"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...

Pardon the MU turkey?

RIP Meaningful Use Born 2009 – Died 2014???
BOB WACHTER, MD

The policy known as Meaningful Use was designed to ensure that clinicians and hospitals actually used the computers they bought with the help of government subsidies. In the last few months, though, it has become clear that the policy is failing. Moreover, the federal office that administers it is losing leaders faster than American Idol is losing viewers.

Because I believe that Meaningful Use is now doing more harm than good, I see these events as positive developments. To understand why, we need to review the history of federal health IT policy, including the historical accident that gave birth to Meaningful Use...

...I believe the policy decisions were sensible. And for a while, everything went pretty well. Meaningful Use Stage 1, implemented in 2010-12, consisted of achievable standards designed to ensure that EHRs were being used effectively. But it was not so prescriptive as to stand in the way of the primary goal, namely, wiring healthcare. Adoption rates soared and MU ensured that the computers were being used.

With Meaningful Use Stage 2 (2012-present), things went sour. The standards became far more aggressive, veering far more deeply into the weeds of clinical practice. MU now dictated how doctors should give out handouts to their patients (they must be prompted by the computer). It held doctors and hospitals responsible for ensuring that patients viewed and transmitted their data to third parties (most patients had no idea how to do this). It forced EHRs to meet onerous disability access requirements. All of these are noble goals, but all are bells and whistles – the kinds of changes you make after you’ve nailed the basics of getting the darned machines to work safely and efficiently...

Interesting post. Read the entire piece.

In the past six months, in fact, more than half of ONC’s senior personnel – its chief scientist, chief nursing officer, chief privacy officer, and director of consumer eHealth – have jumped ship.

I worked in the Meaningful use program for one of the RECs from its inception until I retired last year. I never liked the phrase "meaningful use" -- effective health IT use is what we need for improved patient outcomes, and the MU criteria comprise weak neonatal steps proxies for that. And, CHPL "certification" is an expensive joke. But, conversely, I find the overwrought whining about the putatively lethally onerous standardized data capture criteria equally fatuous. A typical 99123 outpatient encounter workflow, for example, will require the accessing, viewing, updating/editing/appending, perhaps transmitting, and evaluating (SOAP) of hundreds of variables (or more) in a patient's record. The numerator/denominator "structured data capture" MU criteria comprise about two dozen (including the CQMs), and most of those can be handled at the sub-MD support staff level, with minimal workflow adjustment (and, most of those MU vars you'd likely be capturing anyway, albeit in haphazard fashion). That's not theoretical for me; it was my job to help REC clients do precisely that.

Trying to shoehorn all of that necessary logistical and analytical work into a 20-30 minute patient visit is the real problem.

More from Dr. Wachter:

Rather than continuing to push highly prescriptive standards that get in the way of innovation and consume most of the bandwidth of health IT vendors and delivery organizations, MU Stage 3 should focus on promoting interoperability, and little else. Last month, an expert panel presented ONC with a reasonable set of recommendations calling for standardized, publicly available application programming interfaces (APIs), the EHR version of standardized light sockets. This change would allow EHRs to communicate with each other and developers to write apps that could link to the large systems like those built by Epic and Cerner. Promoting this kind of interoperability would be a judicious role for a smaller, less muscle-bound ONC, and for MU Stage 3.

"the EHR version of standardized light sockets?"

I've been making that type of analogy for a long time. See my "Interoperabbable" posts.

"Visualize going to Lowe’s or Home Depot to have to choose among 1,848 ONC Stage 2 CHPL Certified sizes and shapes of 120VAC 15 amp grounded 3-prong wall outlets."

Whatever.

I hope everyone has a safe and happy Thanksgiving holiday weekend, btw. Glad I don't have to travel. Happy 34th anniversary to my wife and best friend (this Friday).
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More to come...

"Big Data" and "surveillant anxiety"

"To control spiraling healthcare costs related to managing patients with chronic conditions, 70 percent of healthcare organizations worldwide will invest in consumer-facing mobile applications, wearables, remote health monitoring, and virtual care by 2018, which will create more demand for big data and analytics capability to support population health management initiatives."

From "Big HIT Changes May Be Coming Soon."

Saw an article in my Atlantic Monthly iPhone app recently. Tweeted it, and then read it and the links to which it led me.

