By Matthew Holt

Last week I was in DC and I caught up with Bryan Sivak, a geek’s geek who has migrated from Silicon Valley (via London) to government service first in Maryland and now at HHS. He has a big job there to keep pounding out the open health data drumbeat Todd Park started. And he’ll have at least two big opportunities to do it this spring, first at Health 2.0′s developer conference Health:Refactored in Silicon Valley in May and then at the now 4th annual Health DataPalooza in DC in June.

By Colin Hill

My father, Foster Hill, has stage III prostate cancer.

At 69 years old, he is a quiet man who was often told in his younger days that he resembled Muhammad Ali. He immigrated in his twenties to Canada from the small Caribbean nation of Antigua to look for opportunities beyond sugar cane and the tourism trade.

My father became a chemical technician for well-known oil refineries, while staying true to his real passion in life – playing organ music. Every Sunday, as he has since I can first remember, he plays the largest church organ in Sarnia, near Lake Huron, where he lives with my mother.

Like many men of his generation, he has always been wary for the medical system. For decades he avoided the test, known as PSA, that screens for prostate cancer. In September of this year, driven by pain he could no longer ignore, he went to his doctor who discovered a rock-hard prostate gland. The diagnosis, stage III prostate cancer, means that the cancer has already begun to spread, but is still potentially treatable.

Now retired, his long hours practicing the organ are punctuated with doctor visits to receive Lupron hormone therapy. The good news? The therapy is working. For now.

We don’t know what lies ahead. The first round of Lupron therapy is often effective, but a significant number of patients later develop a resistance to the drug.

The battle against my father’s cancer has only just begun.

This is where Big Data in healthcare can become a true lifesaver. Typically, in medicine, we know only what works for the majority of patients, not what will work for an individual. However, with enough data from enough people – we are talking hundreds of thousands, and sometimes, even millions of patients – we can apply analytics to build predictive models to discover which interventions will work. For the last twelve years, it has been my job to make that happen.

As CEO and founder of GNS Healthcare, I oversee a team of mathematicians, biologists, and data scientists as they crunch and decode healthcare data to unlock the mysteries of what treatment will work for specific patients.

My father’s cancer has given these efforts a new urgency and has raised a new question: Can I use Big Data to save my father’s life?

Continue reading “Can Big Data Save My Dad From Cancer?”

By DAVID SHAYWITZ, MD

Obama’s most significant healthcare-related accomplishment this year may well have been his campaign’s demonstration of the effective use of analytics and behavioral insight – strategies that also offer exceptional promise for the delivery of care and the maintenance of health.

For starters, of course, there’s the widely-reported “big data” success of the Obama campaign.  In unprecedented fashioned, they collected, mined, analyzed, and actioned information, microtargeting voters in a remarkably individualized fashion.

Imagine if healthcare interventions could be personalized as effectively (or pursued as passionately).

Another example:  according to the NYT, the Obama campaign hired a “dream team” of behavioral psychologists to burnish their message and bring out the vote, using a range of techniques the field has developed over the years.

According to the article, the behavioral experts “said they knew of no such informal advisory committee on the Republican side.”

This idea of focusing intensively on behavior change is without question an idea whose time has come.

Earlier this year, for instance, a colleague (with similar training in medicine, molecular biology, and business) and I were surveying the biopharma landscape, and were struck by the extent to which classic biology hasn’t (yet) delivered the cures for which we had hoped; physiology turns out to be extremely complicated, and people, and communities, even more so.

We were also struck by the remarkably low adherence rates for many drugs, abysmal whether you look at this from the perspective of clinical care or commercial opportunity (imagine if Toyota lost half their cars on the way to the dealership).
Continue reading “The Mentalists”

By David Shaywitz, MD

My twitter stream is awash in math this morning, cheering Nate Silver’s exceptional forecasting (“Triumph of the Nerds: Nate Silver Wins In Fifty States”, Chris Taylor wrote), and celebrating the victory of math and big data over pompous punditry.  Jeff Greenfield tweeted, “I, for one, welcome our new Algorithmic Overlord.”

