Omics update: National Human Genome Research Institute Health IT news

Well, this bears watching.

NIH grants seek best ways to combine genomic information and EHRs
Researchers seek to better understand genomic basis of disease, provide tailored care to patients

Bethesda, Maryland, Tues., September 1, 2015 - A dozen awards from the National Institutes of Health will support research that incorporates DNA sequence information into electronic medical records. The goal of research conducted by the Electronic Medical Records and Genomics (eMERGE) network is to better understand the genomic basis of disease and to tailor medical care to individual patients based on their genomic differences.

The grants, administered by the National Human Genome Research Institute (NHGRI), represent   the third phase of the eMERGE program, and focus on moving genomics research closer to clinical application by identifying the potential medical effects of rare genomic variants (inherited differences in the DNA code) in about 100 clinically relevant genes. The activity of such genes can affect a person's health, and might affect treatment choices.

"The newly funded projects are focused on discovering genes and gene variants with clinical implications by using the latest sequencing technologies to examine rare and common variants suspected to relate to disease risk and treatment effects," said Rongling Li, M.D., Ph.D., program director for eMERGE in the Division of Genomic Medicine at NHGRI, a part of NIH. "The other important component of these grants is implementing what researchers learn about these gene variants into medical settings to improve patient care."

Researchers will look at the best ways to provide DNA test results to physicians and patients, she said, and ways in which doctors might use this information to improve clinical treatment and practice. These funded researchers will also examine the psychological and economic effects on patients and families, and the effects on healthcare systems, in using this information...

Full article w/grantee information here. I'm seeing a total of $52,405,228 in awards (I may be overcounting, given the somewhat unclear way the monies are reported).

I direct you to a number of my prior posts on the "Omics" topical issues:

"It's not so elementary, Watson." Developments in Health IT (June 29th)

"Personalized Medicine" and "Omics" -- HIT and QA considerations (July 16th)

Are EHRs obsolete? (Aug 6th)

"Personalized Medicine" - will Health IT be up to the task? (Aug 16th)

Three core (compound) questions, really:

• Will commercial ("clinical") "omics" assays be sufficiently, uniformly accurate and precise?
• Will clinical staff be adequately up to speed on "omics" dx analytics and tx options, and how will all of this fit into workflows?
• Will Health IT capacity and functionality on the street suffice uniformly, or will we have a digital "omics divide" for a long time?

Another question comes to mind. Are we seriously proposing to import raw gene sequence information into EHRs as "structured data" rather than just document-oriented PDF summary reports (e.g., attachments that are essentially faxes by any other name) -- i.e., the the way we now handle imaging study radiologists' "impression" narratives? (I have yet to see my prostate OncoType dx results, but I'm sure it simply comprises a brief summary genetic "impression" written by my urologist's assay vendor's genetic analyst.)

Above, a snippet of genetic sequence "raw data." I rather doubt that many physicians would have any interest in or diagnostic need to see stuff like this. It seems to me that the utility may well run the other way, i.e., correlating the myriad patient data elements routinely captured in EHRs (e.g., Pt Demographics, FH, SH, PMH, CC, Vitals, Active dx's, Active Rx's, HPI, H&P, ROS, Labs/Imaging, specialist consults, etc) with Omics data. The old word "registry" comes to mind.

UPDATE: eMERGE

"The eMERGE Network brings together researchers with a wide range of expertise in genomics, statistics, ethics, informatics, and clinical medicine from leading medical research institutions across the country. Each center participating in the consortium is uniquely situated to provide critical resources to this highly collaborative and productive network. Each site combines abiobank or study cohort with extensive genomic data and access to clinical data derived from electronic medical records. Sites are geographically dispersed and have diverse patient populations, including two sites focusing specifically on pediatrics."

eMERGE is a national network organized and funded by the National Human Genome Research Institute (NHGRI) that combines DNA biorepositories with electronic medical record (EMR) systems for large scale, high-throughput genetic research in support of implementing genomic medicine.  eMERGE studies and pilots genomic medicine translation  through discovery, implementation, tools, and policy.  During Phase I and II, the Network deployed more than 40 electronic phenotype algorithms across more than 55,000 subjects with dense genomic data.  Returning clinical results has been implemented or planned for pilot at sites across the Network.  A large-scale survey of patient attitudes regarding data sharing is being sent to 90,000 clinic patients across the country.  A multicenter pilot of returning genome sequence information to electronic medical records (EMRs) for use in healthcare is almost complete.  Themes of genomics, bioinformatics, genomic medicine, ethnics, data sharing, privacy, and community engagement are of particular relevance to eMERGE.

eMERGE was initiated in 2007 and included five biorepositories linked to EMRs.  The network demonstrated that EMR phenotyping to develop cohorts for genome-wide studies was a robust approach to genetic discovery, defined approaches for enhancing privacy of shared EMR data, and engaged patients and communities in consent and data sharing. eMERGE expanded to include 7 clinical sites in 2011 and 2 pediatric sites in 2012.

eMERGE is openly interested in collaborations.  Current external collaborations include the US Air Force, ENCODE, IGNITE, and the larger ELSI (Ethical, Legal, and Social Issues) community.  eMERGE is dedicated to developing tools, identifying best practices, and communicating results for participant consent, data sharing, and returning genomic research results,to benefit the broader medical and scientific communities and the general public.

Interesting, all of it.
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More to come...

Digital Health IT = "Better Care at Lower Cost." Right?

That was the promise, recall?

Below, I shot this off a slide deck screen at the HIMSS Conference in 2012.

Last Friday while on my way home from my Muttville.org weekly volunteer day in San Francisco, I heard an interesting NPR "Intelligence Squared" debate on the question "should corporate subsidies be eliminated?"