Self-tracking using a wearable device can be fascinating. It can drive you to exercise more, make you reflect on how much (or little) you sleep, and help you detect patterns in your mood over time. But something else is happening when you use a wearable device, something that is less immediately apparent: You are no longer the only source of data about yourself. The data you unconsciously produce by going about your day is being stored up over time by one or several entities. And now it could be used against you in court.

The first known court case using Fitbit activity data is underway. A law firm in Canada is using a client’s Fitbit history in a personal injury claim. The plaintiff was injured four years ago when she was a personal trainer, and her lawyers now want to use her Fitbit data to show that her activity levels are still lower than the baseline for someone of her age and profession to show that she deserves compensation.

As an additional twist, it is not the raw Fitbit data that will be used in the courtroom. The lawyers are relying on an analytics company called Vivametrica, which compares individual data to the general population by using “industry and public research.” Vivametrica claims that they “define standards for how data is managed, bringing order to the chaos of the wearable.” In other words, they specialize in taking a single person’s data, and comparing it to the vast banks of data collected by Fitbits, to see if that person is above or below average.

Vivametrica says that they are doing more than just enabling consumers to get access to their own data. They are also working with wearable tech companies and healthcare providers, and seeking to “reimagine employee health and wellness programs.” But what happens when there are conflicting interests between individuals who want to monitor data about their body and employers, wearable manufacturers and healthcare providers, and now the law?

Vivametrica isn’t the only company vying for control of the fitness data space. There is considerable power in becoming the default standard-setter for health metrics. Any company that becomes the go-to data analysis group for brands like Fitbit and Jawbone stands to make a lot of money. But setting standards isn’t as simple as it may seem.

Medical research on the relationship between exercise, sleep, diet, and health is moving extremely rapidly. The decisions about what is “normal” and “healthy” that these companies come to depends on which research they’re using. Who is defining what constitutes the "average" healthy person? This contextual information isn’t generally visible. Analytics companies aren’t required to reveal which data sets they are using and how they are being analyzed...

Pretty interesting. I have a Fibit. It was a freebie when I upgraded to my iPhone 5s. Used to use a Jawbone (what a shoddy, flimsy product that is; I bought one for my wife. Both hers and mine flamed out in relatively short order. More hassle trying to get warranty service than the damn things are worth). I've not been wearing the Fibit lately. Put it on the USB charge cable and then forgot about it. In light of this news I may have to inquire more closely of this vendor whether they capture my activity and sleep pattern data, and, if so, what they do with them.

Using customers' data against them is nothing new. It predates the "cloud," social media, and biometric wearables. Back in the late 1990's when I was caring for my terminally ill daughter in L.A., a dustup hit the news regarding a major grocery chain customer who'd slipped on some liquid in an aisle (the result of a broken container that had yet to be cleaned up) and injured himself. He sued. The chain responded by introducing his "customer loyalty discount card" purchase data in their defense, alleging that his history of alcoholic beverage purchases indicated that perhaps he'd been intoxicated while in the store, and that was the proximate cause of his misfortune.

Fast-forward 16 years. Now our "Digital Panopticon" is ubiquitous, going far beyond store loyalty cards (all the way, at it most extreme, to extraconstitutional blanket NSA surveillance). Unless you fastidiously turn off Facebook location permissions, you may find it announced on your page -- for all your friends (and skulking, data-mining others) to see -- that you'd just dined at Lindo Michoacan Mexican restauarant and that you were now at the veterinarian with your dogs.

Both of those things happened to me. Trivialities, in those two instances, to be sure, but tiny snips of a much larger concern.

"Already, the lived reality of big data is suffused with a kind of surveillant anxiety — the fear that all the data we are shedding every day is too revealing of our intimate selves but may also misrepresent us."

From The Anxieties of Big Data, by Kate Crawford, author of the aforementioned Atlantic Monthly piece. Kate continues.

The current mythology of big data is that with more data comes greater accuracy and truth. This epistemological position is so seductive that many industries, from advertising to automobile manufacturing, are repositioning themselves for massive data gathering. The myth and the tools, as Donna Haraway once observed, mutually constitute each other, and the instruments of data gathering and analysis, too, act as agents that shape the social world. Bruno Latour put it this way: “Change the instruments, and you will change the entire social theory that goes with them.” The turn to big data is a political and cultural turn, and we are just beginning to see its scope.

