Big Data

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I am a clinician and a clinical trialist. Medical research in some form or another (performing it, consuming it, reviewing it, editing it, etc.) occupies much of my time. Therefore, you can imagine my excitement while watching Apple’s product announcement yesterday when they introduced a new open source software platform called ResearchKit. Apple states ResearchKit could:

“revolutionize medical studies, potentially transforming medicine forever”

ResearchKit allows clinical researchers to have data about various diseases collected directly from a study participant’s iPhone (and perhaps other devices in the future — see below). The software is introduced as a solution to several important problems with current clinical studies, such as:

  • limited participation (the software allows everyone to participate; anyone with an iPhone can download a specific app for every study they want to participate in)
  • frequent data entry (patients can enter data as often as required/desired, rather than only at limited opportunities such as hospital or clinic visits)
  • data fidelity (currently-used paper patient “diaries” are prone to entering implausible or impossible values — the iPhone can limit the range of data entered)

Specifically, the website states:

ResearchKit simplifies recruiting and makes it easy for people to sign up for a study no matter where they live in the world. The end result? A much larger and more varied study group, which provides a more useful representation of the population.

This is a bold claim. We’ll see below that it doesn’t yet ring true.

Continue reading “Apple’s ResearchKit is Not (Yet) Ready For Primetime – But Soon? A Medical Researcher’s Perspective”

Screen Shot 2015-02-26 at 5.06.17 PMAs government involvement in U.S. health care deepens—through the Affordable Care Act, Meaningful Use, and the continued revisions and expansions of Medicaid and Medicare—the politically electric watchword is “socialism.”

Online, of course, social media is not a latent communist threat, but rather the most popular destination for internet users around the world.

People, whether out of fear for being left behind, or simply tickled by the ease with which they can publicize their lives, have been sharing every element of their public (and very often, their private) lives with ever-increasing zeal. Pictures, videos, by-the-minute commentary and updates, idle musings, blogs—the means by which people broadcast themselves are as numerous and diverse as sites on the web itself.

Even as the public decries government spying programs and panics at the news of the latest massive data-breach, the daily traffic to sites like Facebook and Twitter—especially through mobile devices—not only stays high, but continues to grow. These sites are designed around users volunteering personal information, from work and education information, to preferences in music, movies, politics, and even romantic partners.

So why not health data?

Continue reading “The Facebook Model for Socialized Health Care”

flying cadeuciiI’ve been thinking a lot about “big data” and how it is going to affect the practice of medicine.  It’s not really my area of expertise– but here are  a few thoughts on the tricky intersection of data mining and medicine.

First, some background: these days it’s rare to find companies that don’t use data-mining and predictive models to make business decisions. For example, financial firms regularly use analytic models to figure out if an applicant for credit will default; health insurance firms can predict downstream medical utilization based on historic healthcare visits; and the IRS can spot tax fraud by looking for fraudulent patterns in tax returns. The predictive analytic vendors are seeing an explosion of growth: Forbes recently noted that big data hardware/software and services will grow at a compound annual growth rate of 30% through 2018.

Big data isn’t rocket surgery. The key to each of these models is pattern recognition: correlating a particular variable with another and linking variables to a future result. More and better data typically leads to better predictions.

It seems that the unstated, and implicit belief in the world of big data is that when you add more variables and get deeper into the weeds, interpretation improves and the prediction become more accurate. Continue reading “Will Getting More Granular Help Doctors Make Better Decisions?”

flying cadeucii

In December, THCB asked industry insiders and pundits across health care to give us their armchair quarterback predictions for 2015. What tectonic trends do they see looming on the horizon? What’s overrated? What nasty little surprises do they see lying in wait? What will we all be talking about this time next year? Over the next few weeks, we’ll be featuring their responses in a series of quick takes.

Joe DeSantis, Vice President of HealthShare Platforms, InterSystems

Information Exchange is dead. Long live Information Exchange: There was a lot of talk in 2014 about the failure of information exchange. When people take a closer look, they are going to see there are actually some good examples of this working and changing how care is delivered. We’ll see lots more examples in 2015.

(Big) garbage in, (big) garbage out: People are looking to big data and analytics to tackle population health and other problems. They will soon find that without addressing data quality and conditioning up front, the results will be disappointing at best. This will be the year of clean data.

Keep it simple: The mobile revolution has not yet had the impact on healthcare that it has had in other sectors. Recreating desktop applications on a phone is not the answer, nor are retreads of messaging standards. We will have to rethink how healthcare information is presented and used.