At some level, I thrill to the ascendancy of math, and of math nerds – and I write this as a proud former math team captain (and math team T-shirt designer), and as someone whose very best summers as a teenager were spent in math (and writing) camp at Duke University.  It’s also one of the reasons I love Silicon Valley so much – it’s where nerds rule, and where even emerging VCs promote themselves as “Geeks.”

However, before we turn all of life over to algorithms, as some are suggesting, it’s important to place the election prediction in context.

The accomplishment of Silver’s splendid forecasting was to intelligently aggregate existing data, to accurately summarize the current, expressed intentions of the national electorate.  And we’ve learned that careful analysis is far more useful than blustery experts – something Philip Tetlock has been trying to tell us for years.

At the same time, all forecasting challenges are not created equal, and summarizing current public opinion is a much lower bar than predicting events far into the future – and Silver has been clear about this; it’s others who seem to be leaping ahead.

Continue reading “Nate Silver Is King, Long Live Nate Silver”

By J.D. Kleinke

The most remarkable thing about Health 2.0 this time around, at least for me? The growing number, and percentage, of attendees old enough to get a reference like “Hey, Known Spender.”

If that wordplay evokes the trumpet blare of the brass band that accompanied one of the more pernicious and offensive TV ad campaigns of the 1970s (derived from the 1966 musical Sweet Charity), then you would have had more company than usual at last week’s 2.0 conference in San Francisco.

For all you Gen X’ers, Y’ers, and Millennials pitching your ever more nifty wares this time around: those horrific ads featured a slinky woman – made-over from the ‘60s musical’s stripper chorus to a ‘70s “empowered” glamour-gal – crawling all over some dude in a tux and singing “Hey, Big Spender, spend a little time with me.” The ads were unambiguous proof that American culture’s direct equation of cash and sex pre-dated the 1980s.

The “Known Spenders” who spent a little time at Health 2.0 this year were, for the most part, old enough to remember that ad. And they are actually make a living today working in corporate health care jobs. They’re the people they call “The Suits” in Hollywood, and they can actually get your products out of beta and into the real world. The slow steady creep of relevance not just of Health 2.0 as a marker of the market, but of the entire dream of consumer health IT, can be measured by the slow steady influx of the salt-and-pepper folks my own age who work for health insurance companies, employer groups, hospital systems, and drug companies. Six years ago, at the inaugural 2.0, The Suits were nowhere in sight. This year, they were everywhere you looked, kicking tires and taking business cards. Skepticism was abundant among those I talked with, as it should be with industry lifers who have endured two full cycles of health IT hype. (Healtheon and Revolution Health were the market toppers of valuation, grandiosity, and absurdity; if the current boom goes bust, we lifers know exactly who it will be.)

Among the two dozen or so people I’ve known over the years and who have yet to be paroled from health care, the consensus at 2.0 was “these are mostly good products, not companies, there is too much overlap, they have too narrow a scope of functionality, and many need to be rolled up. But a few actually have replacement revenue potential.”

As for the first part of that consensus, nothing new here. Nor anything new about the classic chicken-and-revenue problem that has hampered Health 2.0 start-ups from the start. I’m hardly the first, and surely won’t be the last, to point out the obvious: health care is not lacking for great consumer information products, services, systems, or apps; those products etc. are lacking users, adoption, exposure, traffic, critical mass, revenue. By “revenue” I mean “cash,” from paying customers, not promises, sales pipelines, booked revenue, or even signed contracts with guarantees. And I certainly don’t mean investors’ cash. I’m talking about revenue from consumers, patients, providers, or any of the myriad third parties who are spending money today – just not happily.

Continue reading “Hey, Known Spender!”

