Culture of Health Blog

This $500K competition seeks proposals for studies that will further our understanding of mood and how it relates to daily life.

Here are a few of my initial ideas from the 2011 Games for Health Conference, put on by Ben Sawyer and Beth Bryant:

1. The sensors are coming, the sensors are coming.  Sensors and the software to collect and analyze the data are on the verge of becoming retail products.  Green Goose is one company hoping to grab this market.  What does it mean for health games?  It means that soon we will be able to turn lots of things into data collection and data input devices.  So think about games that use household objects instead of, say, a Wii remote.  You could turn your house into the playing field for a game.

2. Better seeds and more fertile ground.  The quality of health games—the game mechanics, the theory underlying design is getting better.  At the same time, the context in which we can use games to improve health is expanding.  We can now embed games in social networks, so think about linking someone’s facebook friends in with their game efforts to lose weight or manage their diabetes.

3. There’s likely more than one way for a game that has an impact at scale.  When we got into this space at RWJF, we were thinking about games as therapeutic interventions for individuals—one person with one game addressing one condition that a lot of individuals have.  But building off item 2 above, given what Nicholas Christakis and James Fowler are showing with their research on transmission of health behaviors through social networks, you could design a health game that requires social networks—teams—to play.  Still another level up is a game that could target a health problem that was a function of a system problem.  That is, a game that targeted a health problem that resulted not from someone’s genetics, but from set of processes and systems that are outside of any one individual’s control.  Imagine a game that’s designed to get some producers to make healthier food.

This commentary originally appeared on the RWJF Pioneering Ideas blog.

Are We The Source of Knowledge?

We started hearing about it a couple years ago: an ALS member of Patients Like Me had seen (and translated—it was in Italian) a medical conference poster with results showing lithium carbonate could slow the disease’s progression.  That study was a single-blind trial of 16 treated patients and 28 controls.  The results spread through the ALS community and soon, patients began talking their physicians into prescribing lithium carbonate off-label. PLM soon had 348 members reporting on the effects of their use of the drug.

 PLM realized they had an opportunity to study the experience of their members who were—effectively—experimenting with the drug.  PLM couldn’t randomize, so they developed an algorithm and matched 149 treated patients to 447 controls based on the progression of their disease course.

 On Sunday, the journal Nature Biotechnology published PLM’s findings showing after 12 months of treatment, lithium carbonate had no effect on disease progression.  PLM reports that subsequent clinical trials reached similar conclusions.

 What’s important here is to recognize the potential to conduct research using patient self-reported data from an online social community.  PLM’s sweet spot is social communities for ambiguous diseases (that is, diseases we’re still learning about, diseases that don’t have clear, effective treatment protocols) where the patient does a lot of care at home.  To be sure, PLM is a pretty sophisticated community, but it’s intriguing to think about where we might be in 10-15 years.

A couple of us met last week with PLM’s Jamie Heywood and Dave Clifford.  We had a ranging discussion—hard to avoid with Heywood—that included linking patient self-reported data with clinicians, conducting research with this data, and business models.  A fundamental question Heywood is exploring is “whether it’s faster to get to learning health system through the current confines of the health system or through something like PLM.”

Given the growing ability and inclination of patients to capture and share details on their own experiences, how powerful a role is there for the analysis of this sort of data in our efforts to accelerate the discovery of new treatments for disease?

This commentary originally appeared on the RWJF Pioneering Ideas blog.

I was at the Games for Health meeting in Boston last week. This was the fourth year Pioneer has supported the meeting, which has come a long way since its inception.  When I first attended, most of the conversation I heard was an effort by gamers and health practitioners to each understand the other.  From one side, you heard questions that asked, essentially, “What makes a good game?” From the other side, you heard questions that asked, essentially, “Help me understand diseases, therapies, and how health care works. 

And from both sides, you heard, “When you say X, what exactly do you mean?”

The conversation this year was significantly different.  Instead of talking to each other, people were talking with each other, trying to figure out how to solve problems. Attendees were frequently working off a common language, though some are more fluent than others.

Given that much of the conversation has moved from discovery to collaboration, it has me wondering what’s needed now to move the field along?  The funding we provided under ourHealth Games Research national program focused on establishing efficacy and exploring game design principles.  Does the field need more of that?  Some of the ideas I heard at the Games for Health conference of what was needed now included research to demonstrate cost-effectiveness and the establishment of a journal devoted to the field of health games research.

Any opinions?

This commentary originally appeared on the RWJF Pioneering Ideas blog.

 I’m not a techie.  I’m not an IT guy.  But I found myself among a group of them this weekend at FOO Camp East, put on by O’Reilly Media http://oreilly.com/ (FOO stands for Friends Of O’Reilly.).  It was at an O’Reilly meeting years ago where the term ‘Open Source’ was coined—a good example of how the right language can create both a center of gravity and momentum.

There was some tech talk, some device talk, some website talk, but also a lot of health care talk.  It’s interesting to listen to the not-so-usual-suspects talk about health care.  There is a lot about the system they don’t know.  There’s a lot about the state of policy they don’t know.  There’s a lot about reimbursement, about medical care itself they don’t know.  But I came away thinking it would be foolish to dismiss these folks because of the lot of things they don’t know.  It’s the things they do know and the lens through which they look at problems that are powerful and can be very useful.

They look at data sources and streams, they think about building platforms that can enable innovation broadly (as compared to platforms that enable a single business), they think about engineering systems, about hacks to existing systems and about alternative business models.  In one conversation, it was suggested that instead of just paying going rates for tests to diagnose, we ought to apportion payment as a function of its marginal contribution to diagnosis.  If you’ve already done three tests and ruled out 90% of the serious problems of most concern, the likelihood that the next test will turn up an uncommon yet serious problem is pretty low.  So why do a CAT scan?  Under this approach, if you want to do a CAT scan, fine, but you only get 10% of the going rate.  Now, I’m not necessarily advocating this approach, but it was fun to think about it for a while.

My guess is that 15, 10, maybe even five years ago, most of these folks would not have been much interested in health care as an opportunity because the data sources were not robust enough, the social networking platforms were too narrow—there just wasn’t enough infrastructure.  Now, I suspect there is enough.  Is health care ready?

This commentary originally appeared on the RWJF Pioneering Ideas blog.