"With more data comes greater accuracy and truth?" Myth, indeed. The utility of any set of data is a function of its intended use. "Big data" shot through with inacurracies can still be handsomely profitable for the analytical user (or buyer), irrespective of any harms they might visit on the individuals swept up (usually without their knowledge or assent) in the data hauls and subsequent proprietary modeling.

A personal illustration. I worked for a number of years (2000 - 2005) in subprime credit risk modeling at a VISA/MC issuer. We routinely bought "pre-screened" prospect mailing lists for our direct mail marketing campaigns. Direct mail campaigns can be in the aggregate quite profitable at a one percent response rate or lower. Ours, being targeted to credit-hungry subprime prospects with blemished credit histories, typically had response rates of about 4%. Of those who responded, about half did not pass the initial in-house analytical cut for one reason or another (many owing to impossible, bad data in the individuals' dossiers). Of the remaining 2% that we actually booked, perhaps half of those would eventually "charge off" (default). These were our "false positives."

The surviving 1% were lucrative enough to pay for everything, including a nice net margin (we set new annual profit levels every year I was there). It's called "CPA" -- cost per acquisition. Ours were about $100 per new account. Fairly standard in the industry at the time.

Potentially creditworthy (and profitable) prospects that we passed on after they replied were our "false negatives." And, ~96% of our marketing targets didn't even respond, so were were "wrong" about them (the "unknown unknowns") at the outset.

To sum up; we were in, a material sense, routinely 99% "wrong," but, notwithstanding, incredibly profitable.

Now, "big data shot through with inaccuracies" is entirely another matter when it comes to, say, "terrorism surveillance" and getting it wrong. Recall the stillborn post- 9/11 federal proposal for "Total Information Awareness." I do. Wrote about it here.

From William Safire's NY Times editorial (11/14/2002)
"...Every purchase you make with a credit card, every magazine subscription you buy and medical prescription you fill, every Web site you visit and e-mail you send or receive, every academic grade you receive, every bank deposit you make, every trip you book and every event you attend -- all these transactions and communications will go into what the Defense Department describes as "a virtual, centralized grand database."

To this computerized dossier on your private life from commercial sources, add every piece of information that government has about you -- passport application, driver's license and bridge toll records, judicial and divorce records, complaints from nosy neighbors to the FBI, your lifetime paper trail plus the latest hidden camera surveillance -- and you have the supersnoop's dream: a "Total Information Awareness" about every U.S. citizen.

This is not some far-out Orwellian scenario. It is what will happen to your personal freedom in the next few weeks if John Poindexter gets the unprecedented power he seeks...."

I warned back then:

In addition to the warrantless law enforcement implications of HSA's envisioned data repositories, we must also recognize that a TIA database will also constitute a commercial data-miner's wet dream of scope heretofore unimagined. Vigilance with respect to HSA collaborative "public-private partnerships" had better be tireless.

While TIA was nominally killed off (proud to say I had a small hand in its demise), things inexorably got worse. See my July 9th, 2008 post "Privacy and the 4th Amendment amid the "War on Terror."

Today we have all manner of virtually unregulated big data mining, modeling, and aggregated and re-aggregated resale going on, using all of us as correlational grist -- e.g.,  Google, the overtly commercial Amazon and their lesser competitors, and "free" social media platforms such as Facebook, Twitter, Tumblr, Pinterest, etc, along with business sites such as LinkedIn. Digital gumshoe companies such as Palantir are hard at work quietly drilling in the tar sands of social media, modeling away and "scoring" individuals for their clients, far from any regulatory purview.

The Digital Panopticon.

UBER KNOWS IF YOU'VE BEEN NAUGHTY

The ride-sharing startup Uber has recently gotten mired in some fractious bad publicity over their privacy practices. See, e.g., "Uber’s PR stumble drives new privacy woes."

The ride-sharing app this week finds itself in the midst of a major public-relations nightmare after BuzzFeed captured an Uber executive suggesting the company might conduct opposition research on journalists — not to mention a second incident in which an Uber employee looked at a reporter’s travel history...

See also "What’s Really Wrong With Uber?" 

 Also "7 reasons you may want to delete your Uber app."

