The social crowd has proven to be powerful, if you can find some wayto harness it:crowd-sourcingcan perform tasks and solve collaborativeproblems, crowd-fundingcan raise substantial financing.
I suspect crowd-data will similarly become an effective way to createlarge, realistic databases.
A great application of this is the medical world, where manypeople post to health forums raising medical problems, possible sideeffects from drugs and vaccines, etc. Why not collect all such poststo find previously undiscovered problems? Infact, thispaper describes just that: the authors extracted the nasty sideeffects of statindrugs based on posts to online health forums.Similarly, thisabstract describes a system that used crowd-data to spot nastyside effectsfrom Singulair,years beforethe FDAissued a warning.The VAERSdatabase, which gathers parent-reported problems after childrenreceive vaccines, is another example.
Unfortunately the drug safety trials that take place before a drug can bereleasedare notespecially trustworthy. Here's a scary quote from that interview:
When you look at the highest quality medical studies, the odds that a study will favor the use of a new drug are 5.3 times higher for commercially funded studies than for noncommercially funded studies.
And that was 7 years ago! I imagine the situation has only gotten worse.
When a new drug is released, the true, unbiaseddrug trial begins when millions of guinea-pigs start takingtaking it. Crowd-data makes it possible to draw conclusions from thatthat post-market drug trial.
Of course there are challenging tradeoffs: crowd-data, being derivedfrom "ordinary people" without any rigorous standard collectionprocess, can be dirty, incomplete and reflect sampling bias (only peopleexperiencing nasty side effects speak up). For these reasons,old-fashioned journals turn their noses up at papers drawingconclusions from crowd-data.
Nevertheless, I believe such limitations are more than offsetby the real-time nature and shear scale the millions of people,constantly posting information over time. Inevitably, trustworthypatterns will emerge over the noise. Unlike the synthetic drug trial,this data is as real as you can get: sure, perhaps the drug seemedfine in the carefully controlled pre-market testing, but then out inthe real world, unexpected interactions can suddenly emerge.Crowd-data will enable us to find such cases quickly and reliably, aslong as we still have enough willing guinea-pigs!
Fast forward a few years and I expect crowd-data will be an excellentmeans of drawing conclusions, and will prove more reliable than thecompany-funded pre-market drug trials.![]()
I suspect crowd-data will similarly become an effective way to createlarge, realistic databases.
A great application of this is the medical world, where manypeople post to health forums raising medical problems, possible sideeffects from drugs and vaccines, etc. Why not collect all such poststo find previously undiscovered problems? Infact, thispaper describes just that: the authors extracted the nasty sideeffects of statindrugs based on posts to online health forums.Similarly, thisabstract describes a system that used crowd-data to spot nastyside effectsfrom Singulair,years beforethe FDAissued a warning.The VAERSdatabase, which gathers parent-reported problems after childrenreceive vaccines, is another example.
Unfortunately the drug safety trials that take place before a drug can bereleasedare notespecially trustworthy. Here's a scary quote from that interview:
When you look at the highest quality medical studies, the odds that a study will favor the use of a new drug are 5.3 times higher for commercially funded studies than for noncommercially funded studies.
And that was 7 years ago! I imagine the situation has only gotten worse.
When a new drug is released, the true, unbiaseddrug trial begins when millions of guinea-pigs start takingtaking it. Crowd-data makes it possible to draw conclusions from thatthat post-market drug trial.
Of course there are challenging tradeoffs: crowd-data, being derivedfrom "ordinary people" without any rigorous standard collectionprocess, can be dirty, incomplete and reflect sampling bias (only peopleexperiencing nasty side effects speak up). For these reasons,old-fashioned journals turn their noses up at papers drawingconclusions from crowd-data.
Nevertheless, I believe such limitations are more than offsetby the real-time nature and shear scale the millions of people,constantly posting information over time. Inevitably, trustworthypatterns will emerge over the noise. Unlike the synthetic drug trial,this data is as real as you can get: sure, perhaps the drug seemedfine in the carefully controlled pre-market testing, but then out inthe real world, unexpected interactions can suddenly emerge.Crowd-data will enable us to find such cases quickly and reliably, aslong as we still have enough willing guinea-pigs!
Fast forward a few years and I expect crowd-data will be an excellentmeans of drawing conclusions, and will prove more reliable than thecompany-funded pre-market drug trials.
