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 tools revealed that while REST API queries are suitable for simple tasks, big data frameworks like Trino and Pathling offer superior performance for complex data analysis.
Area of Science:
- Health Informatics
- Data Science
- Computational Health
Background:
- Electronic health records (EHRs) contain valuable clinical data but face interoperability challenges.
- The Health Level Seven Fast Healthcare Interoperability Resources (FHIR) standard facilitates data exchange but presents analytical complexities.
- Existing tools for accessing FHIR data via REST APIs or relational formats have unexplored performance implications.
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) against Pathling (Apache Spark) and Trino (SQL query engine).
- Implemented data aggregation, counting, and extraction queries across varying dataset sizes.
- Measured execution times using Synthea-generated FHIR datasets.
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 showed a 2.6x speedup for extraction, while FHIR-PYrate was faster for some count queries.
Conclusions:
- REST API queries are adequate for simple FHIR data retrieval but lack performance for complex analytics.
- Big data frameworks like Trino and Pathling offer substantial performance advantages for large-scale FHIR data analysis.
- Choosing the right tool depends on the complexity and scale of the analytical task.
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