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