FHIR Bulk Data Export
FHIR is normally used to enable access to data one patient or resource at a time, but new FHIR Bulk Data APIs (which use the FHIR $export operator) are making population level data transfer and analytics possible. There are two main use cases 1upHealth supports.
Export any or all of your FHIR data
Run population health analytics on top of this population
You can immediately have access to these features once you've signed up with the 1upHealth APIs. You don't need to setup any distributed file stores, analytic query engines, or indexes. You get that all out of the box on 1up.
​Create a developer account​
1up supports standard SQL queries run on top of FHIR Bulk Data so that you can run population analytics and queries using tools you're accustomed to (e.g., QuickSight, Looker, SQuirreL, etc.). To see how this analytics query engine works, see our FHIR Analytics documentation.
FHIR has the option to query everything associated to an individual patient. This is useful when transmitting batch data or getting the full patient history. 1upHealth supports the FHIR $everything endpoint.
For more information, FHIR $everything page here​
Request the analytics bulk-data endpoint with the FHIR $export operator to retrieve a list of bulk data files for your client application to download.
For more information, FHIR $export docs here​
Our team is literally setting the standards here. We are balloting the FHIR Bulk Data (i.e., FLAT FHIR) specification through the HL7 standards body along with support from the SMART Health IT team. Additionally, we are building THE reference implementation via the $1M LEAP Grant from the US government in our collaboration with Boston Children's Hospital.
CMS is planning to transform its data pipeline to use FHIR and the FHIR Bulk Data specification. Soon millions of patients' medical claims data will be transmitted using the FHIR Bulk Data APIs. What that will ultimately lead to is most payor / provider relationships will lead to the use of these standard methods of data transfer. This standardization will drastically reduce the esoteric knowledge and interfaces currently required to transmit population level electronic health information.
Numerous use cases for bulk electronic health data transfer and analytics can be supported. Many examples solve or improve upon existing needs using a standards based approach and others will unlock the future of healthcare.
Population health analytics for managing risk or risk adjustments
Reporting on quality and costs
Multi EHR or data ware house integrations
Automating reporting for audits or other partners
Anonymized research data sets for public health
Public health surveillance
Network referrals and leakage analysis
Calculating HEDIS measures
Extracting features for machine learning models and, one day, decisions made by artificial intelligent doctor agents
