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Using Technology to Better Inform Consumers about Privacy Decisions

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By Cameron F. Kerry

Screen Shot 2014-04-30 at 11.21.56 AM

At the first White House public workshop on Big Data, Latanya Sweeney, a leading privacy researcher at Carnegie Mellon and Harvard who is now the chief technologist for the Federal Trade Commission, was quoted as asking about privacy and big data, “computer science got us into this mess; can computer science get us out of it?”

There is a lot computer science and other technology can do to help consumers in this area. Some examples:

•    The same predictive analytics and machine learning used to understand and manage preferences for products or content and improve user experience can be applied to privacy preferences. This would take some of the burden off individuals to manage their privacy preferences actively and enable providers to adjust disclosures and consent for differing contexts that raise different privacy sensitivities.

Computer science has done a lot to improve user interfaces and user experience by making them context-sensitive, and the same can be done to improve users’ privacy experience.

•    Tagging and tracking privacy metadata would strengthen accountability by making it easier to ensure that use, retention, and sharing of data is consistent with expectations when the data was first provided.

•    Developing features and platforms that enable consumers to see what data is collected about them, employ visualizations to increase interpretability of data, and make data about consumers more available to them in ways that will allow consumers to get more of the benefit of data that they themselves generate would provide much more dynamic and meaningful transparency than static privacy policies that few consumers read and only experts can interpret usefully.

In a recent speech to MIT’s industrial partners, I presented examples of research on privacy-protecting technologies.

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Is Medicine a Big Data Problem?

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By Ted Driscoll

Human beings are big data. We aren’t just 175 pounds of meat and bone. We aren’t just piles of hydrogen and carbon and oxygen.  What makes us all different is how it’s all organized and that is information.

We can no longer treat people based on simple numbers like weight, pulse, blood pressure, and temperature. What makes us different is much more complicated than that.

We’ve known for decades that we are all slightly different genetically, but now we can increasingly see those differences. The Hippocratic oath will require doctors to take this genetic variability into account.

I’m not saying there isn’t a place for hands-on medicine, empathy, psychology and moral support. But the personalized handling of each patient is becoming much more complicated.  The more data we can gather, the more each individual is different from others.

In our genome, we have approximately 3 billion base pairs in each of our trillions of cells.  We have more than 25,000 genes in that genome, sometimes called the exome.  Each gene contains instructions on how to make a useful protein.  And then there are long stretches of our genomes that regulate those protein-manufacturing genes.

In the early days, some researchers called this “junk DNA” because they didn’t know what it did.  But this was foolish because why would evolution conserve these DNA sequences between genes if they did nothing?  Now we know they too do things that make us unique.

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Google Flu Trends Shows Good Data > Big Data

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By Kaiser Fung


In their best-selling 2013 book Big Data: A Revolution That Will Transform How We Live, Work and Think, authors Viktor Mayer-Schönberger and Kenneth Cukier selected Google Flu Trends (GFT) as the lede of chapter one.

They explained how Google’s algorithm mined five years of web logs, containing hundreds of billions of searches, and created a predictive model utilizing 45 search terms that “proved to be a more useful and timely indicator [of flu] than government statistics with their natural reporting lags.”

Unfortunately, no. The first sign of trouble emerged in 2009, shortly after GFT launched, when it completely missed the swine flu pandemic. Last year, Nature reported that Flu Trends overestimated by 50% the peak Christmas season flu of 2012. Last week came the most damning evaluation yet.

In Science, a team of Harvard-affiliated researchers published their findings that GFT has over-estimated the prevalence of flu for 100 out of the last 108 weeks; it’s been wrong since August 2011.

The Science article further points out that a simplistic forecasting model—a model as basic as one that predicts the temperature by looking at recent-past temperatures—would have forecasted flu better than GFT.

In short, you wouldn’t have needed big data at all to do better than Google Flu Trends. Ouch.

In fact, GFT’s poor track record is hardly a secret to big data and GFT followers like me, and it points to a little bit of a big problem in the big data business that many of us have been discussing: Data validity is being consistently overstated.

As the Harvard researchers warn: “The core challenge is that most big data that have received popular attention are not the output of instruments designed to produce valid and reliable data amenable for scientific analysis.”

The amount of data still tends to dominate discussion of big data’s value. But more data in itself does not lead to better analysis, as amply demonstrated with Flu Trends. Large datasets don’t guarantee valid datasets. That’s a bad assumption, but one that’s used all the time to justify the use of and results from big data projects.

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Very Big Data

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By DWIGHT MCNEILL

The field of analytics has fallen into a few big holes lately that represent both its promise and its peril.  These holes pertain to privacy, policy, and predictions.

Policy.  2.2/7. The biggest analytics project in recent history is the $6 billion federal investment in the health exchanges.  The goals of the health exchanges are to enroll people in the health insurance plans of their choice, determine insurance subsidies for individuals, and inform insurance companies so that they could issue policies and bills.

