Download issue (PDF) - Nieman Foundation - Harvard University

Search for True North
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 KENNETH S. KOSIK
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 clear. For example, let’s
take treatment decisions with a newly
approved medication for Alzheimer’s
disease. To get approved by the FDA,
42 Nieman Reports | Winter 2008
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
Within the potential of social networks
lies untapped wiki knowledge poised to
challenge the experts by opening wide the
collective knowledge gate.
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 diseaseoriented
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. The statistical power of a
population, i.e. the ability to distinguish
between an experimental and control
group, when posed a research question
often depends on having a sufficiently
large study group. The best way to