Big data and data science are having major implications for health policy and management. Precision medicine initiatives that collect and analyze large amounts of genomic and health data from many individuals could lead to medical breakthroughs but also raise issues regarding data sharing, attribution, security, intellectual property, and ethics. As data-driven technologies continue advancing, it will be important to address the ethical, legal and societal implications of areas like algorithm development, artificial intelligence, open science, and business models to ensure data science research benefits society.

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. What is Big Data?

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. 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. 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. “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. 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. An Example of That Promise:
Comorbidity Network for 6.2M Danes
Over 14.9 Years
Jensen et al 2014 Nat Comm 5:4022

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. 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. Data – Security?
Think Uber
Data – IP?

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. 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. NIHNIH……
Turning Discovery Into HealthTurning Discovery Into Health
philip.bourne@nih.gov