A Successful Academic Medical
Center Must be a Truly Digital
Enterprise
Philip E. Bourne, PhD, FACMI
Associate Director for Data Science
National Institutes of Health
Nina Matheson Lecture
AAMC November 7, 2015
Available on Slideshare

My Experiences
https://commons.wikimedia.org/wiki/File:Geisel_West,_UCSD.JPG
21 Years 1.8 Years

What is My Job?
Change the Culture of NIH
What Do I Do Next Week?

The NIH Data Timeline
6/12 2/14 3/14
• Recommendations:
• Sharing data & software through catalogs
• Support methods and applications development
• Need more training
• Need campus-wide IT strategy
• Hire CSIO
• Continued support throughout the lifecycle
11/15

A Question I ask Myself A Lot…
Are we at a point of deception soon to
see a major disruption to our
institutions?

Some Folks Think So…
 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

For Academic Medical Centers What
Are the Implications of Such a Future?
 Opportunities exist to improve the efficiency and
value of the enterprise
 Open collaborative science becomes of increasing
importance
 The value of data and associated analytics becomes
of increasing value to scholarship
 Current training content and modalities will not match
supply to demand
 Balancing accessibility vs security becomes more
important yet more complex

For Academic Medical Centers What
Are the Implications of Such a Future?
 Opportunities exist to improve the efficiency and
value of the enterprise
 Open collaborative science becomes of increasing
importance
 The value of data and associated analytics becomes
of increasing value to scholarship
 Current training content and modalities will not match
supply to demand
 Balancing accessibility vs security becomes more
important yet more complex

Hypothetical Example of That Value
 Jane scores extremely well in parts of her graduate on-line neurology class.
Neurology professors, whose research profiles are on-line and well described, are
automatically notified of Jane’s potential based on a computer analysis of her scores
against the background interests of the neuroscience professors. Consequently,
professor Smith interviews Jane and offers her a research rotation. During the
rotation she enters details of her experiments related to understanding a widespread
neurodegenerative disease in an on-line laboratory notebook kept in a shared on-line
research space – an institutional resource where stakeholders provide metadata,
including access rights and provenance beyond that available in a commercial
offering. According to Jane’s preferences, the underlying computer system may
automatically bring to Jane’s attention Jack, a graduate student in the chemistry
department whose notebook reveals he is working on using bacteria for purposes of
toxic waste cleanup. Why the connection? They reference the same gene a number
of times in their notes, which is of interest to two very different disciplines – neurology
and environmental sciences. In the analog academic health center they would never
have discovered each other, but thanks to the Digital Enterprise, pooled knowledge
can lead to a distinct advantage. The collaboration results in the discovery of a
homologous human gene product as a putative target in treating the
neurodegenerative disorder. A new chemical entity is developed and patented.
Accordingly, by automatically matching details of the innovation with biotech
companies worldwide that might have potential interest, a licensee is found. The
licensee hires Jack to continue working on the project. Jane joins Joe’s laboratory,
and he hires another student using the revenue from the license. The research
continues and leads to a federal grant award. The students are employed, further
research is supported and in time societal benefit arises from the technology.
From What Big Data Means to Me JAMIA 2014 21:194

How to Get There?
 Recognize an institutions
assets are increasingly digital
 Recognize the value of those
assets
 Recognize that those assets
are siloed
 Put in place a governance,
financial and infrastructure
model that breaks down
those silos while maintaining
community trust
 That is, protect the integrity
of the assets
http://cdn.makeagif.com/media/4-01-2014/Km_F3w.gif

Today’s Data Landscape
Educational
Data
Administrative
Data
Preclinical
Research
Data
Clinical
Research
Data

Consider the NIH Governance Model
NIH Director
Scientific Data Council
Working Groups
Advisory
Committee

Example Work Product

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

Other Areas I Hope the SDC Will
Address
 Sharing of other data types
 Machine readable data sharing plans
 Data citation

For Academic Medical Centers What
Are the Implications of Such a Future?
 Opportunities exist to improve the efficiency and
value of the enterprise
 Open collaborative science becomes of increasing
importance
 The value of data and associated analytics becomes
of increasing value to scholarship
 Current training content and modalities will not match
supply to demand
 Balancing accessibility vs security becomes more
important yet more complex

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

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”)

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

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”)

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

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: Components

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

For Academic Medical Centers What
Are the Implications of Such a Future?
 Opportunities exist to improve the efficiency and
value of the enterprise
 Open collaborative science becomes of increasing
importance
 The value of data and associated analytics becomes
of increasing value to scholarship
 Current training content and modalities will not match
supply to demand
 Balancing accessibility vs security becomes more
important yet more complex

Avoid The Google Bus

BD2K and Clinical Data Science Research
 BD2K Centers of Excellence for Big Data
Computing
 BD2K Targeted Software Topics
 Challenges and Prizes
1. NIH-NSF IDEAS Lab
• Promotes New Collaborations
• Round 1 on Precision Medicine (August 2015), round 2 in
planning.
2. BD2K-Wellcome Trust-HHMI Open Science Prize
• Prize competition announced October 20, 2015.
• Supports development of technology platforms and tools that
make open biomedical data more discoverable, accessible,
analyzable, and citable

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)

For Academic Medical Centers What
Are the Implications of Such a Future?
 Opportunities exist to improve the efficiency and
value of the enterprise
 Open collaborative science becomes of increasing
importance
 The value of data and associated analytics becomes
of increasing value to scholarship
 Current training content and modalities will not match
supply to demand
 Balancing accessibility vs security becomes more
important yet more complex

The BD2K Training and Diversity
Landscape

For Academic Medical Centers What
Are the Implications of Such a Future?
 Opportunities exist to improve the efficiency and
value of the enterprise
 Open collaborative science becomes of increasing
importance
 The value of data and associated analytics becomes
of increasing value to scholarship
 Current training content and modalities will not match
supply to demand
 Balancing accessibility vs security becomes more
important yet more complex

The Problem Statement
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

The Landscape
 The Holdren Memo
 Revisions to the Common Rule
 Meaningful Use
 Centralized IRBs
 ….

Let Me Close on A Promising Note

“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

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

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

The Team
45

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/

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”)

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”)

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

Consider This Response from 3
Intersecting Perspectives
Community
Policy
Infrastructure

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)

Why Revisions to the Common Rule
 is not sufficiently risk-based, resulting in both over- and
under-regulation of research activities;,,
 is not tailored to new and emerging areas of research,
including social and behavioral research and research
involving the collection and use of genetic information
Infectious Disease Society of America. Grinding to a halt:
The effects of the increasing regulatory burden on
research and quality improvement efforts.
 may not effectively inform subjects of psychological,
informational, or privacy risks;,, ,
 does not adequately account for the needs of a “learning”
health-care system for continual quality improvement;,,
and
 provides insufficient mechanisms to ensure the
consistency, quality, and accountability of IRB decision-
making.,,,