MedCase-Structured: A Text-to-FHIR Dataset for Benchmarking Diagnostic Reasoning in Clinically Realistic EHR Settings
arXiv:2605.30295v2 Announce Type: replace-cross Abstract: Large language models (LLMs) show promise for clinical reasoning and decision support, but evaluation in realistic, electronic health record-congruent settings remains limited. Existing benchmarks often rely on static datasets or unstructured inputs that do not reflect the structured, interoperable data formats used in clinical systems. We introduce a pipeline for generating clinically realistic HL7 FHIR R4 bundles from unstructured text, enabling controllable evaluation of clinical decision support systems. The pipeline combines staged
The rapid advancement of LLMs has reached a point where their application in critical domains like healthcare necessitates more rigorous and realistic evaluation methods.
This development addresses a key limitation in clinical AI evaluation, moving towards benchmarks that accurately reflect real-world EHR data structures, which is crucial for safe and effective deployment.
The introduction of MedCase-Structured provides a standardized text-to-FHIR dataset, enabling more robust and comparable benchmarking of LLMs for diagnostic reasoning within clinical systems.
- · AI developers
- · Healthcare providers
- · EHR system vendors
- · Patients
- · Developers relying on simplistic evaluation
- · Legacy AI solutions
Improved reliability and trust in AI-driven clinical decision support systems will emerge.
Faster adoption and integration of advanced AI into healthcare workflows will become more prevalent, leading to efficiency gains.
This could accelerate the creation of new AI-powered diagnostic and treatment planning tools, potentially transforming medical practice.
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Read at arXiv cs.AI