Medical Triage as Pairwise Ranking: A Benchmark for Urgency in Patient Portal Messages
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866909995266736128 |
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| author | Gatto, Joseph Seegmiller, Parker Burdick, Timothy Resnik, Philip Rahat, Roshnik DeLozier, Sarah Preum, Sarah M. |
| author_facet | Gatto, Joseph Seegmiller, Parker Burdick, Timothy Resnik, Philip Rahat, Roshnik DeLozier, Sarah Preum, Sarah M. |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_13178 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Medical Triage as Pairwise Ranking: A Benchmark for Urgency in Patient Portal Messages Gatto, Joseph Seegmiller, Parker Burdick, Timothy Resnik, Philip Rahat, Roshnik DeLozier, Sarah Preum, Sarah M. Computation and Language 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 |
| title | Medical Triage as Pairwise Ranking: A Benchmark for Urgency in Patient Portal Messages |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2601.13178 |