Supporting Healthcare Data Standards at Google Cloud
Today was a big day in the world of healthcare data standardization. Health Level Seven International (HL7®) and Observational Health Data Sciences and Informatics (OHDSI) announced a collaboration to better integrate with each other’s data standards: Fast Healthcare Interoperability Resources (FHIR®) and OHDSI’s Observational Medical Outcomes Partnership (OMOP) common data model respectively.
TL;DR this partnership will allow clinical data coming from EHRs, applications, or other sources in the FHIR format to be utilized with research data in the OMOP standard. This has been a common use case - most notably with GT-FHIR2, the OMOP on FHIR Project. This project was built out of Georgia Tech and allows for mapping between FHIR and OMOP common data model (CDM).
Google Cloud has long recognized the importance of each of these standards and has built out products and solutions for both, such as the Google Cloud Healthcare API, ATLAS on GCP, and provides large, synthetic, public datasets in both the FHIR format and the OMOP data model.
Recognizing the need to interoperate between the standards, Google Cloud also formed a partnership with Odysseus Data Services to assist customers with the conversion of data to OMOP, deployment of research applications such as ATLAS on GCP, and jointly supporting the community with outreach and Google tool integration (like BigQuery support).
Most recently, Google Cloud created an open source healthcare data harmonization tool suite to assist users in translating their data to various clinical standards. We are proud to support the conversion of FHIR to OMOP and look forward to seeing what the new partnership between OHDSI and HL7 will bring to fruition!
For those more interested in the technical aspects of our mapping engine…
Mapping Data to OMOP
The Google Cloud Healthcare team has open sourced a mapping engine that can assist users with harmonizing their data to the OMOP CDM (and many others!). Structural transformation is an important step in building a data transformation pipeline and includes field mapping, parsing, and formatting data. With the open source mapping engine and library, users can easily manage their mapping configurations without the overhead of managing the pipelines.
The healthcare team has also open sourced reference mappings for FHIR to OMOP using the Georgia Tech references to accelerate OMOP adoption for those who already have their data in the FHIR standard. This reference mapping can allow researchers to utilize applications built on the OMOP standard or help further analytics in clinical research use cases.
For more information check out the joint Google Cloud/Odysseus HIMSS session conducted on creating transformation pipelines on Google Cloud for the OHDSI toolsuite!
Using the OHDSI toolsuite on GCP
The OHDSI tool can support many analytics use cases, such as cohort definition, cohort generation and population effect estimation using core OHDSI components (Achilles, WebAPI and ATLAS). These tools have been enhanced to interoperate with BigQuery (and other Google Cloud components), and can be enhanced by use of other Google Cloud offerings, such as Google’s published OMOP synthetic patient dataset and on-going open source efforts to map data to the OMOP standard.
“The introduction of the Google BigQuery into the OHDSI ecosystem allows for unlimited scalability and elasticity for multiple OHDSI collaborators who are utilizing Google Cloud Platform (GCP). The ability to host multi-terabyte data sets while getting fast responses for complex analytical queries enables the execution of internal and collaborative network research. Going forward, Google and Odysseus have partnered in providing ongoing support for OHDSI ATLAS and other tools and methods on GCP platform.”
Gregory Klebanov, Chief Technology Officer (CTO) at Odysseus Data Services
To summarize my thoughts… In someone else’s words
“This partnership closes the loop from research to clinical impact, potentially enabling analytics and models validated on real-world observational data to be put into practice within a health system by leveraging the interoperability standards for healthcare data.”
Marianne Slight, Product Manager for Google Cloud Healthcare and Life Sciences