1. Uber tracks riders’ hookups
In 2012, Uber said it was tracking user data to see which cities had the most one-night stands in a blog post titled “Rides of Glory.” Uber said it was analyzing data on its users’ car rides on Friday and Saturday nights between 10 p.m. and 4 a.m.

“The world has changed and gone are the days of the Walk of Shame,” the post says. “We live in Uber’s world now.” According to Uber’s analysis, Boston topped the list of cities with the most hookups. Uber took it a step further and even broke down its data by neighborhood. [slide 2]

If you don't have "Surveillant Anxiety," you've not been paying attention.

TRAFFICKING IN ePHI

Matthew Holt of Health 2.0 has a post up at THCB entitled "Is Deborah Peel up to her old tricks."

Long time (well very long time) readers of THCB will remember my extreme frustration with Patients Rights founder Deborah Peel who as far as I can tell spent the entire 2000s opposing electronic health data in general and commercial EMR vendors in particular. I even wrote a very critical piece about her and the people from the World Privacy Forum who I felt were fellow travelers back in 2008. And perhaps nothing annoyed me more than her consistently claiming that data exchange was illegal and that vendors were selling personally identified health data for marketing and related purposes to non-covered entities (which is illegal under HIPAA).

Dr. Peel (a psychiatrist) is the nation's pre-eminent alarmist regarding the putative perils of ePHI (electronic Protected Health Information). She apparently believes that her pure motives (and I have no doubt that they are) entitle her to being cavalier with facts and their conflation.

Matthew minces no words in conclusion.

It’s now put up or shut up time. Are personal health data resales a bigger industry than health IT? Are vendors really illegally selling identified health data? Is Deborah going to retract her statements? Or at least explain what she knows–with evidence please–that I’m missing?

See the accruing comments below Matthew's post.

I had a go at her during my October 27th, 2014 post "An Epic battle: Did the EHR kill Dallas Ebola patient zero? On the double-edged sword of Health IT."

In the immediate aftermath of the the Dallas Duncan dx debacle, sharp-elbowed HIT critics wasted no time assigning blame. The ever-strident patient privacy rights advocate Deborah C. Peel, MD posted a LinkedIn piece under a inflammatory click-bait headline "Why did Mr. Duncan have to die for the US to face flaws in EHRs?"

Nothwithstanding that [1] she's a psychiatrist, not an ER doc, [2] wasn't there in the Dallas ER, and [3] is not an Epic user...

Dr. Peel's recent awkward TEDx talk.

Make up your own minds. She asserts a lot of things that are in fact true. However,

Ms. Peel, @11:01,
“So, we know, for example, that physicians and their EHRs sell the [ePHI] data…”

Nice conjunctive conflation. Name some physicians who are selling ePHI?

Beneficent, public-minded motives don't grant you a pass on documentable specifics.
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A THOUGHTFUL BOOK ON PRIVACY

I read them all. This one is excellent.

In his essay “Commodities and the Politics of Value,” Arjun Appadurai has noted “the tendency of all economies to expand the jurisdiction of commoditization and of all cultures to restrict it.” The economy’s jurisdiction has now come to include human organs and genetic material, which are “mined” and “harvested” like minerals and crops. “We used to think our fate is in the stars,” says former Human Genome Project director James Watson. “Now we know, in large measure, our fate is in our genes.” So is the potential for making a lot of money, along with a new frontier for privacy abuse. A 2001 survey by the American Management Association revealed that 30 percent of large and midsize companies solicited genetic information about employees; 7 percent used the information in hiring and promotion. Meanwhile biotech companies “have flooded the federal patent office with applications to patent newly discovered genes” even though the genes occur naturally. One bioethicist has likened this trend to “patenting the alphabet and charging people every time they speak.”

Even when individuals manage to rise above a market mentality, they do not necessarily rise above the Market. In one especially disturbing instance, families whose children were victims of Canavan disease, a rare and fatal recessive disorder, donated their children’s tissue samples for research in the hopes that better prenatal diagnostics and new treatments would spare other families the suffering they had known. Later they discovered that researchers at Miami Children’s Hospital had patented the gene for Canavan and the hospital was charging royalty fees for diagnostic tests. In some cases, the same families whose donations had enabled the research were charged fees when they tested for the condition in other members of their households.