One portal, please: Everyone agrees that patient engagement is essential – but giving me four separate portals, six more for my wife and three more for my mother makes me enraged, not engaged! Thought leaders will begin to realize that patient engagement must be built atop true information sharing. Continue reading “(Big) Garbage In. (Big) Garbage Out.”

flying cadeuciiIf another case of Ebola emanates from the unfortunate Texas Health Presbyterian Hospital, the Root Cause Analysts might mount their horses, the Six Sigma Black Belts will sky dive and the Safety Champions will tunnel their way clandestinely to rendezvous at the sentinel place.

What might be their unique insights? What will be their prescriptions?

One never knows what pearls one will encounter from ‘after-the-fact’ risk managers. I can imagine Caesar consulting a Sybil as he was being stabbed by Brutus. “Obviously Jules you should have shared Cleo with Brutus.” Thanks Sybil. Perhaps you should have told him that last night.

Nevertheless, permit me to conjecture.

First, they might say that the hospital ‘lacks a culture of safety which resonates with the values and aspirations of the American people.’

That’s always a safe analysis when the Ebola virus has just been mistaken for a coronavirus. It’s sufficiently nebulous to never be wrong. The premise supports the conclusion. How do we know the hospital lacks culture of safety? ‘Cos, they is missing Ebola, innit,’ as Ali G might not have said.

They would be careful in blaming the electronic health record (EHR), because it represents one of the citadels of Toyotafication of Healthcare. But they would remind us of the obvious ‘EHRs don’t go to medical school, doctors do.’ A truism which shares the phenotype with the favorite of the pro-gun lobby ‘guns don’t kill, people kill.’

Continue reading “Six Sigma vs Ebola”

Joe FlowerPut the question in 1880: Will technology replace farmers? Most of them. In the 19th century, some 80% of the population worked in agriculture. Today? About 2% — and they are massively more productive.

Put it in 1980: Will technology replace office workers? Some classes of them, yes. Typists, switchboard operators, stenographers, file clerks, mail clerks — many job categories have diminished or disappeared in the last three decades. But have we stopped doing business? Do fewer people work in offices? No, but much of the rote mechanical work is carried out in vastly streamlined ways.

Similarly, technology will not replace doctors. But emerging technologies have the capacity to replace, streamline, or even render unnecessary much of the work that doctors do — in ways that actually increases the value and productivity of physicians. Imagine some of these scenarios with me:

· Next-generation EMRs that are transparent across platforms and organizations, so that doctors spend no time searching for and re-entering longitudinal records, images, or lab results; and that obviate the need for a separate coding capture function — driving down the need for physician hours of labor. Continue reading “Will Technology Replace Doctors?”

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The term Big Data is ubiquitous and enigmatic. It’s so overused that it has practically morphed into a meme for using fancy math to make technology better. In a recent Center for Technology Innovation analysis of Big Data in education the term was defined as a, “group of statistical techniques that uncover patterns.” But, others disagree, so what is Big Data?

To answer that question Jenna Dutcher, Community Relations Manager for datascience@berkeley, the UC Berkeley School of Information’s online masters in data science, asked subject matter experts from industry, academia, and the public sector how they define Big Data. All of the answers are fascinating but there were several worth highlighting.

Continue reading “What Does Big Data Actually Mean?”

flying cadeuciiEverywhere we turn these days it seems “Big Data” is being touted as a solution for physicians and physician groups who want to participate in Accountable Care Organizations, (ACOs) and/or accountable care-like contracts with payers.

We disagree, and think the accumulated experience about what works and what doesn’t work for care management suggests that a “Small Data” approach might be good enough for many medical groups, while being more immediately implementable and a lot less costly. We’re not convinced, in other words, that the problem for ACOs is a scarcity of data or second rate analytics. Rather, the problem is that we are not taking advantage of, and using more intelligently, the data and analytics already in place, or nearly in place.

For those of you who are interested in the concept of Big Data, Steve Lohr recently wrote a good overview in his column in the New York Times, in which he said:

“Big Data is a shorthand label that typically means applying the tools of artificial intelligence, like machine learning, to vast new troves of data beyond that captured in standard databases. The new data sources include Web-browsing data trails, social network communications, sensor data and surveillance data.”

Applied to health care and ACOs, the proponents of Big Data suggest that some version of IBM’s now-famous Watson, teamed up with arrays of sensors and a very large clinical data repository containing virtually every known fact about all of the patients seen by the medical group, is a needed investment. Of course, many of these data are not currently available in structured, that is computable, format. So one of the costly requirements that Big Data may impose on us results from the need to convert large amounts of unstructured or poorly structured data to structured data. But when that is accomplished, so advocates tell us, Big Data is not only good for quality care, but is “absolutely essential” for attaining the cost efficiency needed by doctors and nurses to have a positive and money-making experience with accountable care shared-savings, gain-share, or risk contracts.

