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Health Policy and Management as it Relates to Big Data

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Lecture given to graduate students at NIH, October 11, 2016

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Health Policy and Management as it Relates to Big Data

  1. 1. Health Policy and Management as it Relates to Data Science October 11, 2016 Philip E. Bourne PhD, FACMI philip.bourne@nih.gov http://www.slideshare.net/pebourne
  2. 2. What is Big Data?
  3. 3. Why is it Important?  Evidence: – Google car – 3D printers – Waze – Robotics – Sensors From: The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies by Erik Brynjolfsson & Andrew McAfee
  4. 4. What Are the Implications? Digitization Deception Disruption Demonetization Dematerialization Democratization Time Volume,Velocity,Variety Digital camera invented by Kodak but shelved Megapixels & quality improve slowly; Kodak slow to react Film market collapses; Kodak goes bankrupt Phones replace cameras Instagram, Flickr become the value proposition Digital media becomes bona fide form of communication
  5. 5. Are We At a Point of Deception? The 6D Exponential Framework Digitization of Basic & Clinical Research & EHR’s Deception We Are Here Disruption Demonetization Dematerialization Democratization Open science Patient centered health care
  6. 6. “And that’s why we’re here today. Because something called precision medicine … gives us one of the greatest opportunities for new medical breakthroughs that we have ever seen.” President Barack Obama January 30, 2015 Possible Disruptor – Precision Medicine
  7. 7. Precision Medicine Initiative  National Research Cohort – >1 million U.S. volunteers – Numerous existing cohorts (many funded by NIH) – New volunteers  Participants will be centrally involved in design and implementation of the cohort  They will be able to share genomic data, lifestyle information, biological samples – all linked to their electronic health records
  8. 8. An Example of That Promise: Comorbidity Network for 6.2M Danes Over 14.9 Years Jensen et al 2014 Nat Comm 5:4022
  9. 9. Policies - Sharing  Data Sharing – Holdren memo – Genomic data sharing policy implemented – Data sharing plans on all research awards 2016 – Data sharing plan enforcement • Machine readable plan • Repository requirements to include grant numbers http://www.nih.gov/news/health/aug2014/od-27.htm
  10. 10. Policies - Attribution  Data Citation – Goal: legitimize data as a form of scholarship – Process: • Machine readable standard for data citation (done) • Endorsement of data citation for inclusion in NIH bib sketch, grants, reports, etc. • Example formats for human readable data citations • Slowly work into NLM/NCBI workflow  dbGaP in the cloud (done!)
  11. 11. Data – Security? Think Uber Data – IP?
  12. 12. Ethical Legal & Societal Implications (ELSI)  Ethical, legal, and societal implications of data algorithm and analytic software approaches and their implementation  Ethical, legal, and social implications of machine learning approaches and artificial intelligence  Bioethical, legal, and societal implications of systemic change of data acquisition, storage, management, and use  Ethical and societal issues impacting data sharing, integration, and mining  Ethical issues in crowdsourcing data and software
  13. 13. Ethical Legal & Societal Implications (ELSI) (cont.)  Ethical challenges in open science; citizen participation  Ethical challenges of data science research in human subjects protection regulatory environment  Ethical challenges of data science in therapeutic/drug/device development regulatory environment  Ethical and legal issues in business models for data science research and collaboration  Ethical challenges in interdisciplinary and collaborative research  Ethical issues related to new and emerging data technologies
  14. 14. NIHNIH…… Turning Discovery Into HealthTurning Discovery Into Health philip.bourne@nih.gov