The Wikification of Knowledge: A Neuroscientist Explores the Shared Challenges of Medicine and Journalism When It Comes to Gathering Information and Reaching Conclusions in the Era of Social Media

By Kosik, Kenneth S. | Nieman Reports, Winter 2008 | Go to article overview

The Wikification of Knowledge: A Neuroscientist Explores the Shared Challenges of Medicine and Journalism When It Comes to Gathering Information and Reaching Conclusions in the Era of Social Media


Kosik, Kenneth S., Nieman Reports


How do we know what we think we know? To narrow this longstanding epistemological question, let me ask this about the world I generally inhabit--medicine, where I work as a neuroscientist. For questions about medical conditions, two sources of knowledge exist. There is expert knowledge--the kind acquired by those who read the primary scientific papers, examine findings from controlled studies, and who, by virtue of their training and their advanced degrees, carry the weight of authority. The second is what today would be called "wiki" knowledge, the kind that arises from collective experience. Today, the knowledge of the designated expert is increasingly challenged by the collective experience of ever-expanding cybercommunities. In the battle of the blogosphere vs. the expert, the expert seems to be losing ground. This contemporary dialectic represents a challenge for many disciplines, including the journalist, who must decide how to balance expert views with those of the cybercommunity.

Knowledge: Expert Vs. Wiki

When medical findings are announced, whether a new therapy, a new preventive measure, or a new research finding, neither the journalist nor the physician should assume that an expert opinion is definitive. The expert may be "as good as it gets," but the limitations of the expert approach need to be dear. For example, let's take treatment decisions with a newly approved medication for Alzheimer's disease. To get approved by the FDA, the pharmaceutical company had to prove safety and efficacy. But how frequently does the drug fail to work, and do other health-related factors such as lifestyle or coexisting disease or genetic risk affect the likelihood the drug will work? These are difficult questions for the expert. In the case of the most commonly used drug in Alzheimer's disease--donepezil--the physician has no idea about enhanced or diminished benefit in association with other health factors and usually does not mention to the family that many users show no benefit at all.

Perhaps the power of the wiki could provide more depth when one is making a decision about a drug treatment. Certainly, the choice of a medication becomes even more acute for some of the stratospherically priced drugs used in cancer treatment today. So how can we create a wiki-based knowledge environment for medical information? In times past, collective knowledge derived from folk medicine, old wives tales, and anecdotal reports. The number of contributors to collective knowledge in any one community was small and, therefore, the conclusions clinically suspect.

The modern-day version of folk medicine is no longer confined to a small circle of happenstance encounters within the limits of our physical geography. With the disappearance of these boundaries, our links to medical conditions like our own can reach across the globe. Large numbers of people--well beyond the numbers found in most medical studies--can build disease-oriented social networks with layers of added information and with an ease of follow-up to create a living, dynamic wiki. From the network one can cluster individuals in any way desired--by geographic location, by occupation, by response to a medication--and begin to extract patterns and correlations. We can organize and reorganize data and perform statistics based on any parameter we chose and create hypotheses that can then be verified prospectively.

Within the potential of social networks lies untapped wiki knowledge poised to challenge the experts by opening wide the collective knowledge gate. In November, Google announced its new Web tool--Google Flu Trends--which uses people's search clues (entering phrases such as "flu symptoms") to create graphs and maps to predict and show regional outbreaks of the flu.

Can social networks rival what is learned from expert approaches such as controlled studies and disease registries? Sound conclusions in the medical field are based upon statistical significance. …

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