Continue reading “The Power of Small”

flying cadeuciiHealthcare costs far too much. We can do it better for half the cost. But if we did cut the cost in half, we would cut the jobs in half, wipe out 9% of the economy and plunge the country into a depression.

Really? It’s that simple? Half the cost equals half the jobs? So we’re doomed either way?

Actually, no. It’s not that simple. We cannot of course forecast with any precision the economic consequences of doing healthcare for less. But a close examination of exactly how we get to a leaner, more effective healthcare system reveals a far more intricate and interrelated economic landscape.

In a leaner healthcare, some types of tasks will disappear, diminish, or become less profitable. That’s what “leaner” means. But other tasks will have to expand. Those most likely to wane or go “poof” are different from those that will grow. At the same time, a sizable percentage of the money that we waste in healthcare is not money that funds healthcare jobs, it is simply profit being sucked into the Schwab accounts and ski boats of high income individuals and the shareholders of profitable corporations.

Let’s take a moment to walk through this: how we get to half, what disappears, what grows and what that might mean for jobs in healthcare.

Getting to half

How would this leaner Next Healthcare be different from today’s?

Waste disappears: Studies agree that some one third of all healthcare is simple waste. We do these unnecessary procedures and tests largely because in a fee-for-service system we can get paid to do them. If we pay for healthcare differently, this waste will tend to disappear.

Prices rationalize: As healthcare becomes something more like an actual market with real buyers and real prices, prices will rationalize close to today’s 25th percentile. The lowest prices in any given market are likely to rise somewhat, while the high-side outliers will drop like iron kites.

Internal costs drop: Under these pressures, healthcare providers will engage in serious, continual cost accounting and “lean manufacturing” protocols to get their internal costs down.

The gold mine in chronic: There is a gold mine at the center of healthcare in the prevention and control of chronic disease, getting acute costs down through close, trusted relationships between patients, caregivers, and clinicians.

Tech: Using “big data” internally to drive performance and cost control; externally to segment the market and target “super users;” as well as using widgets, dongles, and apps to maintain that key trusted relationship between the clinician and the patient/consumer/caregiver.

Consolidation: Real competition on price and quality, plus the difficulty of managing hybrid risk/fee-for-service systems, means that we will see wide variations in the market success of providers. Many will stumble or fail. This will drive continued consolidation in the industry, creating large regional and national networks of healthcare providers capable of driving cost efficiency and risk efficiency through the whole organization.

Continue reading “Half the Cost. Half the Jobs?”

Nortin Hadler

European health care systems are already awash in “big data.” The United States is rushing to catch up, although clumsily thanks to the need to corral a century’s worth of heterogeneity. To avoid confounding the chaos further, the United States is postponing the adoption of the ICD-10 classification system. Hence, it will be some time before American “big data” can be put to the task of defining accuracy, costs and effectiveness of individual tests and treatments with the exquisite analytics that are already being employed in Europe. From my perspective as a clinician and clinical educator, of all the many failings of the American “health care” system, the ability to massage “big data” in this fashion is least pressing. I am no Luddite – but I am cautious if not skeptical when “big data” intrudes into the patient-doctor relationship.

The driver for all this is the notion that “health care” can be brought to heel with a “systems approach.”

This was first advocated by Lucien Leape in the context of patient safety and reiterated in “To Err is Human,” the influential document published by the National Academies Press in 2000. This is an approach that borrows heavily from the work of W. Edwards Deming and later Bill Smith. Deming (1900-1993) was an engineer who earned a PhD in physics at Yale. The aftermath of World War II found him on General Douglas MacArthur’s staff offering lessons in statistical process control to Japanese business leaders. He continued to do so as a consultant for much of his later life and is considered the genius behind the Japanese industrial resurgence. The principal underlying Deming’s approach is that focusing on quality increases productivity and thereby reduces cost; focusing on cost does the opposite. Bill Smith was also an engineer who honed this approach for Motorola Corporation with a methodology he introduced in 1987. The principal of Smith’s “six sigma” approach is that all aspects of production, even output, could be reduced to quantifiable data allowing the manufacturer to have complete control of the process. Such control allows for collective effort and teamwork to achieve the quality goals. These landmark achievements in industrial engineering have been widely adopted in industry having been championed by giants such as Jack Welch of GE. No doubt they can result in improvement in the quality and profitability of myriad products from jet engines to cell phones. Every product is the same, every product well designed and built, and every product profitable.

Continue reading “Missing the Forest For the Granularity”

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