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arXiv · 2605.14543

RxEval: A Prescription-Level Benchmark for Evaluating LLM Medication Recommendation

Shuhao Chen·Weisen Jiang·Changmiao Wang·Xiaoqing Wu·Xuanren Shi·Yu Zhang·James T. Kwok
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Abstract

Inpatient medication recommendation requires clinicians to repeatedly select specific medications, doses, and routes as a patient's condition evolves. Existing benchmarks formulate this task as admission-level prediction over coarse drug codes with multi-hot diagnostic and procedure code inputs, failing to capture the per-timepoint, information-rich nature of real prescribing. We propose RxEval, a prescription-level benchmark that evaluates LLM prescribing capability by multiple-choice questions: each question presents a detailed patient profile and time-ordered clinical trajectory, requiring selection of specific medication-dose-route triples from real prescriptions and patient-specific distractors generated via reasoning-chain perturbation. RxEval comprises 1,547 questions spanning 584 patients, 18 diagnostic categories, and 969 unique medications. Evaluation of 16 LLMs shows that RxEval is both challenging and discriminative: F1 ranges from 45.18 to 77.10 across models, and the best Exact Match is only 46.10%. Error analysis reveals that even frontier models may overlook stated patient information and fail to derive clinical conclusions.

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Shuhao Chen, Weisen Jiang, Changmiao Wang, Xiaoqing Wu, Xuanren Shi, Yu Zhang, James T. Kwok. 2026-05-14. RxEval: A Prescription-Level Benchmark for Evaluating LLM Medication Recommendation. https://arxiv.org/abs/2605.14543

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