Electronic Health Records
Co-Chairs: Rachel Richesson, Keith Marsolo
NIH Representatives: Jerry Sheehan
Members: Nick Anderson, Arne Beck, Srinivasan Beddhu, Andrea Cheville, Lesley Curtis, Dana Dailey, Kim Faurot, Pedro Gozalo, Ed Hammond, Trevis Huff, Michael Kahn, Maxine Koepp, Andrea Kline-Simon, Josh Lakin, Julie Lima, Charles Lu, Andy MacKelfresh, David Magid, Devin Mann, Kathleen McTigue, Vincent Mor, Brett Moran, George "Holt" Oliver, Hyung Paek, Alice Pressman, Stacy Sterling, Jordan Swartz, Erik Van Eaton, Ferdinand Velasco, Angelo Volandes
Project Manager: Kady-Ann Steen-Burrell
The ability to harness electronic health data is transforming the way clinical research is conducted. The Electronic Health Records (EHR) Core’s goal is to facilitate multisite research collaborations between investigators and data stewards. Core members have expertise in data models, data standards and quality, algorithms, and approaches to define clinical phenotypes, extract information, define endpoints, and discover errors in data from healthcare systems.
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 EHR 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
On the use of EHR data, data capture issues, quality assessment, and statistical approaches
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
Rachel Richesson, PhD, Duke University School of Nursing, describes recent updates from the Collaboratory’s EHR Core (formerly the Phenotypes, Data Standards, and Data Quality Core).
Data and Resource Sharing
- Onboarding Data and Resource Sharing Questionnaire
- Closeout Data and Resource Sharing Checklist
- Phenotype Case Study: MURDOCK Trial
- 10/11/2017: Reflections on the First 5 Years of the Phenotypes, Data Standards, and Data Quality Core