{"id":9240,"date":"2011-05-25T10:22:29","date_gmt":"2011-05-25T15:22:29","guid":{"rendered":"http:\/\/pmedicine.org\/epatients\/?p=9240"},"modified":"2011-05-25T10:23:05","modified_gmt":"2011-05-25T15:23:05","slug":"peer-to-peer-healthcare-crazy-crazy-crazy-obvious","status":"publish","type":"post","link":"https:\/\/participatorymedicine.org\/epatients\/2011\/05\/peer-to-peer-healthcare-crazy-crazy-crazy-obvious.html","title":{"rendered":"Peer-to-peer Healthcare: Crazy. Crazy. Crazy. Obvious."},"content":{"rendered":"

Here’s my simple definition of peer-to-peer healthcare<\/a>:<\/p>\n

Patients and caregivers know things — about themselves, about each other, about treatments — and they want to share what they know to help other people. Technology helps to surface and organize that knowledge to make it useful for as many people as possible. <\/strong><\/p>\n

An idea whose time has come? Let’s think that through, beginning with an excerpt of Kevin Kelly’s post, The Natural History of a New Idea<\/a>:<\/p>\n

The notion that ideas have lifecycles has many antecedents. Various people get credit with first articulating it. Here is my version:<\/p>\n

The Natural History of a New Idea:<\/p>\n

1) Outright wacko.<\/strong>
\n“This is worthless nonsense.”<\/p>\n

2) Odd but unproven.<\/strong>
\n“This is an interesting, but perverse, point of view.”<\/p>\n

3) True but trivial.<\/strong>
\n“This may be correct, but it is quite unimportant.”<\/p>\n

4) Obvious. <\/strong>
\n“What’s new? This is what we’ve said all along.”<\/p>\n

Apply to your favorite example.<\/p>\n

I’ve seen this abbreviated to: “Crazy. Crazy. Crazy. Obvious.” But I think it’s more useful to pay attention to the gradations. Where along this scale is your idea?<\/p>\n

Pew Internet’s research <\/a>has documented people’s interest in sharing what they know (the first part of peer-to-peer healthcare). I’d say we hover between “odd but unproven” and “true but trivial,” with the exception of some health digerati who have literally said “duh<\/a>.”<\/p>\n

Other researchers take patients’ and caregivers’ knowledge to the next level to make it useful. Four recent examples:<\/p>\n

1) Ian Eslick, a PhD candidate at the MIT Media Lab, posted his plans to harness patient-generated information to improve care. Specifically, he will “enable patient communities to convert anecdotes into structured self-experiments that apply to their daily lives.” Read the PDF: Personalized health experiments to optimize well-being and enable scientific discovery<\/a>.<\/p>\n

2) Kristina Doing-Harris and Qing Zeng-Treitler, researchers at the University of Utah, crawled PatientsLikeMe (with the company’s permission) to identify new health terms used by consumers as they discussed their conditions. Read the article: Computer-Assisted Update of a Consumer Health Vocabulary Through Mining of Social Network Data<\/a> (JMIR).<\/p>\n

3) Elissa R. Weitzman, Ben Adida, Skyler Kelemen, and Ken Mandl of Children’s Hospital in Boston created a privacy-preserving social networking software application for members of the TuDiabetes community<\/a> to report and chart hemoglobin A1c values. Read the article: Sharing Data for Public Health Research by Members of an International Online Diabetes Social Network<\/a> (PLoS ONE).<\/p>\n

And finally:<\/p>\n

\n

4) Paul Wicks, Timothy E. Vaughan, Michael P. Massagli, and Jamie Heywood of PatientsLikeMe blew up the idea that double-blind randomized trials are the only valid path to clinical insights. Their study: Accelerated clinical discovery using self-reported patient data collected online and a patient-matching algorithm<\/a> (Nature Biotechnology).<\/p>\n

I often think in metaphors and similes, so forgive the following summary: Eslick harvests clinical insights from naturally-occurring social networks, whereas Weitzman et al. created a farm and invited an existing community to work on it. Wicks et al. not only created a farm, but a community to work on it (as well as inviting other “farmers” along, like Doing-Harris and Zeng-Treitler).<\/p>\n

Then there is the question of scale: micro vs. macro vs. massive. To stretch the metaphor, Eslick is like a mushroom hunter<\/strong>, Weitzman et al. & TuDiabetes operate a co-operative farm<\/strong>, and Wicks et al. & PatientsLikeMe are an agribusiness<\/strong>.<\/p>\n

All of this research is moving peer-to-peer healthcare along the new idea scale.<\/p>\n

In fact, I’m having fun watching people’s reactions (and mine<\/em>) when I describe these new studies:\u00a0 from indifference (bummer, they don’t get it), <\/em>to puzzlement (OK, we’re at least up to “odd, but unproven”<\/em>), to excitement (oh good, let’s talk<\/em>). It speaks volumes to me that the Wall Street Journal<\/em> covered <\/a>the PatientsLikeMe study, for example, and other major news outlets did not.<\/p>\n

A nice article <\/a>in SearchHealthIT connected the TuDiabetes and the PatientsLikeMe studies and featured some intriguing quotes:<\/p>\n

“We found this very high level of what we called ‘information altruism<\/strong>.’ People were willing, in a privacy-preserving model, to make individual decisions about how they were going to share their data.” – Elissa Weitzman<\/p>\n

The word “altruism” jumped out at me, since it resonates with other observations of how people stick around online health communities<\/a> to help other people who come along after them. It also resonated with what Weitzman and Mandl found in a previous study<\/a>: 9 in 10 patients using a personally-controlled health record are willing to share medical information for health research (under certain conditions).<\/p>\n

The other quote encapsulates the difference between PatientsLikeMe and every other online patient site I have seen:<\/p>\n

“It’s easier to add a social network to a clinical research platform than to think about adding a clinical research platform to a social network.” – Jamie Heywood<\/p>\n

But is that 100% true? Ian Eslick’s project is a potential proof that you can <\/em>graft a clinical research platform on to an existing social network.<\/p>\n

When I chatted with Jamie about all this, he pointed me to Kevin Kelly’s mind-blowing “Speculations on the Future of Science<\/a>.” Here’s a quote, but please read the whole thing when you have time:<\/p>\n

New tools enable new structures of knowledge and new ways of discovery. The achievement of science is to know new things; the evolution of science is to know them in new ways. What evolves is less the body of what we know and more the nature of our knowing.<\/p><\/blockquote>\n

\n

My two favorite concepts in the piece are Triple-Blind Experiments<\/strong> (pattern recognition based on streams of data, collected unbeknownst to the population being studied) and Wiki-Science<\/strong> (massive collaborative research will be the first word on a new area).<\/p>\n

What if all the storytelling, discussions, and data-sharing among patients and caregivers could be coded, analyzed, and harvested for insights (as Eslick and Doing-Harris\/Qing Zeng-Treitler discuss)? What if social networking data could join the “big data” party and allow public health researchers to engage in syndromic surveillance<\/strong>, as Mandl et al. presciently described in 2004<\/a>? Is that outright wacko? Or are these new scientific methods on the path to Triple-Blind Experiments?<\/p>\n

What if, instead of running clinical trials on <\/em>patients, scientists ran trials with <\/em>patients (a turn of phrase used by Siddhartha Mukherjee<\/a> to describe Herceptin trials and as Wicks et al. discuss)? Crazy? Or the beginning of Wiki-Science?<\/p>\n

What are your ideas? Where are they along the new idea scale? What are you doing to move them ahead to obvious – and is there a downside to getting there?<\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

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