ABOUT THIS EVENT
The auto industry, agriculture, the energy sector. What do they have in common? These industries benefit from government subsidies in the form of loans, tax breaks, regulation, and other preferences. Critics from the left and right say that not only do these subsidies transfer wealth from taxpayers to corporations, they distort the markets and our economy. Proponents say that government has an important role to play in launching innovation via strategic investment, and its support helps American companies thrive. Do we need subsidies, or is this corporate welfare?

From the transcript:

[applause]

And that motion is Eliminate Corporate Subsidies and here to make his closing statement against the motion, Michael Lind, policy director of the economic growth program at New America and author of "Land of Promise."

Michael Lind:
Medical errors are the third leading cause of death in the United States. Up to 440,000 Americans die each year from preventable medical errors. A lot of it because the lag with doctor's offices using paper records. As part of the stimulus act in 2009, the federal government has spent to date more than $35 billion in incentives to individual doctors, hospitals, and other health care providers. What's the result? In 2009, those using -- the physicians using electronic health records were only 21.8 percent. Today, a few years later, they are 78 percent. The subsidy worked. In 2009, only 12.2 percent of non-federal acute care hospitals were using a basic computer electronic health record system. By 2014, after this $30 billion had been spent, more than 76 percent were. This is a tremendous unsung success of federal policy. Now, there are problems with the next stage.

There are problems with interoperability, with monopolistic corporations engaged in so- called data blocking. So there are problems with it. But, you know, this sort of really brings it home. As a result of this particular highly successful federal subsidy, your loved ones or your own life may be saved and as long as there are a few programs like this that are successes, you need to vote against the motion.

John Donvan:
Thank you, Michael Lind.

[applause]

Well, myriad critics would dispute the assertion that the HITECH "Meaningful Use" initiative has turned out to be a "highly successful federal subsidy." Mr. Lind's citing of post-MU accelerated HIT uptake does nothing to answer the "better care at lower cost" question. Many critics argue that MU has in fact stifled innovation, and has largely served to line the pockets the large incumbent EHR vendors, while perhaps even having an adverse effect on care delivery, via clinically irrelevant "productivity treadmill" compliance imperatives.

Beyond issues of uniform, appropriate operational definitions of "better" and "lower cost," I continue to regard it as premature to declare either "failure" or "victory" in this area. As I observed a year ago in my March 2015 HITECH Interoperababble Update post:

I have noted before what I call "Health IT Policy ADHD." Major legislation gets passed and funded, and when we don't get immediate, dazzling results, we go sour on it, lamenting its "failure," and calling for its demise. HITECH is not that old. There have really only been four years of full-bore boots-on-the-ground operation. REC contracts were let in 2010, and the RECs spent most of their first year getting their sea legs under them and scurrying about hustling skeptical clinical participants...

We never tire of citing the "information superhighway" analogy. Fine. The U.S. Interstate Highway system took more than 35 years to complete. Significantly and sustainably transforming the incredibly complex, heterogeneous U.S. healthcare delivery and information infrastructure easily rivals it in scale and exceeds it in complexity by orders of magnitude. Moreover, this $35 billion outlay comes to about $22 per capita per year since the deployment of the HITECH program five years ago. The latest NHE per capita expenditure is about $10k. $22 is about 0.2% of that (0.0022). Close to being a rounding error...

Some other vexingly inhibitory factors go to the byzantine, dysfunctional economic imperatives of organizational structures and cultures in the health care space. See my accruing rant The U.S. healthcare "system" in one word: "shards."

On healthcare workforce cultural dysfunction specifically, recall my post The "Talking Stick" and the three-legged stool of sustained, transformative healthcare QI.

Socioeconomic issues: “When it comes to health, your zip code matters more than your genetic code.”

Relatedly, The future of health care, continued. Where will economics come in?

Then, there are more fundamental concerns, going to the effects of digitization on clinical cognition per se. See Are structured data now the enemy of health care quality?

I've also set forth my concerns regarding "personalized medicine," which necessarily include the various "omics" scientific disciplines. See, e.g., Omics update: National Human Genome Research Institute Health IT news.

So many contending, frequently mutually-negating moving parts, so little time.

Now comes a very big new wrinkle.

This book has been an utter delight to read, at once scholarly, scientific, accessible, and conversationally genial. The implications for health and health care delivery could not be more important, IMO. Seems like I spent half of the weekend reading parts of it aloud to my wife. I bought the Kindle edition, and have also just ordered a hardcopy for her and my daughter.

PREFACE
...I have made the human body the focus of my career. In fact, I am extremely lucky to be a professor at Harvard University, where I teach and study how and why the human body is the way it is ... I study fossils, I travel to interesting corners of the earth to see how people use their bodies, and I do experiments in the lab on how human and animal bodies work...

...of all the questions I am commonly asked, the one I used to dread the most was “What will human beings look like in the future?” ... My reflexive answer was always something along the lines of: “Human beings aren’t evolving very much because of culture.” This response is a variant of the standard answer that many of my colleagues give when asked the same question.

I have since changed my mind about this question and now consider the human body’s future to be one of the most important issues we can think about. We live in paradoxical times for our bodies. On the one hand, this era is probably the healthiest in human history. If you live in a developed country, you can reasonably expect all your offspring to survive childhood, to live to their dotage, and to become parents and grandparents. We have conquered or quelled many diseases that used to kill people in droves: smallpox, measles, polio, and the plague. People are taller, and formerly life-threatening conditions like appendicitis, dysentery, a broken leg, or anemia are easily remedied. To be sure, there is still too much malnutrition and disease in some countries, but these evils are often the result of bad government and social inequality, not a lack of food or medical know-how.

