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Here’s my simple definition of peer-to-peer healthcare:

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.

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:

The notion that ideas have lifecycles has many antecedents. Various people get credit with first articulating it. Here is my version:

The Natural History of a New Idea:

1) Outright wacko.
“This is worthless nonsense.”

2) Odd but unproven.
“This is an interesting, but perverse, point of view.”

3) True but trivial.
“This may be correct, but it is quite unimportant.”

4) Obvious.
“What’s new? This is what we’ve said all along.”

Apply to your favorite example.

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?

Pew Internet’s research 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.”

Other researchers take patients’ and caregivers’ knowledge to the next level to make it useful. Four recent examples:

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.

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 (JMIR).

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 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 (PLoS ONE).

And finally:

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 (Nature Biotechnology).

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).

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

All of this research is moving peer-to-peer healthcare along the new idea scale.

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

A nice article in SearchHealthIT connected the TuDiabetes and the PatientsLikeMe studies and featured some intriguing quotes:

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

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

The other quote encapsulates the difference between PatientsLikeMe and every other online patient site I have seen:

“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

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

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

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.

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

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, as Mandl et al. presciently described in 2004? Is that outright wacko? Or are these new scientific methods on the path to Triple-Blind Experiments?

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

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?


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