STATUS: Active Project
The Office of the National Coordinator for Health Information Technology (ONC)’s project will contribute to the use of electronic health data from health information exchanges (HIEs) for research by implementing data standardization, application programming interfaces (APIs), and privacy-preserving machine learning (ML) techniques.
This project will result in three HIEs with expanded data capacity and interoperability through further adoption of two national data standards - the United States Core Data for Interoperability (USCDI) and the HL7® Bulk FHIR® (Fast Healthcare Interoperability Resources) API to efficiently access large amounts of electronic health data. The machine learning technique split learning will be used to test the suitability of HIE data for research, including COVID-19. These capabilities are foundational to building data infrastructure that can be used by PCOR researchers. Valuable insights and lessons learned from this project will be disseminated as resources tailored for researchers and stakeholders that can apply these methods at other HIEs.
PROJECT PURPOSE & OBJECTIVES
The purpose of this project is to address the following objectives:
- Upgrade HIE infrastructure to support PCOR by
- Implementing USCDI, a nationally recognized data standard for interoperable health information exchange and
- Implementing Bulk FHIR API to facilitate efficient data access for health systems and providers.
- Test the use of split learning, a machine learning technique, to facilitate privacy-preserving data sharing to conduct PCOR and COVID-19 related analysis using HIE data.
- Disseminate resources and lessons learned to support the adoption of data standards, technology, and methods used in this project among HIEs and to encourage PCOR researchers to understand and explore HIE data as a source for research.