a successful academic medical center must be a truly digital enterprise

A Successful Academic Medical Center Must be a Truly Digital

Enterprise

Philip E. Bourne, PhD, FACMIAssociate Director for Data Science

National Institutes of Health

Nina Matheson LectureAAMC 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

DigitizationDeception

Disruption

Demonetization

Dematerialization

Democratization

Time

Vol

ume,

Vel

ocity

, Var

iety

Digital camera invented byKodak but shelved

Megapixels & quality improve slowly; Kodak slow to react

Film market collapses;Kodak goes bankrupt

Phones replacecameras

Instagram,Flickr become thevalue 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

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

EducationalData

AdministrativeData

PreclinicalResearch

Data

ClinicalResearch

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

Laying the Foundation for Open Access:

HGP, Bermuda, 1996

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

Data Sharing Goes Global: GA4GHGlobal 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


1999 20042003 2007 20142008





2012







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


1999 20042003 2007 20142008





2012







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 researchFacilitate use and re-use of biomedical dataDevelop analytical methods and softwareEnhance biomedical data science training

BD2KCenter

BD2KCenter

BD2KCenter

BD2KCenter

BD2KCenter

BD2KCenter

DDICC

Software

Standards

Infrastructure - The Commons

Labs

Labs

Labs

Labs

The Commons: Components

The CommonsDigital 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

Avoid The Google Bus

BD2K and Clinical Data Science Research

BD2K Centers of Excellence for Big Data Computing

BD2K Targeted Software Topics

Challenges and Prizes1. 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 TopicsSupports 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)

The BD2K Training and Diversity Landscape

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 ObamaJanuary 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

[email protected]://datascience.nih.gov/

http://www.ncbi.nlm.nih.gov/research/staff/bourne/

mailto:[email protected]

https://datascience.nih.gov/


1999 20042003 2007 20142008





2012







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.,,,