FHIR Analytics

FHIR Bulk Data - SQL Analytics API

FHIR is normally used to enable access to data one patient or resource at a time, but new FHIR Bulk Data APIs are making population level data transfer and analytics possible. That means, any app can run deep queries that support JOINs, GROUP BYs, and other aggregations that are not possible on FHIR directly.

Synthetic FHIR Bulk Dataset - (~1.2M Patients, ~300M total FHIR DSTU2 resources)
Resource Count
Organization 216
Practitioner 216
ImagingStudy 543,721
AllergyIntolerance     589,067
Patient 1,219,932
Resource Count
Goal 1,923,745
CarePlan 3,236,588
Condition 8,591,047
MedicationOrder      8,961,699
DiagnosticReport 13,654,460
Resource Count
Immunization 15,274,254
Procedure 33,036,892
Encounter 39,701,408
Claim 48,663,107
Observation 199,211,482

Demo Query Executor


Identify patients in Boston aged 10 to 13 with required HPV, TDAP, Meningitis vaccines

Dev Examples

Although FHIR Bulk Data transfer standard will help communicate pop health data between orginazations, it will not enable easy interaction on the data directly. That's why the 1upHealth FHIR API platform supports an ANSI SQL interface into all FHIR data stored. The available tables are:

FHIR Bulk Data Analytics APIs are currently available in the 1up development environment. View the examples below to test in dev. All examples connect to the Athena Query Engine on AWS using JDBC connectivity.
- Java Examples
- Node Examples

Contact us to discuss how this can help your organization

We Are Experts

Our team is literally setting the standards here. We are balloting the FHIR Bulk Data 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.

Legislation & Policy

Under new patient access rules, 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.

Use cases

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