The project touches on all the requisites of analytics including big data collection, multiple sources, integration, embedded algorithms, real time reporting, and state of the art software and hardware.  As everyone knows, the implementation was a terrible failure.

The CBO’s conservative estimate was that 7 million individuals would enroll in the exchanges.  Only 2.2 million did so by the end of 2013.  (This does not include Medicaid enrollment which had its own projections.)  The big federal vendor, CGI, is being blamed for the mess.

Note that CGI was also the vendor for the Commonwealth of Massachusetts which had the worst performance of all states in meeting enrollment numbers despite its long head start as the Romney reform state and its groundbreaking exchange called the Connector. New analytics vendors, including Accenture and Optum, have been brought in for the rescue.

Was it really a result of bad software, hardware, and coding?   Was it  that the design to enroll and determine subsidies had “complexity built-in” because of the legislation that cobbled together existing cumbersome systems, e.g. private health insurance systems?  Was it because of the incessant politics of repeal that distracted policy implementation?  Yes, all of the above.

The big “hole”, in my view, was the lack of communications between the policy makers (the business) and the technology people.  The technologists complained that the business could not make decisions and provide clear guidance.  The business expected the technology companies to know all about the complicated analytics and get the job done, on time.

This ensuing rift where each group did not know how to talk with the other is recognized as a critical failure point.  In fact, those who are stepping into the rescue role have emphasized that there will be management status checks daily “at 9 AM and 5 PM” to bring people together, know the plan, manage the project, stay focused, and solve problems.

Walking around the hole will require a better understanding as to why the business and the technology folks do not communicate well and to recognize that soft people skills can avert hard technical catastrophes.

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Are Payors Changing What They Pay For Medical Billing Codes To Adjust For Supply and Demand?

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By THCBist

Startup Mojo from Rhode Island writes:

Hey there, maybe THCB readers can weigh in on this one. I work at a healthcare startup. Somebody I know who works in medical billing told me that several big name insurers they know of are using analytics to adjust reimbursement rates for  medical billing codes on an almost daily and even hourly basis (a bit like the travel sites and airlines do to adjust for supply and demand) and encourage/discourage certain codes.  If that’s true, its certainly fascinating and pretty predictable, I guess.

I’m not sure how I feel about this. It sounds draconian. On the other hand,  it also sounds cool. Everybody else is doing the same sort of stuff with analytics: why not insurers? Information on this practice would obviously be useful for providers submitting claims, who might theoretically be able to game the system by timing when and how they submit. Is there any data out there on this?

Is this b.s. or not?

Lost in the health care maze? Having trouble with your health Insurance? Confused about your treatment options? Email your questions to THCB’s editors. We’ll run the good ones as posts.

Could Digital Rights Management Solve Healthcare’s Data Crisis?

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By Newman, Herrera, Frost & Parente

Today, academic medicine and health policy research resemble the automobile industry of the early 20th century — a large number of small shops developing unique products at high cost with no one achieving significant economies of scale or scope.

Academics, medical centers, and innovators often work independently or in small groups, with unconnected health datasets that provide incomplete pictures of the health statuses and health care practices of Americans.

Health care data needs a “Henry Ford” moment to move from a realm of unconnected and unwieldy data to a world of connected and matched data with a common support for licensing, legal, and computing infrastructure. Physicians, researchers, and policymakers should be able to access linked databases of medical records, claims, vital statistics, surveys, and other demographic data.

To do this, the health care community must bring disparate health data together, maintaining the highest standards of security to protect confidential and sensitive data, and deal with the myriad legal issues associated with data acquisition, licensing, record matching, and the Health Insurance Portability and Accountability Act of 1996 (HIPAA).

Just as the Model-T revolutionized car production and, by extension, transit, the creation of smart health data enclaves will revolutionize care delivery, health policy, and health care research. We propose to facilitate these enclaves through a governance structure know as a digital rights manager (DRM).

The concept of a DRM is common in the entertainment (The American Society of Composers, Authors and Publishers or ASCAP would be an example) and legal industries.  If successful, DRMs would be a vital component of a data-enhanced health care industry.

Giving birth to change. The data enhanced health care industry is coming, but it needs a midwife.There has been explosive growth in the use of electronic medical records, electronic prescribing, and digital imaging by health care providers. Outside the physician’s office, disease registries, medical associations, insurers, government agencies, and laboratories have also been gathering digital pieces of information on the health status, care regimes, and health care costs of Americans.

However, little to none of these data have been integrated, and most remain siloed within provider groups, health plans, or government offices.

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Chaos and Order: An Update From Patient Privacy Rights

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By Andy Oram

Thanks to the flood of new data expected to enter the health field from all angles–patient sensors, public health requirements in Meaningful Use, records on providers released by the US government, previously suppressed clinical research to be published by pharmaceutical companies–the health field faces a fork in the road, one direction headed toward chaos and the other toward order.

