Uncertain Health in
an Insecure World – 31
“Boundary Crossing”
Mark Zuckerberg was a psychology major at Harvard. As a social media mogul at the
forefront of digital culture change, Zuckerberg recently revealed that one secret to his company’s
success has been converting the anonymous barrier-laden internet of history
(remember early chat rooms like 1970’s Talkomatic
and 1980’s CompuServe CB Simulator?) into the psychologically compelling and engaging personal data-sharing platform that is Facebook.
Sitting among one hundred North American medical school
deans and hospital CEO’s, we have again heard of THE PROMISE of scientific innovation
to offer personalized, precision medicine for complex chronic diseases. For decades, we have advanced fundamental studies of the genome
(genomics) and its products (proteomics) as a way of mapping
disease-predisposing pathways – from symptoms to molecule science.
Increasingly, there have been attempts to connect these data to clinical (phenotype) information about real people that is captured in electronic medical
records (EMR’s) and national healthcare databases – from singular mutation
syndromes to variable disease presentations.
Thousands of hospitals and millions of healthcare providers
interact with billions of patients around the world to generate zettabytes of
data that bounces electronically from bedside tablets to the Cloud to Big
Pharma repositories. Such personal health information (PHI) is collected under
privacy rules and public policies that encrypt computer access and de-identify data under
penalty of law. While well-intended, from the onset this privacy protection environment
has constrained scientific contributions of PHI data to healthcare. Of note, EMR
documentation demands (often tied to reimbursement) are also strongly linked to a 45%
average U.S. physician burnout rate!
THE INNOVATOR Dr. Stephen Friend of Sage Bionetworks spoke to our group
about “maximizing the flow of relevant
insights for maximizing impact…” as the ideal framework for future
crowd-sourced network modeling approaches. https://www.ted.com/talks/stephen_friend_the_hunt_for_unexpected_genetic_heroes?language=en
Friend’s innovative approach to studying complex diseases involves three breakout concepts:
Friend’s innovative approach to studying complex diseases involves three breakout concepts:
·
De-linking the generation of data by biomedical
scientists from data analysis
·
Migrating PHI data generation & sharing from
“guilds of experts” to patients
·
Converting fixed institutional EMR platforms to
ubiquitous hand-held technologies
Among the novel tools under development by the
non-for-profit (NFP) Sage consortium is Synapse,
a portal for accessing transparent reproducible datasets that serves as the “sandbox” for studying different problems
using common scientific language. This approach, heavily borrowed from the
GitHub used by physicists and engineers, incents & rewards attacking
challenges collaboratively through sharing scientific communities and
open-access peer review.
In addition to studying complex diseases like cancers,
psychoses, Dengue Fever, Alzheimer’s Disease and ALS, the Synapse consortium asks, “Why
are healthy people staying healthy?” They have screened genetic sequences
from nearly 600,000 highly-penetrated genetic diseases to selectively identify
50 candidate mutations that eventually yielded 15 “hero” gene sequences that appear to be protective, at least until
environmental influences are factored into disease resilience.
Sage’s crowd-sourcing research movement is partnering with
high technology firms (Apple) that have already created applications for health
monitoring to develop windows of self-intervention for diseases such as
Parkinson’s.
mPower is one such app that unpacks 100 voice dimensions of subjects saying “Ahhhh” for 5 seconds into their iPhone to predict Parkinson’s disease. This approach is also being used as a method for screening candidates for participant-centered clinical trials (using Stanford IRB e-consent). Other Parkinson’s smartphone apps can measure physical task performance (fingertip tapping) and allow users to report anecdotal “signals” (two glasses of wine improves my symptoms).
mPower is one such app that unpacks 100 voice dimensions of subjects saying “Ahhhh” for 5 seconds into their iPhone to predict Parkinson’s disease. This approach is also being used as a method for screening candidates for participant-centered clinical trials (using Stanford IRB e-consent). Other Parkinson’s smartphone apps can measure physical task performance (fingertip tapping) and allow users to report anecdotal “signals” (two glasses of wine improves my symptoms).
THE POINT is that just like Mark Zuckerberg at Facebook, very
bright scientists in the private and NFP biomedical research sectors have
recognized that the intractable bureaucracy and legal constraints to using
EMR-derived PHI data as the phenotypic profile for linking to disease
predisposition markers mandate an innovative connector approach. As recently revealed
by Hewlett-Packard computer scientists in Palo Alto and as reported in The Economist (“Planet of the Phones”), 80% of humans
will have the power of a supercomputer on their hand-held device by 2020.
THE FUTURE of complex chronic disease science and management has much more to do with “How I can (transparently)
get the rest of the world to help”, instead of hiding behind the “I can’t (legally) tell you this about your
chances of getting sick.”
Mark Zuckerberg’s understanding of psychology was critical to Facebook
encouraging large user communities to voluntarily contribute personal data for
subsequent social mega-trend analytics. Bio-medical research also requires voluntary
PHI contributions by hand-held device users to achieve a fundamental
understanding of the biology of complex diseases.
An analytics full court press on massive big data
repositories is mere chat. The social network medium is the message.
The boundary to complex chronic disease progress at scale is system-imposed health privacy
constraints.
We in the Square need to cross over.
We in the Square need to cross over.
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