EHRBench: New Benchmark for LLM Clinical Decision-Making
Researchers have introduced EHRBench, an automated and reliable benchmark grounded in electronic health records (EHRs) for evaluating large language models (LLMs) on clinical decision-making (CDM) tasks. The benchmark addresses the need for scalable, high-quality evaluation of LLMs in real-world clinical workflows, where models must infer diagnoses, select treatments, or predict health outcomes under incomplete evidence. EHRBench uses an automated pipeline to ensure both scale and quality, grounding tasks in real patient data to test substantive biomedical knowledge and clinical inference. The work highlights the growing role of LLMs in healthcare while underscoring the insufficient understanding of their reliability in practical CDM scenarios.
Key facts
- EHRBench is an automated and reliable EHR-grounded benchmark for LLM-based clinical decision-making.
- The benchmark is described in a paper on arXiv (2605.30637).
- Clinical decision-making involves inferring diagnoses, selecting treatments, or anticipating health outcomes.
- LLMs are increasingly used for clinical decisions due to language capabilities and biomedical knowledge.
- The benchmark aims to fill gaps in evaluating LLM reliability on real-world clinical tasks.
- The pipeline is designed to ensure both scale and quality of evaluation.
- Tasks are grounded in real patient EHRs to require substantive biomedical knowledge.
- The work emphasizes the need for understanding LLM reliability in clinical settings.
Entities
Institutions
- arXiv