On the other hand, we could be doing better, much better. A wave of obesity and chronic, preventable illnesses and disabilities is sweeping across the globe. These preventable diseases include certain cancers, type 2 On the other hand, we could be doing better, much better. A wave of obesity and chronic, preventable illnesses and disabilities is sweeping across the globe. These preventable diseases include certain cancers, type 2 diabetes, osteoporosis, heart disease, strokes, kidney disease, some allergies, dementia, depression, anxiety, insomnia, and other illnesses. Billions of people are also suffering from ailments like lower back pain, fallen arches, plantar fasciitis, myopia, arthritis, constipation, acid reflux, and irritable bowel syndrome. Some of these troubles are ancient, but many are novel or have recently exploded in prevalence and intensity. To some extent, these diseases are on the rise because people are living longer, but most of them are showing up in middle-aged people. This epidemiological transition is causing not just misery but also economic woe. As baby boomers retire, their chronic illnesses are straining health-care systems and stifling economies. Moreover, the image in the crystal ball looks bad because these diseases are also growing in prevalence as development spreads across the planet.

The health challenges we face are causing an intense worldwide conversation among parents, doctors, patients, politicians, journalists, researchers, and others. Much of the focus has been on obesity. Why are people getting fatter? How do we lose weight and change our diets? How do we prevent our children from becoming overweight? How can we encourage them to exercise? Because of the urgent necessity to help people who are sick, there is also an intense focus on devising new cures for increasingly common noninfectious diseases. How do we treat and cure cancer, heart disease, diabetes, osteoporosis, and the other illnesses most likely to kill us and the people we love?

As doctors, patients, researchers, and parents debate and investigate these questions, I suspect that few of them cast their thoughts back to the ancient forests of Africa, where our ancestors diverged from the apes and stood upright. They rarely think about Lucy or Neanderthals, and if they do consider evolution it is usually to acknowledge the obvious fact that we used to be cavemen (whatever that means), which perhaps implies that our bodies are not well adapted to modern lifestyles. A patient with a heart attack needs immediate medical care, not a lesson in human evolution.

If I ever suffer a heart attack, I too want my doctor to focus on the exigencies of my care rather than on human evolution. This book, however, argues that our society’s general failure to think about human evolution is a major reason we fail to prevent preventable diseases. Our bodies have a story— an evolutionary story— that matters intensely. For one, evolution explains why our bodies are the way they are, and thus yields clues on how to avoid getting sick. Why are we so liable to become fat? Why do we sometimes choke on our food? Why do we have arches in our feet that flatten? Why do we have backs that ache? A related reason to consider the human body’s evolutionary story is to help understand what our bodies are and are not adapted for. The answers to this question are tricky and unintuitive but have profound implications for making sense of what promotes health and disease and for comprehending why our bodies sometimes naturally make us sick. Finally, I think the most pressing reason to study the human body’s story is that it isn’t over. We are still evolving. Right now, however, the most potent form of evolution is not biological evolution of the sort described by Darwin, but cultural evolution, in which we develop and pass on new ideas and behaviors to our children, friends, and others. Some of these novel behaviors, especially the foods we eat and the activities we do (or don’t do), make us sick...

The core subjects of this book— human evolution, health, and disease— are enormous and complex. I have done my best to try to keep the facts, explanations, and arguments simple and clear without dumbing them down or avoiding essential issues, especially for serious diseases such as breast cancer and diabetes...

...I have rashly concluded the book with my thoughts about how to apply the lessons of the human body’s past story to its future. I’ll spill the beans right now and summarize the core of my argument. We didn’t evolve to be healthy, but instead we were selected to have as many offspring as possible under diverse, challenging conditions. As a consequence, we never evolved to make rational choices about what to eat or how to exercise in conditions of abundance and comfort. What’s more, interactions between the bodies we inherited, the environments we create, and the decisions we sometimes make have set in motion an insidious feedback loop. We get sick from chronic diseases by doing what we evolved to do but under conditions for which our bodies are poorly adapted, and we then pass on those same conditions to our children, who also then get sick. If we wish to halt this vicious circle then we need to figure out how to respectfully and sensibly nudge, push, and sometimes oblige ourselves to eat foods that promote health and to be more physically active. That, too, is what we evolved to do.

Lieberman, Daniel (2013-10-01). The Story of the Human Body: Evolution, Health, and Disease. Knopf Doubleday Publishing Group. Kindle Edition, locations 41-99.

Boy, does he ever deliver across the full span of the book. A must-read, in my view.

Some triangulation.

I came to this book by way of ScienceBasedMedicine.org, one of my requisite daily stops.

Human life has changed immensely over the millennia, but never so much or so quickly as in the past century. For almost the entire 200,000-year existence of our species, Homo sapiens, biology controlled us. We gathered fruits, nuts, and plants; hunted and fished for the animals that were available; and like the wildebeest or zebra, we moved on when resources ran low. Even after the advent of farming and civilization, and the development of cities, we were still very vulnerable to the whims of the weather, and to famine and epidemics. 

But in just the past hundred years or so, we have turned the tables and taken control of biology. Smallpox, a virus that killed as many as 300 million people in the first part of the twentieth century (far more than in all wars combined) has not merely been tamed but has been eradicated from the planet. Tuberculosis, caused by a bacterium that infected 70– 90 percent of all urban residents in the nineteenth century and killed perhaps one in seven Americans, has nearly vanished from the developed world. More than two dozen other vaccines now prevent diseases that once infected, crippled, or killed millions, including polio, measles, and pertussis. Deadly diseases that did not exist in the nineteenth century, such as HIV/ AIDS, have been stopped in their tracks by designer drugs. 

Food production has been as radically transformed as medicine. While a Roman farmer would have recognized the implements on an American farm in 1900— the plow, hoe, harrow, and rake— he would not be able to fathom the revolution that subsequently transpired. In the course of just one hundred years, an average yield of corn more than quadrupled from about 32 to 145 bushels per acre. Similar gains occurred for wheat, rice, peanuts, potatoes, and other crops. Driven by biology, with the advent of new crop varieties, new livestock breeds, insecticides, herbicides, antibiotics, hormones, fertilizers, and mechanization, the same amount of farmland now feeds a population that is four times larger, but that is accomplished by less than 2 percent of the national labor force compared to more than 40 percent a century ago. 