For scale of exploitation, few examples can match the story of Henrietta Lacks, a poor African American tobacco farmer, whose “immortal life” is recounted in a recent book by Rebecca Skloot. Prior to her death from cervical cancer in 1951, medical researchers at the “colored” ward of Johns Hopkins Hospital removed and cultured Lacks’s cells without her knowledge or consent. Since then more than 50 million metric tons of cells grown from this original “harvest” have been used for medical research, including the development of the polio vaccine, in vitro fertilization, and cloning. Of the millions in profits generated by Lacks’s unwitting donation, her family did not receive a penny. They did not even learn that her cells had been used until more than twenty years later, when they too became unwitting subjects of medical research.

In an effort to curtail such abuses, the state of Oregon passed the Genetic Privacy Act of 1995 , which mandated “informed consent for the collection , analysis, and disclosure of DNA information” and the destruction of DNA samples once testing was completed. The most controversial provision of the law was its “property clause,” stating that an “individual’s genetic information is the property of the individual.” Not surprisingly, the most vocal opposition to the property clause— in Oregon and in other states such as New Jersey and Maryland, which followed with genetic privacy statutes of their own— came from and on behalf of the biotechnical industry.

Sociologist Margaret Everett, who served on the Oregon Genetic Privacy Advisory Committee and whose son died as the result of a rare genetic disorder, opposed the property clause for different reasons. Though initially she had joined the committee with the aim of protecting the clause against legislative revision, noting that she felt “very‘proprietary’ about my son’s cells,” she eventually came to feel that “the proponents of individual property rights were encouraging, perhaps unwittingly, the very commodification and objectification that I found so troubling.” In other words, her son’s cells were not saved from economic exploitation simply by giving her an exclusive patent to exploit them.

Everett’s experience of wrestling with the property clause raises questions about how we construe privacy rights within the structures of a market economy. Even when we oppose the economic exploitation of our bodies, we find it difficult to do so in any way other than to turn them into salable property. To use Marx’s words, we cannot think of something as ours unless we “have it.” Is it possible that bodily integrity and personal privacy would find better fulfillment in a society where neither could be sold? Might our genetic material become most indisputably ours in a society that viewed its citizens— as the parents of the children with Canavan disease surely viewed themselves— as stewards of the common wealth of humankind?

Keizer, Garret (2012-08-07). Privacy (Big Ideas//Small Books) (pp. 89-92). Macmillan. Kindle Edition.

ERRATUM

Below: this is pretty interesting, my top 10 viewing countries the past 5 days.

Thanks for continuing to read.
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SPEAKING OF "SURVEILLANT ANXIETY"

How Medical Care Is Being Corrupted
PAMELA HARTZBAND and JEROME GROOPMAN, NY Times OpEd

WHEN we are patients, we want our doctors to make recommendations that are in our best interests as individuals. As physicians, we strive to do the same for our patients.

But financial forces largely hidden from the public are beginning to corrupt care and undermine the bond of trust between doctors and patients. Insurers, hospital networks and regulatory groups have put in place both rewards and punishments that can powerfully influence your doctor’s decisions.

Contracts for medical care that incorporate “pay for performance” direct physicians to meet strict metrics for testing and treatment. These metrics are population-based and generic, and do not take into account the individual characteristics and preferences of the patient or differing expert opinions on optimal practice...

Physicians who meet their designated targets are not only rewarded with a bonus from the insurer but are also given high ratings on insurer websites. Physicians who deviate from such metrics are financially penalized through lower payments and are publicly shamed, listed on insurer websites in a lower tier. Further, their patients may be required to pay higher co-payments.

These measures are clearly designed to coerce physicians to comply with the metrics. Thus doctors may feel pressured to withhold treatment that they feel is required or feel forced to recommend treatment whose risks may outweigh benefits.

When a patient asks “Is this treatment right for me?” the doctor faces a potential moral dilemma. How should he answer if the response is to his personal detriment? Some health policy experts suggest that there is no moral dilemma. They argue that it is obsolete for the doctor to approach each patient strictly as an individual; medical decisions should be made on the basis of what is best for the population as a whole...

...the power belongs to the insurers and regulators that control payment. There is now a new paternalism, largely invisible to the public, diminishing the autonomy of both doctor and patient...

Medical care is not just another marketplace commodity. Physicians should never have an incentive to override the best interests of their patients.

