenburg and recently released by HIMSS Press. Directed at clinicians, it lays out reasons for exchanging information more freely with patients, the use of modern media ranging from text messaging to video, and the role of regulations such as HIPAA. Typically for an anthology, the authors repeat each other to some extent, but the reader who persists can find a broad range of ideas and models to follow.

The book does not recommend particular tools or vendors, a smart choice because the field is likely to explode with innovation soon and be subject to rapid change. But in some ways the book is even broader than the promise in its title, because it contains critiques of the current delivery of medicine and visions for change.

Blue Horizon: getting under the tent with the Intelligent Hospital Pavilion

Each year, HIMSS offers a knock-your-socks off tour of an idealized hospital setting, fully wired with every monitoring, recording, and alerting tool made possible by modern technology. If this makes you think of a Panopticon where you can’t even enter a room without showing up on a screen as a miscreant for failing to wash your hands, you got it right. This may be a stressful environment to work in, because not only do your missteps generate immediate error messages, but they are counted and squirreled away for later analysis. On the other hand, the knowledge that a machine is checking your dosages and keeping track of potentially wandering patients can reduce staff stress.

I felt much better about the Intelligent Hospital Pavilion this year. For one thing, robots are cute. But more significantly, last year, I reacted negatively to the screens of numbers and graphs being thrust at the viewer. This year, my sense of information overload lessened, perhaps because I was more accustomed to the concept and perhaps because the interfaces themselves have improved. I still wonder whether the designers of the pavilion have considered the perenial problem of alert fatigue. I suspect that, if doctors and nurses have some say in the implementation of these technologies, the real-life deployments will be less technologically intense than the demo in the pavilion.

My favorite room in the pavilion was the pharmacy, because here the technology was most seamlessly incorporated into a physical environment that facilitates a human being’s workflow. Pre-packaging medications in small doses, putting controlled substances in locked cabinets, and adding RFIDs all help a physician and nurse do the right thing automatically.

Another concern I have with the intelligent hospital is how much effort an institution will have to put in–and how many bugs they’ll have to iron out during production use–just to integrate the many different vendors that provide the equipment. (At least half a dozen vendors were listed for each room.) This harks back to my criticisms of the health IT field in yesterday’s article. Along those lines, it was encouraging to hear in one session that device manufacturers have made great progress during the past year adhering to industry standards for data exchange.