The combined effects of the past century’s advances in medicine and agriculture on human biology are enormous: the human population exploded from fewer than 2 billion to more than 7 billion people today. While it took 200,000 years for the human population to reach 1 billion (in 1804), we are now adding another billion people every twelve to fourteen years. And, whereas American men and women born in 1900 had a life expectancy of about forty-six and forty-eight years, respectively, those born in 2000 have expectancies of about seventy-four and eighty years. Compared to rates of change in nature, those greater than 50 percent increases in such a short timespan are astounding...

Diseases, it turns out, are mostly abnormalities of regulation, where too little or too much of something is made. For example, when the pancreas produces too little insulin, the result is diabetes, or when the bloodstream contains too much “bad” cholesterol, the result can be atherosclerosis and heart attacks. And when cells escape the controls that normally limit their multiplication and number, cancer may form. 

To intervene in a disease, we need to know the “rules” of regulation...

Carroll, Sean B. (2016-02-16). The Serengeti Rules: The Quest to Discover How Life Works and Why It Matters (Kindle Locations 96-130). Princeton University Press. Kindle Edition.

"To intervene in a disease, we need to know the “rules” of regulation."

Yeah, and Dr. Lieberman would say that we need to look more closely at the implications of the broad span of human evolution in order to effectively manage, mitigate, and/or cure what he calls today's "diseases of evolutionary mismatch." Absent that contextual grounding, we may well do everything else (including HIT deployment and process QI) as efficaciously as possible and still come up short.

BTW, tangentially, a bit more "evolution" triangulation.

Michael Tomasello sets forth a pretty compelling case for the evolutionary adaptive utility of prosocial, empathic, and altruistic inclinations and behaviors. My summary excerpts here.

Ayn Randians will have a cow.

Dr. Lieberman:

...For millions of years, our ancestors relied on innovation and cooperation to get enough food, to help care for one another’s children, and to survive in hostile environments, such as deserts, tundras, and jungles. Today we need to innovate and cooperate in new ways to avoid eating too much food, especially excess sugar and processed industrial foods, and to survive in cities, suburbs, and other unnatural environments. We therefore need government and other social institutions on our side, because we never evolved to choose healthy lifestyles. Most people don’t get sick through any fault of their own, but instead they acquire chronic illnesses as they age because they grew up in an environment that encourages, entices, and sometimes even forces them to become sick. For many of these diseases, we can then only treat the symptoms. Unless we want to end up as a species ever more dependent on medicines and expensive technologies to cope with the symptoms of preventable diseases, we need to change our environments. In fact, it is questionable whether we can continue to afford the cost of our current trajectory of increased longevity and population sizes combined with increased chronic morbidity. 

I think it is reasonable to conclude that cultural evolutionary processes today are gradually replacing one form of coercion with another. For millions of years, our ancestors were required to consume a naturally healthy diet and to be physically active. Cultural evolution, especially since humans began farming, has transformed how our bodies interact with the environment. Many people today still live in poverty and suffer from diseases caused by poor sanitation, contagion, and malnutrition that were much less common in the Paleolithic. Those of us fortunate enough to live in the developed world have escaped those miseries, and we can now choose to be inactive as much as we want and eat whatever we crave. In fact, for some, such habits are the default setting. Those choices or urges, however, often make us sick in other ways, which then compel us to treat our symptoms. Right now, we are generally satisfied with the system we have created, thanks to long life spans and overall decent health. But we could do better. And as the mismatch environments we have created and pass on to our children through the pernicious feedback loop of dysevolution intensify, we increase our risk of suffering from needless, preventable diseases. [Lieberman, op cit, pp. 364-365].

I would make Daniel Lieberman's book required reading in Med School. Buy it and study it ASAP.

BTW, I came to the book here, at The Daily Beast.

CODA

Count me a fan of Gould's "Drunkard's Walk" theory of evolution.

Before the advent of rapid, accurate, and inexpensive DNA sequencing technology in the early 2000s, biologists guessed that genes would provide more evidence for increasing complexity in evolution. Simple, early organisms would have fewer genes than complex ones, they predicted, just as a blueprint of Dorothy’s cottage in Kansas would be less complicated than one for the Emerald City. Instead, their assumptions of increasing complexity began to fall apart. First to go was an easy definition of how complexity manifested itself. After all, amoebas had huge genomes. Now, DNA analyses are rearranging evolutionary trees, suggesting that the arrow scientists envisioned between simplicity and complexity actually spins like a weather vane caught in a tornado...

With comb jellies at the base of the tree, evolution suddenly seems less like a march towards complexity and more like a meandering stroll. This isn’t a new idea. Back in 1996, evolutionary biologist Stephen Jay Gould posited that evolution progresses like a drunkard’s walk. Organisms, he said, stand an equal chance of becoming simpler or more complex over millions of years—although sometimes there’s a lower limit on how simple they can possibly be, just as a drunk may fall into a gutter at the far left side of the road. An Internet meme even celebrates oddities that result from evolution’s stumble: “ Go Home Evolution, You’re Drunk,” features organisms with sub-optimal traits that have managed to survive just fine...

Perhaps the fact that people are stunned whenever organisms become simpler says more about how the human mind organizes the world than about evolutionary processes. People are more comfortable envisioning increasing complexity through time instead of reversals or stasis. Physicist Sean Carroll calls humans “ terrible temporal chauvinists” for this reason, because they desperately want the street from the past to the future to run in one direction. The textbook scenarios on early animal evolution might be correct, but they should be treated as hypotheses built by temporal chauvinists. When new data suggests a rearrangement, it must be considered no matter how perplexing the conclusion seems.