I am not a fan of the "population health" thing. If every physician does what's best for her patients, the population health needle will move in the right direction in relatively short order.
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More to come...

A damning Health IT quote from Joe Flower

"A Medical Economics survey published last February showed that over 70 percent of physicians would not buy their current information system, if they had the chance to make that choice again, because they hate the way it works. Nearly 70 percent have seen no improvement in care coordination with the hospitals. And 45 percent believe it has actually hurt patient care."

From "Why You Should Ditch Your IT System."

So, three survey findings. The first one we can take at face value. The second and third, though, would be stronger with some underlying data having firm prior operational definitions, i.e., some scientific metrics going to adverse process "care coordination" and HIT proximate causes that "actually hurt patient care." Absent those, we're left mostly with partisan shouting-past-each-other points of contention. See, e.g., my prior post "An Epic battle: Did the EHR kill Dallas Ebola patient zero? On the double-edged sword of Health IT."

I am reminded of the saying "the plural of 'anecdote' is not 'data'."

Nonetheless, Joe's post is highly worthy of consideration. He's one of the most astute and charitable healthcare observers I've ever met. Read the entire post carefully.

So you spent millions to billions of dollars on information systems over the past few years, right?

How’s that working out for you?

For a large percentage of you, whether or not you admit it, not so well. What you bought needs some serious tweaks, re-engineering, re-thinking, re-vamping.

For an even larger percentage, maybe most of you, the best advice is: Junk it. Throw it out and start over.

Poorly designed and poorly implemented information systems are worse than useless, worse than a waste of those millions and billions of dollars. As we go through rapid, serious changes in health care, poor information systems will strangle your every strategy, hobble your clinicians, kill patients and actually threaten the viability of your organization.

A lot of health care executives dismiss the complaints about the new systems as the carping of stubborn technophobic doctors and nurses who should just get with the program. If you are tempted to do that, you need to take a step back. You need to get real. The complaints and concerns are too widespread, too deep and indeed too frightening for that kind of blithe denial. And they are not just coming from disgruntled docs...

In a recent Black Book survey, 98 percent of 13,650 registered nurses polled said nurses in their facility were never asked to help design the system; it was just imposed on them. As a result, 85 percent say the system is flawed and gets in their way, 94 percent feel that it has not improved communication among the care team, and 90 percent feel that it has damaged communication with the patient...

It’s Time
It’s not like this is new. It’s not like we didn’t see this coming. I have been writing about and advocating for the digitization of health care for 30 years, as have many others. I have to tell you: We knew back then that interconnectivity through industry standards — and smart user interfaces that assist clinicians in their normal workflow rather than hinder them — were hard problems that needed solving...

Yeah. "How's that working out for you?" Maybe Joe might inquire of Kaiser Permanente, users of the much-maligned Epic platform? See my Feb 6th, 2014 post "Meaningful Use 2013 review, ONC Working Group Stage 3 draft report, and discussion of KP's book "Connected for Health."

See also my May 28th, 2014 post on Joe, "Joe Flower on the Health tech revolution."

"It's not like we didn't see this coming." I return to my 2011 post "Use Case" and the observations of medical economist J.D. Kleinke, written nearly a decade ago.

HIT market failure. The underlying cause of Joe’s death is health information technology (HIT) market failure. If the state of U.S. medical technology is one of our great national treasures, then the state of U.S. HIT is one of our great national disgraces. We spend $1.6 trillion a year on health care—far more than we do on personal financial services—and yet we have a twenty-first-century financial information infrastructure and a nineteenth-century health information infrastructure. Given what is at stake, health care should be the most IT-enabled of all our industries, not one of the least. Nonetheless, the “technologies” used to collect, manage, and distribute most of our medical information remain the pen, paper, telephone, fax, and Post-It note.

Meanwhile, thousands of small organizations chew around the edges of the problem, spending hundreds of millions of dollars per year on proprietary clinical IT products that barely work and do not talk to each other. Health care organizations do not relish the problem, most vilify it, many are spending vast sums on proprietary products that do not coalesce into a systemwide solution, and the investment community has poured nearly a half-trillion dollars into failed HIT ventures that once claimed to be that solution. Nonetheless, no single health care organization or HIT venture has attained anything close to the critical mass necessary to effect such a fix.

