Benchmarking Fast Healthcare Interoperability Resources-Based Analytics: Quantitative Study of RESTful Server Queries
Christian Gulden1,2, Marvin Kampf3, Detlef Kraska3
1Lehrstuhl für Medizinische Informatik, Institut für Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universität-Erlangen-Nürnberg, Erlangen, Germany.
JMIR Medical Informatics
|July 18, 2026
Summary
Benchmarking Health Level Seven Fast Healthcare Interoperability Resources (FHIR) analytics, this study found that while REST APIs suffice for simple queries, big data frameworks like Trino and Pathling offer superior performance for complex data analysis.
Area of Science:
- Health Informatics
- Data Science
- Healthcare Research
Background:
- Electronic health records (EHRs) contain valuable data but face interoperability challenges.
- Health Level Seven Fast Healthcare Interoperability Resources (FHIR) standard aids EHR analysis, yet its complex structure complicates analytical access.
- Existing tools for FHIR data access include REST APIs and relational encoding, but their performance is underexplored.
Purpose of the Study:
- To benchmark the performance of different FHIR analytical approaches.
- To compare REST API queries against SQL and Spark-based big data frameworks for FHIR data analysis.
Main Methods:
- Benchmarked FHIR-PYrate (REST API), Pathling (Apache Spark), and Trino (SQL query engine).
- Executed data aggregation, counting, and extraction queries on Synthea-generated FHIR datasets of varying sizes.
- Measured execution times for each query and engine.
Main Results:
- Trino and Pathling significantly outperformed FHIR-PYrate on large datasets for aggregation and extraction queries.
- Trino was over 12,000 times faster than FHIR-PYrate for aggregate queries on the largest dataset.
- Pathling and Trino showed substantial performance advantages over REST API for complex analytical tasks.
Conclusions:
- REST-based FHIR search APIs are suitable for basic queries but lack performance for complex analytics.
- Big data frameworks like Trino and Pathling offer significant performance benefits for large-scale FHIR data analysis.
- Choosing the right tool is crucial for efficient healthcare data research.
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