Casey Dunn, an evolutionary biologist at Brown University in Providence, R.I. who took part in the still-contentious comb jelly project, now doubts all notions of increasing complexity. Instead, he says the environment selects whatever form handles the challenges at hand, be it simple, complex, or plain ugly.  Mother Nature, with her 4 billion years of experience, does not work like Steve Jobs, continuously designing sleeker versions. When asked whether de-evolution, a reversal from the complex to the simple, happens frequently, Dunn replies, sure. “But,” he adds, “I wouldn’t call that de-evolution, I’d call it evolution.”

The "bush of life" rather than "the tree of life" metaphor. I just like the "Occam's Razor" simplicity as it applies to evolution. You need assume only three things, all of which exist: [1] simple carbon-based organisms capable of reproducing, [2] a relatively stable environment with a reliable source of energy input, and [3] a lot of time. You need not anthropomorphically assume "purpose," "intentionality," an evolutionary "drive toward complexity."

Pop the clutch, and 4.7 billion years later you might end up with us (along with the enormous volume of single-celled microbial life that still accounts for the bulk of planetary biota). Re-run the experiment and you probably get something unrecognizably different (Dr. Lieberman even generally alludes to this likelihood).

All the more reason to treat life with reverence.

PS-

Put up a short post, one pointing back here, over at the new Medium.com publishing platform. See The underappreciated evolutionary factors that bear on human health and impede effective modern health care

Just trying out the Medium.com platform. It's OK. I have my doubts as to their business model.
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More to come...

"Health Data Outside the Doctor's Office" - My latest THCB comment

Yeah. My comment:

@BobbyGvegas says:

“In fact, a minority of our overall health is the result of the health care we receive. If we’re to have an accurate picture of health, we need more than what is currently captured in the electronic health record.”
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Minority indeed. Only ~10% by some estimates. Most of the causal and contributory factors are “upstream.” But, beyond the often politically radioactive socioeconomic factors, included in that upstream estimate are the huge sets of “omics,” which many HIT people want to see included in next generation EHRs. Current CHPL certified ambulatory EHRs today house perhaps 3,500 – 4,000 variables or more “under the hood” in the RDBMS tables and schema. Adding “omics” data to those arrays will be problematic absent [1] a transformative shift the the current payment paradigm, and [2] widespread clinician competence with respect to accurately including “omics” data in the dx and tx. A typical 99213 visit today requires a fleeting time-constrained “access/view/update/append/transmit” drive-by of the several hundred (or more) variables that go into the SOAP and progress note (many of which are pro-forma in order to get paid, which goes to point 1). I will soon be 69. I’m a 99213, so this is no abstraction for me.

The cost of “omics” assays is coming down dramatically. Their irreducible analytical and predictive complexity will remain. For Old Coots in Training like me, the “omics” horse is likely largely already out of the barn. Mandating their inclusion, given the age-related pt encounter UTIL may well be a net loser, writ large.
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“Data sharing is a critical piece of this equation. While we need infrastructure to capture and organize this [sic] data, we also need to ensure that individuals, health care professionals and community leaders can access and exchange this [sic] data, and use it to make decisions that improve health.”
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No small undertaking. We need to figure out how move beyond the fundamental “Opacity = Margin” market imperative. I’m midway through the new Schmidt-Rosenberg book “How Google Works” (which I will soon cite and review on my blog re its import for Health IT). While they laud the Google ethos of “Default of Open,” they’re not about to publish their search and ad placement algorithms. They note that such a stance opens them to charges of hypocrisy, but, so be it.

“With a few exceptions, Google defaults to open, and for these exceptions we are often criticized as being hypocritical, since we preach open in some areas but then sometimes ignore our own advice. This isn’t hypocritical, merely pragmatic. While we generally believe that open is the best strategy, there are certain circumstances where staying closed works as well.”

Schmidt, Eric; Rosenberg, Jonathan (2014-09-23). How Google Works (Kindle Locations 1163-1166). Grand Central Publishing. Kindle Edition.

I guess I should have more accurately noted that "omics" data are considered "upstream" only to the extent that they largely remain pretty much "outside the doctor's office." The other principal "upstream" factors include, in addition to socioeconomic and environmental metrics, issues of "lifestyle" All of these latter factors are correlated to a significant degree.

BTW: I'll be attending and reporting on the event below this Thursday.

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I got onto the "How Google Works" book by way of a New Yorker article when the latest issue showed up in my snailmail box the other day.

When G.M. Was Google
The art of the corporate devotional.

...What’s Google’s secret? This is an irresistible question, because Google is the most successful new business corporation of the twenty-first century. Still only fifteen years old, it is worth about three hundred and eighty billion dollars; its revenues are more than fifty billion dollars a year, and around a quarter of that is profit. More than a billion people perform a Google search every month. It’s natural to wonder whether there’s something each of us can do to emulate Google, with directionally similar, if perhaps more modest, results. What makes the Google model especially alluring is that, as Page and Sergey Brin put it in the statement that accompanied their initial public offering, ten years ago, “Google is not a conventional company.” Getting very rich is always fascinating, but getting very rich while proclaiming that you’re breaking the rules about how to run a business is even more so...

Epic is reported to have earned $1.7 billion in 2013. Maybe Google could just buy them and open them up. They could pay cash and never miss the money.

From "How Google Works"

Three powerful technology trends have converged to fundamentally shift the playing field in most industries. First, the Internet has made information free, copious , and ubiquitous— practically everything is online. Second, mobile devices and networks have made global reach and continuous connectivity widely available. And third, cloud computing 10 has put practically infinite computing power and storage and a host of sophisticated tools and applications at everyone’s disposal, on an inexpensive, pay-as-you-go basis...

