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:

·         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).

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.