Secure Data Sharing and Related Matters – An NIH View

Secure Data Sharing and Related
Matters – An NIH View
Philip E. Bourne, PhD, FACMI
Associate Director for Data Science
National Institutes of Health
October 26, 2015

Disclaimer…
I am not a cybersecurity expert, and
as an informatician previously
working primarily in the pre-clinical
space not an expert in security
associated with human subjects

Conversation Cards
 What is happening that makes a
discussion of security important
 What is secure anyway?
 How is the NIH responding in this
changing landscape

Big Data in the Life Sciences …
This speaks to something more
fundamental that more data …
It speaks to new methodologies, new
skills, new emphasis, new cultures,
new modes of discovery …

Consider this change from my own
career experience ….

The History of Computational
Biomedicine According to Bourne
1980s 1990s 2000s 2010s 2020
Discipline:
Unknown Expt. Driven Emergent Over-sold A Service A Partner A Driver
The Raw Material:
Non-existent Limited /Poor More/Ontologies Big Data/Siloed Open/Integrated
The People:
No name Technicians Industry recognition data scientists Academics
Searls (ed) The Roots in Bioinformatics Series PLOS Comp Biol

Consider what the expert
prophets are saying …

We are at a Point of Deception …
 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

Example - Photography
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

We Are 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

What Are Some General Implications
of Such a Future?
 Open collaborative science becomes of increasing
importance
 The value of data and associated analytics becomes
of increasing value to scholarship
 Opportunities exist to improve the efficiency of the
research enterprise and hence fund more research
 Cooperation between funders will be needed to
sustain the emergent digital enterprise
 Current training content and modalities will not match
supply to demand
 Balancing accessibility vs security becomes more
important yet more complex

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

“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

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

For the Purposes of this NIH Centric
Digital Discussion:
What is Secure Anyway?
Access to digital research objects
when, how, and by whom are
authorized to access them in
accordance of the wishes of the
owner and/or laws and policies which
define accessibility

Some of the Complexities
 Research objects
– Narrative
– Data – preclinical and
clinical
– Software
– Publications
 Owner
– Individual
– Institution
– Funding agency
– Third party
 Governance
– Federal
– Funding agency
– Institutional
– Third party

Consider This Response from 3
Intersecting Perspectives
Community
Policy
Infrastructure

Laying the Foundation for Open
Access:
HGP, Bermuda, 1996

“The HGP changed the norms around data sharing
in biomedical research.”
“The HGP changed the norms around data sharing
in biomedical research.”

Data Sharing Goes Global: GA4GH
Global Alliance for Genomics and
Health
 Accelerating the potential of genomic medicine to
advance human health, by:
– Establishing common framework of approaches to enable
effective, responsible sharing of genomic and clinical data
– Catalyzing data sharing projects that drive and demonstrate
value of data sharing
 Alliance*: >350 leading institutions (healthcare, research,
advocacy, life science, IT) representing 35 countries
 Working groups (Clinical, Data, Security, Regulatory &
Ethics) assess, prioritize needs
– Form task teams to produce tools, solutions, demonstration
projects
*Statistics as of October 5, 2015

A Culture of Sharing
1999 20042003 2007 20142008
Research
Tools
Policy
NIH Data
Sharing Policy
Model
Organism
Policy
Genome-wide
Association
(GWAS) Policy
2012
NIH Public
Access Policy
(Publications)
Big Data to
Knowledge
(BD2K) Initiative
Genomic Data
Sharing (GDS)
Policy
Modernization of
NIH Clinical
Trials
White House
Initiative
(2013 “Holdren
Memo”)

Guiding Principle of NIH GWAS Policy
The greatest public benefit will be
realized if data from GWAS are made
available, under terms and conditions
consistent with the informed consent
provided by individual participants, in a
timely manner to the largest possible
number of investigators.
NIH expectation that data would be shared in the
NIH database of Genotype and Phenotype (dbGaP)

Data Access Requests Per Year
2007–September 2015

NIH Public Access Policy for Publications
 Ensures public access to published results of all
research funded by NIH since 2008
– Recipients of NIH funds required to submit final peer-
reviewed journal manuscripts to PubMed Central (PMC)
upon acceptance for publication
– Papers must be accessible to the public on PMC no later
than 12 months after publication

Harnessing Data to Improve Health:
BD2K (Big Data to Knowledge)
NIH’s 6-year initiative to use data science to foster an
open digital ecosystem that will accelerate efficient,
cost-effective biomedical research to enhance health,
lengthen life, and reduce illness and disability
Programs and activities:
Advance discovery for biomedical research
Facilitate use and re-use of biomedical data
Develop analytical methods and software
Enhance biomedical data science training

NIH Genomic Data Sharing (GDS)
Policy
 Purpose
– Sets forth expectations, responsibilities that ensure broad,
responsible sharing of genomic research data in a timely
manner
 Scope
– All NIH-funded research generating large-scale human or
non-human genomic data – and their use for subsequent
research
• Data to be submitted to NIH-designated data repositories
(e.g., dbGaP, GEO, GenBank, WormBase, FlyBase, Rat
Genome Database)
– Applies to all funding mechanisms (grants, contracts,
intramural support) with no minimum threshold for cost
 Released August 2014; effective January 25, 2015
gds.nih.gov

Modernizing NIH Clinical Trials
Activities:
The Need
 NIH-Funded trials published within 100 months of
completion
Less than 50% published within 30 months of completion
BMJ 2012;344:d7292

Modernizing NIH Clinical Trials
Activities:
Call to Action

Increasing Clinical Trial Transparency
Proposed November 2014; Final Spring 2016 (est.)
 Notice of Proposed Rulemaking: Clinical Trials
Registration and Results Submission (FDAAA, Section
801)
– Further implements statutory requirements on private and
public sponsors to register; report results on phase 2, 3,
and 4 trials
– Includes drugs, biologics, and devices (except small
feasibility)
 Draft NIH Policy on Clinical Trial Information
Dissemination
– Extends Section 801 requirements to all NIH-funded clinical
trials
– Includes phase 1 trials and trials of non-FDA regulated
interventions such as behavioral trials

BD2K
Center
BD2K
Center
BD2K
Center
BD2K
Center
BD2K
Center
BD2K
Center
DDICC
Software
Standard
s
Infrastructure - The
Commons
Labs
Labs
Labs
Labs

The Commons
Digital Object Compliance: FAIR
 Attributes of digital objects in the Commons
 Initial Phase
• Unique digital object identifiers of some type
• A minimal set of searchable metadata
• Physically available in a cloud based Commons provider
• Clear access rules (especially important for human subjects data)
• An entry (with metadata) in one or more indices
– Future Phases
• Standard, community based unique digital object identifiers
• Conform to community approved standard metadata for enhanced
searching
• Digital objects accessible via open standard APIs
• Are physically and logical available to the commons

BD2K Targeted Software Topics
Supports innovative analytical methods and software tools
that address critical current and emerging needs of the
biomedical research
2015 Topics (18 awards, U01s)
– Data Compression
– Data Provenance
– Data Visualization
– Data Wrangling
2016 Topics (U01s, under review)
– Data Privacy
– Data Repurposing
– Applying Metadata
– 2016: Crowdsourcing and interactive Digital Media
(UH2)

I not only use all the brains
I have, but all I can borrow.
– Woodrow Wilson

NIHNIH……
Turning Discovery Into HealthTurning Discovery Into Health
philip.bourne@nih.gov
https://datascience.nih.gov/
http://www.ncbi.nlm.nih.gov/research/staff/bourne/

Secure Data Sharing and Related Matters – An NIH View

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