Co-Chairs: Rachel Richesson and W. Ed Hammond, Jr.
NIH Representative: Jerry Sheehan
Members: Monique Anderson, Alan Bauck, Denise Cifelli, Lesley Curtis, Pedro Gozalo, Beverly Green, Michael Kahn, Reesa Laws, Rosemary Madigan, Meghan Mayhew, Tom Meehan, Vincent Mor, George "Holt" Oliver, Jon Puro, Greg Simon, Kari Stephens, Erik Van Eaton
Project Manager: Michelle Smerek
The secondary use of electronic health record (EHR) data for clinical research requires not only an understanding of data representation, exchange standards, and the influence of workflows, but also the development and implementation of valid approaches for identifying cohorts with clinical conditions. This involves collaboration among clinicians, EHR experts, and informaticians to develop algorithms, or computable phenotypes, for identifying patients with clinical conditions being studied by researchers.
There are many ways to identify patients who have been diagnosed with a specific condition, and understanding the pros and cons of the various approaches is essential for using EHRs effectively in pragmatic clinical trials. Also, comprehensive data characterization and data quality assessment enable investigators to match a research question with data of appropriate quality in order to conduct the research. The Phenotypes, Data Standards, and Data Quality Core supports these efforts across the Collaboratory and makes tools available to the wider research community.
Areas of Focus
Develop and test phenotype algorithms for use within and across projects
Identify data validation best practices
Store generalizable definitions and best practices in an accessible format
Use standards organizations to move these measures into practice
Contribute to a learning healthcare system
Develop a suite of standards appropriate for a collaborating center
Formalize standards through accredited standards-developing organizations
Produce implementation guides that define standards, data elements, format, and coding system