Medical Triage as Pairwise Ranking: A Benchmark for Urgency in Patient Portal Messages

• URL: http://arxiv.org/abs/2601.13178v1
• Date: Mon, 19 Jan 2026 16:05:31 GMT
• Title: Medical Triage as Pairwise Ranking: A Benchmark for Urgency in Patient Portal Messages
• Authors: Joseph Gatto, Parker Seegmiller, Timothy Burdick, Philip Resnik, Roshnik Rahat, Sarah DeLozier, Sarah M. Preum,
• Abstract summary: This paper introduces the first largescale public dataset for studying medical triage in the context of outpatient portal messages.<n>Our novel task formulation views patient message triage as a pairwise inference problem, where we train LLMs to choose "which message is more medically urgent" in a head-to-head tournament-style re-sort of a physician's inbox.<n>Our benchmark PMR-Bench contains 1569 unique messages and 2,000+ high-quality test pairs for pairwise medical urgency assessment.
• Score: 7.172145560602484
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Medical triage is the task of allocating medical resources and prioritizing patients based on medical need. This paper introduces the first large-scale public dataset for studying medical triage in the context of asynchronous outpatient portal messages. Our novel task formulation views patient message triage as a pairwise inference problem, where we train LLMs to choose `"which message is more medically urgent" in a head-to-head tournament-style re-sort of a physician's inbox. Our novel benchmark PMR-Bench contains 1569 unique messages and 2,000+ high-quality test pairs for pairwise medical urgency assessment alongside a scalable training data generation pipeline. PMR-Bench includes samples that contain both unstructured patient-written messages alongside real electronic health record (EHR) data, emulating a real-world medical triage scenario. We develop a novel automated data annotation strategy to provide LLMs with in-domain guidance on this task. The resulting data is used to train two model classes, UrgentReward and UrgentSFT, leveraging Bradley-Terry and next token prediction objective, respectively to perform pairwise urgency classification. We find that UrgentSFT achieves top performance on PMR-Bench, with UrgentReward showing distinct advantages in low-resource settings. For example, UrgentSFT-8B and UrgentReward-8B provide a 15- and 16-point boost, respectively, on inbox sorting metrics over off-the-shelf 8B models. Paper resources can be found at https://tinyurl.com/Patient-Message-Triage

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