By David C. Kibbe, MD  & Vince Kuraitis

Everywhere 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”

By Russ Richmond, MD

In an article posted earlier this year on this blog I argued that hospitals have traditionally done a sub-par job of leveraging what has now been dubbed “big data.” Effectively mining and managing the ever rising oceans of data presents both a major challenge – and a significant opportunity – for hospitals.

By doing a better of job connecting the dots of their big data assets, hospital management teams can start to develop the crucial insights that enable them to make the right and timely decisions that are vital to success today. And, better, timelier decisions lead to improved results and a higher level of quality patient care.

That’s the good news. The less than positive story is that hospitals are still way behind in using the mountains of data that are being generated within their institutions every day. Nowhere is this more apparent than in the advanced data management practice of predictive modeling.

At its most basic, predictive modeling is the process by which data models are created and used to try to predict the probability of an outcome. The exciting promise of predictive modeling is that it literally gives hospitals the ability to see into (and predict) the future. Given the massive changes and continuing uncertainty that are buffeting all sectors of the healthcare industry (and especially healthcare providers), having a clearer future view represents an important strategic advantage for any hospital leader.

Continue reading “Using Predictive Modeling to Make Better Decisions”

By David Shaywitz, MD

In a piece just posted at TheAtlantic.com, I discuss what I see as the next great quest in applied science: the assembly of a unified health database, a “big data” project that would collect in one searchable repository all the parameters that measure or could conceivably reflect human well-being.

I don’t expect the insights gained from these data will obsolete physicians, but rather empower them (as well as patients and other stakeholders) and make them better, informing their clinical judgment without supplanting their empathy.

I also discuss how many companies and academic researchers are focusing their efforts on defined subsets of the information challenge, generally at the intersection of data domains.  I observe that one notable exception seems to be big pharma, as many large drug companies seem to have decided that hefty big data analytics is a service to be outsourced, rather than a core competency to be built.  I then ask whether this is savvy judgment or a profound miscalculation, and suggest that if you were going to create the health solutions provider of the future, arguably your first move would be to recruit a cutting-edge analytics team.

The question of core competencies is more than just semantics – it is perhaps the most important strategic question facing biopharma companies as they peer into a frightening and uncertain future.

Continue reading “Time For Biopharma To Jump On The “Big Data” Train?”

By David Harlow

HealthCamp Boston is a forum for people with interest in all areas of health and wellness to gather, to generate ideas, and to take practical steps towards building the future of health care. HealthCamps are different from traditional conferences where speakers talk at you. At HealthCamp Boston, an “unconference,” attendees set the agenda, and all contribute to the event according to their interests.

The Boston area is a center of innovation for all aspects of health care, so you can be certain that people at HealthCamp Boston will be discussing things like:

· Big Data in health care

· Improving engagement and outcomes through mobile devices and social media

· Personalized medicine and translational medicine

· Empowered patients

· Practical impacts of health care reform

· and more…

Continue reading “HealthCamp Boston 2012: Brainstorming the Future of Health Care”

By Jim Golden

There were two interesting developments in the field of social networks for healthcare practitioners last week.  The first was the publication of a paper in JAMA “Variation in Patient-Sharing Networks of Physicians Across the United States”.  The second was the sale of Sermo Physician Network to WorldOne for an undisclosed price.  Sermo had raised $40+m in venture capital prior to sale, making a bet that social networking for physicians could drive value to pharmaceutical and financial firms based on disclosing interactions between members of the network.

If physician behavior and prescribing activity are key to your healthcare business, I think it is important to understand the relationship and differences between these two events.

Sermo bet hard on the Facebook model – physicians would interact on social networks, share knowledge and insight, and third parties could benefit from getting access to those interactions concerning their products or services.  Sermo had also begun expanding its revenue model by providing paid content and sponsored education programs to network members, trying to capture “digital” dollars from life science companies.  Pharma companies are desperately trying to gain advantage through digital advertising campaigns to influence physician prescribing behaviors, and multi-channel marketing efforts including the development of web sites for branded medications.

Continue reading “Physicians Aren’t (Feeling Very) Social”