The road toward chaos is forged by the providers’ and insurers’ appetites for categorizing us, marketing to us, and controlling our use of the health care system, abetted by lax regulation. The alternative road is toward a healthy data order where privacy is protected, records contain more reliable information, and research is supported or even initiated by cooperating patients.

This was my main take-away from a day of meetings and a panel held recently by Patient Privacy Rights, a non-profit for whom I have volunteered during the past three years. The organization itself has evolved greatly during that time, tempering much of the negativity in which it began and producing a stream of productive proposals for improving the collection and reuse of health data. One recent contribution consists of measuring and grading how closely technology systems, websites, and applications meet patients’ expectations to control and understand personal health data flows.

With sponsorship by Microsoft at their Innovation and Policy Center in Washington, DC, PPR offered a public panel on privacy–which was attended by 25 guests, a very good turnout for something publicized very modestly–to capitalize on current public discussions about government data collection, and (without taking a stand on what the NSA does) to alert people to the many “little NSAs” trying to get their hands on our personal health data.

It was a privilege and an eye-opener to be part of Friday’s panel, which was moderated by noted privacy expert Daniel Weitzner and included Dr. Deborah Peel (founder of PPR), Dr. Adrian Gropper (CTO of PPR), Latanya Sweeney of Harvard and MIT, journalist Sydney Brownstone of Fast Company, and me. Although this article incorporates much that I heard from the participants, it consists largely of my own opinions and observations.

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Moving Beyond the Quantified Self

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By Kim Krueger & Sophie Park

In a world where big data plays an important role of monitoring individual health care and wellness, Health 2.0’s CEO and Co-Founder Indu Subaiya had an exclusive interview with Christine Robbins, CEO of BodyMedia on the future of health care in the marketplace as well as the role of big data. As we all know, BodyMedia was recently acquired by Jawbone – and we’re excited to have Christine joining us on the famous “3 CEOs” panel at the Health 2.0 Annual Fall Conference next week to tell us more about it.

Here’s a preview of what you should be looking forward to.

Indu Subaiya: We’re really excited for the Health 2.0 7th Annual Fall Conference and of course, I’ve been following news about you and BodyMedia over the last two months, which is really exciting. Congratulations on the acquisition.

Christine Robbins: Thank you. We’re on to the next chapter.

IS: That’s just amazing to me because BodyMedia in and of itself has had so many chapters and we’ve followed you almost from the very beginning. But what would be great is [if you could give] us an overview of the last year. When we saw you at Health 2.0 last — what you were beginning to present at the earliest stages, I believe, were data that BodyMedia had collected that could then be used in partnership with health plans and larger healthcare organizations.

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THCB Calendar: What Change on Capitol Hill Means for Big Data and Health Care

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Co-instructors Aman Bhandari and Dr. Tom Tsang of Merck’s Data Partnership Group will lead the next and final class in Health 2.0 EDU’s summer webseries, Big Data, Big Business, on How HITECH and the ACA Are Changing the Data Landscape, today, Tuesday, July 30th at 3pm PT/6pm ET.

Together Bhandari and Tsang bring decades of experience analyzing, predicting, and writing the legislation that most impacts the use of big data in health care. Join us to learn how the fine print in both the ACA and HITECH is creating both new opportunities and new challenges for using data. If you are a startup and have questions about either piece of legislation do NOT miss this class- Bhandari and Tsang will answer your queries live.

Sign up here and join us today.

Science-Driven Innovation and Tech-Driven Innovation: A Marriage of Convenience or a Marriage Made in Heaven?

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By Tom Hubbard

NEHI recently convened a meeting on health care innovation policy at which the Harvard economist David Cutler noted that debate over innovation has shifted greatly in the last decade. Not that long-running debates about the FDA, regulatory approvals, and drug and medical device development have gone away: far from it.

But these concerns are now matched or overshadowed by demands for proven value, proven outcomes and, increasingly, the Triple Aim, health care’s analog to the “faster, better, cheaper” goal associated with Moore’s Law.

To paraphrase Cutler, the market is demanding that cost come out of the system, that patient outcomes be held harmless if not improved, and it is demanding innovation that will do all this at once.   Innovation in U.S. health care is no longer just about meeting unmet medical need. It is about improving productivity and efficiency as well.

In this new environment it‘s the science-driven innovators (the pharma, biotech, and medtech people) who seem like the old school players, despite their immersion in truly revolutionary fields such as genomic medicine. It’s the tech-driven innovators (the healthcare IT, predictive analytics, process redesign, practice transformation and mobile health people) who are the cool kids grabbing the attention and a good deal of the new money.

To make matters worse for pharma, biotech and medtech, long-held assumptions about our national commitment to science-driven innovation seem to be dissolving. There’s little hope for reversing significant cuts to the National Institutes of Health. User fee revenues painstakingly negotiated with the FDA just last year have only barely escaped sequestration this year. Bold initiatives like the Human Genome Project seem a distant memory; indeed, President Obama’s recently announced brain mapping project seems to barely register with the public and Congress.

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