@inproceedings{michalopoulos-etal-2026-overview,
title = "Overview of the {MEDIQA}-{SYNUR} 2026 Shared Task on Observation Extraction from Nurse Dictations",
author = "Michalopoulos, George and
Corbeil, Jean-Philippe and
Bader, Cari and
Bodenstab, Nathan and
Ben Abacha, Asma",
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://aclanthology.org/2026.clinicalnlp-1.3/",
doi = "10.63317/3s6vwtvsw85q",
pages = "19--26",
abstract = "Hospital nurses spend a significant portion of their shifts performing manual data entry tasks. An automatic solution for extracting medical information from nurse dictations into large spreadsheet ontology (flowsheet) could reduce the documentation burden of nurses and alleviate nurse burnout. We introduce the MEDIQA-SYNUR shared task, the first challenge on extracting and normalizing clinical observations from nurse dictations and mapping them to a large ontology of clinical concepts. 13 teams participated in the challenge and experimented with a broad range of approaches. In this paper, we describe the MEDIQA-SYNUR task, the datasets, and the participant{'}s results and solutions."
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<abstract>Hospital nurses spend a significant portion of their shifts performing manual data entry tasks. An automatic solution for extracting medical information from nurse dictations into large spreadsheet ontology (flowsheet) could reduce the documentation burden of nurses and alleviate nurse burnout. We introduce the MEDIQA-SYNUR shared task, the first challenge on extracting and normalizing clinical observations from nurse dictations and mapping them to a large ontology of clinical concepts. 13 teams participated in the challenge and experimented with a broad range of approaches. In this paper, we describe the MEDIQA-SYNUR task, the datasets, and the participant’s results and solutions.</abstract>
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%0 Conference Proceedings
%T Overview of the MEDIQA-SYNUR 2026 Shared Task on Observation Extraction from Nurse Dictations
%A Michalopoulos, George
%A Corbeil, Jean-Philippe
%A Bader, Cari
%A Bodenstab, Nathan
%A Ben Abacha, Asma
%Y Ben Abacha, Asma
%Y Bethard, Steven
%Y Bitterman, Danielle
%Y Naumann, Tristan
%Y Roberts, Kirk
%S Proceedings of the 8th Workshop on Clinical Natural Language Processing (Clinical NLP) @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F michalopoulos-etal-2026-overview
%X Hospital nurses spend a significant portion of their shifts performing manual data entry tasks. An automatic solution for extracting medical information from nurse dictations into large spreadsheet ontology (flowsheet) could reduce the documentation burden of nurses and alleviate nurse burnout. We introduce the MEDIQA-SYNUR shared task, the first challenge on extracting and normalizing clinical observations from nurse dictations and mapping them to a large ontology of clinical concepts. 13 teams participated in the challenge and experimented with a broad range of approaches. In this paper, we describe the MEDIQA-SYNUR task, the datasets, and the participant’s results and solutions.
%R 10.63317/3s6vwtvsw85q
%U https://aclanthology.org/2026.clinicalnlp-1.3/
%U https://doi.org/10.63317/3s6vwtvsw85q
%P 19-26
Markdown (Informal)
[Overview of the MEDIQA-SYNUR 2026 Shared Task on Observation Extraction from Nurse Dictations](https://aclanthology.org/2026.clinicalnlp-1.3/) (Michalopoulos et al., ClinicalNLP 2026)
ACL