This is the textbook definition of a market failure. All but the most zealous free-market ideologues recognize that some markets simply do not work. Indeed, reasoned free-market champions often deconstruct specific market failures to elucidate normal market functioning. The most obvious examples of such failures (such as public transit and the arts) are subsidized by society at large because such subsidies yield benefits to the public that outweigh their costs. Economists refer to these net benefits as “positive externalities,” defined as effects that cannot be captured through the economic equation of direct cost and benefit.

The positive externalities of an HIT system approaching the functionality of our consumer finance IT system include reduction of medical errors like the one that killed Joe Wilson; elimination of tens of thousands of redundant and expensive tests, procedures, and medications, many of which are not only wasteful but harmful; and the coordination and consistency of medical care in ways only promised by the theoretical version of managed care. These public health benefits are well beyond the reach of a health care system characterized by the complexities of medicine and conflicts of multiple parties working at economic cross-purposes. They are trapped outside the economic equation, positive externalities of a stubbornly fee-for-service health care system that inadvertently rewards inefficiency, redundancy, excessive treatment, and rework...

Back to Joe:

"[For] maybe most of you, the best advice is: Junk it (your EHR). Throw it out and start over."

Interesting. Maybe a small shop ambulatory practice will have that luxury, but I don't see larger acute care institutions going there, absent crushing exigent circumstances.

Joe in the comments (and my reply):

I subseqently went on with more.

“Meaningful Use,” btw, is a red herring whipping boy. The typical ambulatory EHR houses close to 4,000 variables in its RDBMS, with hundreds or more having to be accessed, viewed, updated, transmitted, synthesized etc during a conventional pt visit (and, yeah, to your point, a lot of them just to get paid). The subset numerator/denominator MU vars comprise less than 2 dozen (including the CQMs), most of which can be handed at the sub-MD support staff level.

Just to be clear: I’m no big MU fan, btw. I mocked the bozo-ness of it even while working in the MU program. See my Clinic Monkey (a “Survey Monkey” riff) ClinicMonkey.blogspot.com

The fact that it may take 5-8 clicks to get to a MU data target is stupid (e.g., in eClinicalWorks there are 4 different workflows for doing CPOE, ranging from 5 to 8 clicks). EVERY MU criterion should be macro-accessible in 1 click (or Dragon voice command). It’s not much to have to learn. I’d have insisted on macro capability within every ONC certified system as a condition of cert. Windows no longer has a macro utility, but there are 3rd party vendors of inexpensive macro utilities. Still, no one wanted the added expense and hassle of going 3rd party macro. And, in fairness, even optimizing MU workflows might still result in an additional $4,000 a year in labor cost (even if it only added minute or less to a note; I could show you the math).

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apropos of all of the foregoing, let's fast-forward up to late 2014. Where might we be headed? Jerome Carter, MD, at EHR Science:

A Question for EHR Vendors: What Is a “Real” Computer?

This might seem like a silly question with an obvious answer, but is it really? The solution to any problem grows out of the environment in which it appears and from the mindset in which it was conceived. In 1970, the answer to this question would have been a mainframe system. By 1981, after the Apple II and a few other microcomputers had been around for a few years, the answer for most people at that time would still have been mainframes (or maybe minicomputers as well)  because microcomputers were still considered to be toys. When IBM released the IBM PC AT in 1982, microcomputers began to be taken seriously as computers—that is, computers that could be used for real business applications.  The arrival of reliable local area networking technology cemented the status of PCs as real business computers.

Initially, local area networks (LANs) were used to share printers, disk storage, and applications.  However, as servers became more powerful and disk storage more dense and affordable, database management systems and sophisticated client/server software appeared...

Personal computers had to mature for a few years before they could tackle real problems, and smartphones and tablets are following the same pattern. Among other things, mobile computers add new user interface options and portability to the computing mix in ways that no other computing platform can match.   Solutions to clinical information management problems must now embrace mobile computing capabilities: touch-based interfaces, multi-media data management, communications functionality, and location/ movement awareness. Without question, the iPad Air 2 is a real computer, and it and other tablets with similar specs can be used to solve real problems.

Most current EHR software was designed well before tablets and smartphones existed, and many were born before the Internet really caught on. These EHR systems were designed back when LANs were state-of-the-art computing platforms, the cloud did not exist, Wi-Fi was painfully slow, and pointing was done with a mouse. The computing platform and development tools dictated how developers approached clinical information management problems...