Today the components are all about information, connectivity, and computing. Would-be inventors have all the world’s information, global reach, and practically infinite computing power. They have open-source software and abundant APIs that allow them to build easily on each other’s work. They can use standard protocols and languages. They can access information platforms with data about things ranging from traffic to weather to economic transactions to human genetics to who is socially connected with whom , either on an aggregate or ( with permission) individual basis. So one way of developing technical insights is to use some of these accessible technologies and data and apply them in an industry to solve an existing problem in a new way. Besides these common technologies, each industry also has its own unique technical and design expertise. We have always been involved in computing companies, where the underlying technical expertise is computer science. But in other industries the underlying expertise may be medicine, mathematics, biology, chemistry, aeronautics, geology, robotics, psychology, logistics, and so on...

Schmidt, Eric; Rosenberg, Jonathan (2014-09-23). How Google Works (Kindle Locations 176-180, 972-2980). Grand Central Publishing. Kindle Edition.

Recall my earlier post citing Dr. Jerome Carter's "EHR Science."

Looking at clinical care and its computing needs, I see requirements that are distinct when compared to standard business computing. Clinical data are varied and numerous. Clinical work consists of interacting with patients to obtain information, consulting information sources  (e.g., chart, guidelines, articles, other clinicians), making decisions, recording information, and moving on. Support for clinical work requires large, searchable data stores, fast networks, sophisticated communications functionality, and portable computers capable of displaying text, pictures, sound and video. Tablets and smartphones are the first computers to meet all of these requirements.

Writing for mobile means stepping back from web and client/server applications and being willing to see a problem purely from the standpoint of mobile computing; that is, adopting a “mobile first” attitude.

Mobile first requires a willingness to rethink past approaches. At the top of the list is use of cloud capabilities. Like mobile computers, the cloud is a new way of doing things. Building mobile applications that link to cloud storage and use APIs to interact with other applications is a new way of delivering functionality. There is no reason to have local terminology services if they can be obtained via a cloud application. The same is true of workflow engines or another service that supports clinical work. Mobile first also means not taking a client/server app and putting a mobile face on it. That will not work any better than putting a browser interface on a standard desktop app. It might work to some extent, but the original design limitations will show through.

Until the 64-bit chips arrived, the amount of computing power in mobile systems made them useful only for limited applications. However, Apple has shown in its A8X chip that tablets and smartphones are rapidly gaining sufficient computing power and communications capability to make serious clinical applications possible in a way that has never existed–and this is only the chip’s second generation! The fourth generation will appear in 24 months, if Apple sticks to form. What will those systems be capable of doing?

How many EHR vendors will bite the bullet and start serious mobile-first projects? Few, I imagine, because if the past is prologue, most will cling to the prevailing wisdom that mobile devices are not real computers. And we know how that story ends…

WEDNESDAY MORNING UPDATE

 Finished "How Google Works" yesterday afternoon. A fun read. A few of the authors' closing thoughts:

We see most big problems as information problems, which means that with enough data and the ability to crunch it, virtually any challenge facing humanity today can be solved. We think computers will serve at the behest of people— all people— to make their lives better and easier. And we are quite sure that we, as a couple of Silicon Valley guys, will come under a lot of criticism for this Pollyannaish view of the future. But that doesn’t matter. What matters is that there is a bright light at the end of the tunnel.

There are solid reasons underlying our optimism . The first is the explosion of data and a trend toward the free flow of information. From geological and meteorological sensors to computers that record every single economic transaction to wearable technology (such as Google’s smart contact lenses) 210 that continuously tracks a person’s vital signs, types of data are being collected that simply have never been available before, at a scale that was the stuff of science fiction only a few years ago. And there is now practically limitless computing power with which to analyze that data. Infinite data and infinite computing power create an amazing playground for the world’s smart creatives to solve big problems.

This will result in greater collaboration among smart creatives— scientists, doctors, engineers, designers , artists— trying to solve the world’s big problems, since it is so much easier to compare and combine different sets of data. As Carl Shapiro and Hal Varian note in Information Rules, information is costly to produce but cheap to reproduce. So if you create information that can help solve a problem and contribute that information to a platform where it can be shared (or help create the platform), you will enable many others to use that valuable information at low or no cost. Google has a product called Fusion Tables, which is designed to “bust your data out of its silo” by allowing related data sets to be merged and analyzed as a single set, while still retaining the integrity of the original data set. Think of all the research scientists in the world working on similar problems, each with their own set of data in their own spreadsheets and databases. Or local governments trying to assess and solve environmental and infrastructure issues, tracking their progress in systems sitting on their desks or in the basement. Imagine the power of busting down these information silos to combine and analyze the data in new and different ways...

And the advent of networks is giving rise to greater collective wisdom and intelligence . When reigning world champ Garry Kasparov lost his chess match to IBM’s Deep Blue computer in 1997, we all thought we were witnessing a seminal passing of the torch. But it turns out that the match heralded a new age of chess champions: not computers, but people who sharpen their skills by collaborating with computers. Today’s grandmasters (and there are twice as many now as there were in 1997 ) use computers as training partners, which makes the humans even better players. Thus a virtuous cycle of computer-aided intelligence emerges: Computers push humans to get even better, and humans then program even smarter computers. This is clearly happening in chess; why not in other pursuits?

Schmidt, Eric; Rosenberg, Jonathan (2014-09-23). How Google Works (Kindle Locations 3358-3390). Grand Central Publishing. Kindle Edition. 

Think "Watson." That latter paragraph also goes straight to Dr. Weed, et al. See, e.g., my earlier post "Back down in the Weeds': A Complex Systems Science Approach to Healthcare Costs and Quality." And, my first post on Larry Weed, blogged nearly two years ago: "Down in the Weeds'."

