The NIH Pragmatic Trials Collaboratory has launched a new learning module, Healthcare Data Interoperability and Standardization for Research, exploring the complexities of collecting, storing, and transforming healthcare data in the electronic health record to achieve optimal patient and research outcomes.
The learning module, which was developed by members of the NIH Collaboratory’s Electronic Health Records Core Working Group, comprises 2 new learning topic videos, “The Big Picture: Healthcare Data and Interoperability” and “Common Data Models.” The module is led by Lesley Curtis of Duke University, a co–principal investigator of the NIH Collaboratory Coordinating Center.
“Research requires a common data structure that can be meaningfully compared across sources,” Curtis explained. “To be useful beyond patient care, complex and variable healthcare data must be organized and standardized,” she said.
The first video in the module, “The Big Picture: Healthcare Data and Interoperability,” covers the key concepts of the Fast Healthcare Interoperability Resources (FHIR) standard, the US Core Data for Interoperability (USCDI) standard, and the Trusted Exchange Framework and Common Agreement (TEFCA). The module explains that these key elements of healthcare data collection, storage, and transfer are a step toward greater interoperability for the US healthcare system.
“Common Data Models” explores the tools and processes available for transforming healthcare data into research data. The video introduces common data models such as Sentinel, OMOP, and PCORnet and explains how each model accomplishes the goal of transforming raw data into a standard format, curating this data for accuracy and completeness, and phenotyping for consistency.
“By employing frameworks that improve healthcare data interoperability and using tools to standardize data structure, we move closer to unlocking the full potential of healthcare data,” Curtis said.