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...

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…

'eh?

OK, where will we get the requisite top gun HIT programming / UX design talent seemingly in short supply? From the current issue of The New Yorker:

THE PROGRAMMER’S PRICE
Want to hire a coding superstar? Call the agent.
BY LIZZIE WIDDICOMBE

Not long ago, Stephen Bradley, a New York tech entrepreneur, was looking to expand his company, AuthorBee, which aggregates tweets and Instagram posts and puts them together in story form. Instead of following people, readers can follow their interests—“Breaking Bad,” for example, or the New England Patriots. Bradley is not a stereotypical startup founder, a hoodie-wearing college dropout; he’s been working in tech and media for decades. To launch AuthorBee, he raised three-quarters of a million dollars from angel investors and hired programmers in Pakistan and Bangladesh to build a prototype. Now he wanted to build a bigger, better version of the site, so he had to find someone to write the code that would form AuthorBee’s DNA. The guys in Pakistan and Bangladesh were O.K., but the cultural differences and the language barriers slowed things down. He needed “one really good developer” with a mastery of all the coding languages and frameworks that AuthorBee uses: Python, Django, Angular, JavaScript, the Twitter A.P.I. The search for programming talent was the part of building a startup that Bradley most dreaded. “It is a nightmare,” he told me. “And I’m as plugged in as you can be to the New York tech scene.”

He put up a job posting on the Web site AngelList, and was immediately flooded with calls from headhunters and e-mails from offshore companies wanting to set up a “short online telephonic meeting.” “I could have had two hundred résumés on my desk,” Bradley said. But he knew that the people behind those résumés weren’t the ones he was looking for. His dream developer might be buried in there somewhere, but Bradley had come to think that developers were like social media itself: “Ninety-nine per cent of them suck.” He added, “The entire problem is wading through the noise.”...

The world is being rebuilt in code. Hiring computer engineers used to be the province of tech companies, but, these days, every business—from fashion to finance—is a tech company. City governments have apps, and the actress Jessica Alba is the co-founder of a startup worth almost a billion dollars. All of these enterprises need programmers. The venture capitalist Marc Andreessen told New York recently, “Our companies are dying for talent. They’re like lying on the beach gasping because they can’t get enough talented people in for these jobs.”

The computer science taught in colleges still focusses more on theory than on commercial application; the business of teaching practical coding skills has the whiff of trade school. So-called coding “boot camps,” such as General Assembly, founded in 2010, are trying to fill the gap, teaching crash courses in how to design Web sites and write code. But Jake Schwartz, the co-founder and C.E.O. of General Assembly, told me, “There’s simply not enough senior people in the system.”...

Well worth your time. Ponder the ramifications for Health IT.

More Joe Flower:

Zane Burke, the president of Cerner (which just bought Siemens), made a strong point about interoperability in a recent interview with H&HN Daily’s Matthew Weinstock:

“We all owe it to the country … to really perform true interoperability and create openness…. You need platforms that don’t just open up your APIs [application programming interfaces] but actually create ecosystems for other players … to perform well. We can’t use the operating system at the EHR lever as a competitive advantage. It just can’t be that way.

Yeah. See my posts on "Interoperababble" See also "Interoperability solution? HL7® FHIR® -- We ® Family."
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More to come...

When HHS (via ONC and AHRQ) publishes two spiffy documents announcing major long-term Quality Improvement initiatives,

Link (pdf)

Link (pdf)

you might confidently assume that you'd encounter some detail regarding fundamental priority cultural, tactical, and strategic terms and phrases such as

• workforce;
• workplace;
• staff;
• high-performance;
• team-based;
• burnout;
• turnover;
• CQI;
• Deming;
• Lean;
• PDSA;
• Six Sigma (or 6 Sigma);
• DMAIC;
• Agile.

You would be WRONG.

Zero. Zip. Zilch. Nada. Nyet. Don't take my word for it. Search the documents yourself.
 

Re-read my little recent trilogy:

• On healthcare system improvement: are the Feds proposing the building of a two-legged stool?
• Continuing on the three-legged stool riff: more on "Just Culture," workforce engagement, and leadership in healthcare.
• The "Talking Stick" and the three-legged stool of sustained, transformative healthcare QI

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More to come...