Continuing with Eric Schmidt and Jonathan Rosenberg:

The future’s so bright…

It is hard for us to look at an industry or field and not see a bright future. In health care, for example, real-time personal sensors will enable sophisticated tracking and measurement of complex human systems. Combine all that data with a map of risk factors generated by in-depth genetic analysis , and we will have unprecedented abilities (only with an individual’s consent) to identify and prevent or treat individual health issues much earlier. Aggregating that data can create platforms of information and knowledge that enable more effective research and inform smarter health-care policies.

Health-care consumers suffer from a dearth of information: They have virtually no data on procedural outcomes and doctor and hospital performance, and often have a hard time accessing their own health data, especially if it is held by different institutions. And pricing for medical services, medicine, and supplies is completely opaque and varies widely from patient to patient and facility to facility . Just bringing even a basic level of information transparency to health care could have a tremendous positive impact, lowering costs and improving outcomes... [ibid, Kindle Locations 3391-3399]

An excellent book. (It's only $3.75 in Kindle edition at Amazon.) I share their optimism. Guardedly. The comments are piling up in the Health Care Blog post that launched this one: "Health Data Outside the Doctor's Office." Check them out. Lots of cynicism out there, much of it openly hostile.

Here's one of the better ones.

One answer I have for the cynics can be found in my recent post "Physician, Health Thy System."

We also do well to stay mindful of the thoughtful caveats proffered in Nicholas Carr's excellent book "The Glass Cage," a topic of my October 27th, 2014 post. See also my citation of Simon Head's book "Mindless: Why Smart Machines Are Making Dumber Humans."

No shortage of diligent and cautious work to be done in the Health IT space. No shortage of dots to be firmly connected. We will need legions of Schmidt's and Rosenberg's "smart creatives" at their best.

CODA

Not As Loony As It Sounds
Google’s “impossible" plan to beam Internet from solar-powered balloons is actually working. Here’s how.  

The majority of people in the world lack access to the Internet. Either they can’t afford a connection, or none exists where they live. Of all the efforts to bring those people online, Google’s “Project Loon” sounds like the most far-fetched. At the secretive Google X labs, it’s a moonshot among moonshots.

When the search company announced in June 2013 that it was building “Wi-Fi balloons” to blanket the world’s poor, remote, and rural regions with Internet beamed down from the skies, expert reaction ranged from skeptical to dismissive—with good reason. The plans called for Google to put hundreds of solar-powered balloons in the air simultaneously, each coordinating its movements in an intricate dance to provide continuous service even as unpredictable, high-speed winds buffeted them about the stratosphere.

“Absolutely impossible,” declared Per Lindstrand, a Swedish aeronautical engineer and perhaps the world’s best-known balloonist, in an early Wired article about the project...

AFTERWORD

"Who could have conceived that the Information Super Highway would usher in a new Dark Age; a return to blind faith over science, cant over reason?"

The unilateral rejection of facts may be the ultimate metaphor and irony of the Information Age; the more official the source the less likely it is to be believed.

Skepticism is as old as king and country. History itself has been aptly described as an argument un-ended. But there is a profound difference between revisionist history based on new evidence and evolving social mores and the rejection of facts.

In our digital world, all the accumulated knowledge of human history is available in the palm of our hands. But intermingled with hard-won truths are half-baked theories and outright lunacy—decorated with footnotes, graphs, pie charts, and citations from credentialed “experts”—proving that the Earth is warming, the Earth is cooling, or the Earth is flat.

If you seek it, you’ll find it. That’s the problem.

We are overwhelmed with data from every quarter, and our capacity to filter fact from fraud is limited. But the web never rests. Men and women of good intent who simply seek “the truth” upon which to base their opinions find themselves awash in folderol.

No longer confident in any single source for simple truths, more and more of us today are choosing to believe what we are predisposed to believe, period. Contravening facts are dismissed as lies or propaganda.

In a more circumspect time Daniel Patrick Moynihan famously said, “You are entitled to your own opinion, but you are not entitled to your own facts.” In the dotcom world we all have our own facts...

From The Facts About Ferguson Matter, Dammit
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More to come...

"Human Nature"

From Science Magazine:

CRISPR comes to the silver screen
If you could cure a child suffering from sickle cell anemia, would you? This is the question at the center of Human Nature, a new documentary directed by Adam Bolt about the revolutionary gene-editing technology known as CRISPR. The documentary is visually stunning, thought-provoking, informative, and tightly focused on human health—which means that there are a few pieces missing from the overall picture.

"Omics" update stuff. Documentary website here.

UPDATE: ANOTHER VIDEO ON THE TOPIC

NOTE: Some of this is not in English, you might want to click 'subtitles/CC."
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More to come...

Cumulative Meaningful Use attestations update

 

These comprise accrued data through March 2015. Notice that 2014 attestation incentive payments were roughly only half those of 2013 (51.4%). $30 billion paid out total.

Pretty much all that remain are reimbursement penalties.

UPDATE

Attestations to date by EHR vendors, via Healthcare IT News.

No surprises here, 'eh?

COMING UP

My newest read.

There are major implications for Health IT.

Just as we are getting to grips with the idea of sequencing millions of genomes, evidence is suggesting that even one per person might not be enough. The dogma that each of us has one genome to sequence is crumbling under the weight of evidence. It seems that we might be genomic mosaics and the new paradigm could be ‘one human, multiple genomes’.

The most common source of our multiple human genomes is cancer. Genetic disease is conventionally thought to arise from inherited genetic lesions found in the germ line— the sperm and eggs that combine to form the first human cells from which we all grow. In contrast, cancer is a disease that can arise from genetic mutations occurring within cells in the body— somatic cells (for soma, meaning body). Cancerous cells are aggressive in their attempts to grow and spread to places they are not meant without permission.

We all possess precancerous or slow-growing cancerous cells. In an autopsy study of six individuals, high rates of cellular mosaicism were found across different tissues. Mosaics were classified as having one or more large insertions, deletions, or duplications of DNA compared to the original ‘parent genome’ created at conception.

