Co-Chairs: W. Ed Hammond, Jr., Meredith Nahm Zozus, Rachel Richesson
NIH Representative: Jerry Sheehan
Members: Monique Anderson, Alan Bauck, Denise Cifelli, Lesley Curtis, John Dickerson, Pedro Gozalo, Beverly Green, Chris Helker, Michael Kahn, Cindy Kluchar, Reesa Laws, Melissa Leventhal, John Lynch, Rosemary Madigan, Vincent Mor, George "Holt" Oliver, Jon Puro, Alee Rowley, Shelley Rusincovitch, 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 standards, interoperability, 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.
Furthermore, 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 is supporting these efforts across the Collaboratory and making tools available to the wider research community.
The Core’s activities include the following:
Develop phenotype definitions