@inproceedings{aryoyudanta-etal-2026-night,
    title = "Night Shift Nerds at {MEDIQA}-{SYNUR} 2026: Pushing Small Large Language Model Capability for Clinical Observation Extraction and Normalization from Nurse Dictation using {RLVR}",
    author = "Aryoyudanta, Bayu  and
      Yuliana, Maria  and
      Rachman, Mikie  and
      Setiawan, I Made Agus",
    editor = "Ben Abacha, Asma  and
      Bethard, Steven  and
      Bitterman, Danielle  and
      Naumann, Tristan  and
      Roberts, Kirk",
    booktitle = "Proceedings of the 8th Workshop on Clinical Natural Language Processing (Clinical {NLP}) @ {LREC} 2026",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/ingest-nlpsi/2026.clinicalnlp-1.18/",
    doi = "10.63317/328obtnwpmsw",
    pages = "163--173",
    abstract = "We presented a small decoder-only language model for clinical observation extraction and normalization from nurse dictation developed using Reinforcement Learning with Verifiable Rewards (RLVR). We fine-tune Qwen3-1.7B model using a two-stage pipeline: (1) supervised fine-tuning (SFT) with an augmented chain-of-thought (CoT) dataset generated by a teacher model to mitigate RL cold-start, followed by (2) GRPO-based RLVR with multi-component reward functions that verify output format, concept presence, value type, and value correctness using the shared-task ontology (193 concepts) as a verifier. On the development set, SFT+GRPO substantially outperforms GRPO-only (F1 0.803 vs. 0.620). After the test holdout was released, our final system achieved 0.700 precision, 0.785 recall, and 0.740 F1. Error analysis shows remaining challenges in concept over-detection and missed concepts, as well as boundary errors in categorical and multi-select value type extraction. Our results demonstrates that small language models can enable accurate, cost-effective, and privacy preserving automated clinical documentation for nurse dictation, supporting scalable deployment in low-resource healthcare settings to reduce nurses' documentation burden."
}