Mosaicism goes far beyond cancer. An increasing number of somatic mutations are being linked to other genetic diseases. These include neurodevelopmental diseases that can arise in prenatal brain formation and cause recognizable symptoms even when present at low levels. Brain malformations associated with these changes are linked to epilepsy and intellectual disability.

Humans can also be mosaics of ‘foreign’ genomes. Rare cases of confounded identities brought to light the first examples. In one case, a woman needing a kidney transplant did not genetically match her children; her kidney grew from the cells of her lost twin brother. In another case, the identity of a criminal was masked because cells from his bone marrow transplant had migrated into the lining of his cheek. Cheek swabs were taken for his DNA test. Even more remarkable, observations suggest that many women who have been pregnant might be genomic chimeras. In samples from brain autopsies of 59 women, for example, 63 per cent of neurons contained Y chromosomes originating from their male offspring (actually from the fathers).

Doctors and geneticists are just starting to explore what having a multiplicity of genomes means for human health. At this point they are busy mapping the extent of the phenomenon but the message is already loud and clear: genomics continues to astonish us and genomic diversity is appearing everywhere we imagine to look, including inside our own bodies.

Beyond genomics, epigenomics is perhaps an even higher mountain of diversity to scale. Genomes might be relatively static entities at the level of their nucleotides A, C, G, and T, but the double helix can be decorated in numerous ways that change how genes are turned on and off, and in which combinations.

In essence, exactly the same genome sequence can have very different effects depending on its history and context. Gene expression patterns can change frequently, and in some cases the modifications are even passed on to the next generation. It never ends. Human genetic variation continues to blindside us with its enormity and complexity.”

Field, Dawn; Davies, Neil (2015-01-31). Biocode: The New Age of Genomics (pp. 33-34). Oxford University Press. Kindle Edition. 

I have a couple of concerns. Docs often don’t have enough time TODAY to get through an electronic SOAP note effectively, given workflow constraints. Adding in torrents of “omics” data may be problematic, both in terms of the sheer number of additional potential dx/tx variables to be considered in a short amount of time, and questions of “omics” analytic competency. To that latter point, what will even constitute dx "competency" in the individual patient dx context, given the relative infancy of the research domain? (Not to mention issues of genomic lab QC/QA -- a particular focus that I will have, in light of my 80's lab QC background).

President Obama’s current infatuation with “Precision Medicine” notwithstanding, just dumping bunch of “omics” data into EHRs (insufficiently vetted for accuracy and utility, and inadequately understood by the diagnosing clinician) is likely to set us up for our latest HIT disappointment -- and perhaps injure patients in the process.

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More to come...

On deck: the Health 2.0 Technology for Precision Health Summit

Imagine if you will, a future in which a cancer diagnosis will be treated with a lifestyle change, like a chronic condition. Survivable. Manageable. Like Diabetes. Sure, to receive a cancer diagnosis today does not mean what it meant twenty years ago, but we are also unlikely to reach a point of ever acting casual about the term or the treatment plan.

In the meantime though, the increasing prevalence of personal data collection is driving new approaches in care plans that have a real shot at improving quality of life. The narrative of one's life can be seen in the data
- everything from where you live, what you eat, how you workout, even what you search for on the internet. The sources of such personal data come from places like clinical trials, biosensors, and wearables and is being stored in your Electronic Medical Record.

The sticking point though is the advancement of technological tools to view, aggregate, extract, and analyze relevant data to derive a meaningful plan of attack (er, treatment plan). One interoperable tool that plugs right into the EMR is Cota Healthcare. Pair this with omics data and genome sequencing technology, like 2bPrecise, and physicians are gaining insight into what makes you, you. And thus are better able to customize a bespoke cancer treatment plan, designed for you and only you.

Learn more about how omics data is driving new care plans, and see a live demo from Cota Healthcare and others at the Technology for Precision Health Summit next week in San Francisco.

Why wait. Register today for next week's event and save 50% on the ticket by using discount code TPH50.

Hope to see you there. Hashtag #tph2017.

"Omics," 'eh? See my prior "Personalized Medicine" and "Omics" -- HIT and QA considerations."

UPDATE

apropos of my prior post "Science, intellectual property, taxpayer funding, and the law."

Why a lot of important research is not being done
Aaron Carroll MD

We have a dispiriting shortage of high-quality health research for many reasons, including the fact that it’s expensive, difficult and time-intensive. But one reason is more insidious: Sometimes groups seek to intimidate and threaten scientists, scaring them off promising work.

By the time I wrote about the health effects of lead almost two years ago, few were questioning the science on this issue. But that has not always been the case. In the 1980s, various interests tried to suppress the work of Dr. Herbert Needleman and his colleagues on the effects of lead exposure. Not happy with Dr. Needleman’s findings, the lead industry got both the federal Office for Scientific Integrity and the University of Pittsburgh to conduct intrusive investigations into his work and character. He was eventually vindicated — and his discoveries would go on to improve the lives of children all over the country — but it was a terrible experience for him.

I often complain about a lack of solid evidence on guns’ relationship to public health. There’s a reason for that deficiency. In the 1990s, when health services researchers produced work on the dangers posed by firearms, those who disagreed with the results tried to have the National Center for Injury Prevention and Control shut down. They failed, but getting such work funded became nearly impossible after that…

And, this is sequally interesting:

Benign Effects of Automation: New Evidence from Patent Texts

Researchers disagree over whether automation is creating or destroying jobs. This column introduces a new indicator of automation constructed by applying a machine learning algorithm to classify patents, and uses the result to investigate which US regions and industries are most exposed to automation. This indicator suggests that automation has created more jobs in the US than it has destroyed…

As always at Naked Capitalism, spend some time reading the comments. Predominantly an articulate, well-informed crowd there.

ERRATUM

Posted by one of my FB friends. Lordy.

The jokes just write themselves.
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More to come...