Secure Data Sharing and Related Matters – An NIH View

Secure Data Sharing and Related Matters – An NIH View

Philip E. Bourne, PhD, FACMIAssociate 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

Conversation Cards




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

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

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 ObamaJanuary 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

Conversation Cards




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

Conversation Cards




Consider This Response from 3 Intersecting Perspectives

Community Policy

Infrastructure


Community Policy

Infrastructure

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


Community Policy

Infrastructure

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


1999 20042003 2007 20142008





2012







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


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


Community Policy

Infrastructure

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

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

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/

Education

Secure Data Sharing and Related Matters – An NIH View