Elaine Collier, M.D. Data Standards in Clinical Trials: Maximizing Innovation by

Standardization meeting October 19, 2012

§  Research Match – national recruitment registry

§  REDCap platform

§  SHRINE – federated clinical data repositories

§  Researcher/People Networking – VIVO/Profiles/Loki

§  Resource Discovery – eagle-i, open-i, LAMBHRI, NIF

§  CTSA Connect – semantic framework for collaboration platforms

§  Standards and standardized methodologies!

Researchmatch.org  •  Na-onal  Volunteer  and  Researcher  Matching  Tool  •  Incorporates  invisible  standards    

v    Medical  Condi-ons,  Medica-ons,  Demographics  v Geo  coding  v UMLS  

New Researcher Dashboard

New Volunteer Dashboard Features

Volunteers can share experiences and research ideas

Customized pages based on reported conditions provide aggregate data for all volunteers reporting the same condition

> 472 Consortium Partners; 48 Countries; >62,000 Users; > 46,700 REDCap Projects

REDCap

www.project-redcap.org

Shared Library Encouraging Standardized Collection PROMIS and other validated instruments Data De-Identification Services Data Transfer Services & API Standards mapping Graphical Data Review Double-Data Entry Multi-site Data Collection Participant Scheduling Support Full audit trails and logging

Standards and Standard Methodologies

SHRINE

•  6M  pa*ents,  10B  FACTS:  – Demographics  – Diagnosis  – Medica-ons  – Lab  Results  

•  Reach  N  – Rare  Dx  – Small  Effects  

I2b2  DFCI  

8 Participating Institutions

The Institute of Translational Health Sciences, University of Washington, Seattle

Cincinnati Children’s Hospital Medical Center, Ohio

Wake Forest Baptist Medical Center, Salem, North Carolina

University of California San Francisco

The University of Texas Health Science Center at Houston

Partners HealthCare

University of Michigan Institute for Clinical & Health Research, An Arbor

Boston Children’s Hospital

§  Researcher/People Networking – VIVO/Profiles/Loki

§  Resource Discovery – eagle-i, open-i, LAMHDI, NIF

§  CTSA Connect – semantic framework for collaboration platforms

§  Recommender Systems

How  can  CTSAconnect  be  used?  Examples  1.  Find  inves-gators  who  are  good  candidates  for  

repurposing  a  par-cular  drug  in  a  new  area  

2.  Iden-fy  cohorts  based  on  clinician  exper-se  derived  from  encounter  paLerns  

3.  Provide  the  capability  to  report  on  interdependencies  of  research  investment  outcomes    

4.  Recommender  systems  to  promote  team  science  

Use URIs as names for things. Use HTTP URIs so we can look up the names. Return useful information using standards when someone looks up a name. Link to other URIs so we can discover more things (“follow your nose”). http://www.w3.org/DesignIssues/

LinkedData.html

Recommendation 1: Encourage Research Networking Adoption by Institutions

All CTSAs encourage their institutions to adopt Research Networking

 Implement tool institution-wide with authoritative sources

 Ensure tool chosen:

ü Publishes data in RDF triples

ü Incorporates VIVO ontology

Recommendation 2: Expertise Information Publicly Available

Availability of expertise information:  As general principle, information is made

publicly available as data   Institution policies apply  Value is enhanced by the quality and quantity

of information  Linked Open Data

 Machine readable so widely used  Linked to other data to enhance value

Connec*ng  researchers  based  on  their  stuff  

Image  by  Julie  McMurry  2012  

Underlying  connec*ons  driven  by  mul*ple  seman*c  rela*onships  

§  Facilitate — not duplicate — other translational research activities supported by NIH

§  Complement — not compete with — the private sector

§  Reinforce — not reduce — NIH’s commitment to basic research

§  Enable — not impede — collaboration, conduct, and access to science