@inproceedings{xu-etal-2024-overview,
title = "Overview of the First Shared Task on Clinical Text Generation: {RRG}24 and {``}Discharge Me!{''}",
author = "Xu, Justin and
Chen, Zhihong and
Johnston, Andrew and
Blankemeier, Louis and
Varma, Maya and
Hom, Jason and
Collins, William J. and
Modi, Ankit and
Lloyd, Robert and
Hopkins, Benjamin and
Langlotz, Curtis and
Delbrouck, Jean-Benoit",
editor = "Demner-Fushman, Dina and
Ananiadou, Sophia and
Miwa, Makoto and
Roberts, Kirk and
Tsujii, Junichi",
booktitle = "Proceedings of the 23rd Workshop on Biomedical Natural Language Processing",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.bionlp-1.7",
pages = "85--98",
abstract = "Recent developments in natural language generation have tremendous implications for healthcare. For instance, state-of-the-art systems could automate the generation of sections in clinical reports to alleviate physician workload and streamline hospital documentation. To explore these applications, we present a shared task consisting of two subtasks: (1) Radiology Report Generation (RRG24) and (2) Discharge Summary Generation ({``}Discharge Me!{''}). RRG24 involves generating the {`}Findings{'} and {`}Impression{'} sections of radiology reports given chest X-rays. {``}Discharge Me!{''} involves generating the {`}Brief Hospital Course{'} and '{`}Discharge Instructions{'} sections of discharge summaries for patients admitted through the emergency department. {``}Discharge Me!{''} submissions were subsequently reviewed by a team of clinicians. Both tasks emphasize the goal of reducing clinician burnout and repetitive workloads by generating documentation. We received 201 submissions from across 8 teams for RRG24, and 211 submissions from across 16 teams for {``}Discharge Me!{''}.",
}
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<abstract>Recent developments in natural language generation have tremendous implications for healthcare. For instance, state-of-the-art systems could automate the generation of sections in clinical reports to alleviate physician workload and streamline hospital documentation. To explore these applications, we present a shared task consisting of two subtasks: (1) Radiology Report Generation (RRG24) and (2) Discharge Summary Generation (“Discharge Me!”). RRG24 involves generating the ‘Findings’ and ‘Impression’ sections of radiology reports given chest X-rays. “Discharge Me!” involves generating the ‘Brief Hospital Course’ and ’‘Discharge Instructions’ sections of discharge summaries for patients admitted through the emergency department. “Discharge Me!” submissions were subsequently reviewed by a team of clinicians. Both tasks emphasize the goal of reducing clinician burnout and repetitive workloads by generating documentation. We received 201 submissions from across 8 teams for RRG24, and 211 submissions from across 16 teams for “Discharge Me!”.</abstract>
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%0 Conference Proceedings
%T Overview of the First Shared Task on Clinical Text Generation: RRG24 and “Discharge Me!”
%A Xu, Justin
%A Chen, Zhihong
%A Johnston, Andrew
%A Blankemeier, Louis
%A Varma, Maya
%A Hom, Jason
%A Collins, William J.
%A Modi, Ankit
%A Lloyd, Robert
%A Hopkins, Benjamin
%A Langlotz, Curtis
%A Delbrouck, Jean-Benoit
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Miwa, Makoto
%Y Roberts, Kirk
%Y Tsujii, Junichi
%S Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F xu-etal-2024-overview
%X Recent developments in natural language generation have tremendous implications for healthcare. For instance, state-of-the-art systems could automate the generation of sections in clinical reports to alleviate physician workload and streamline hospital documentation. To explore these applications, we present a shared task consisting of two subtasks: (1) Radiology Report Generation (RRG24) and (2) Discharge Summary Generation (“Discharge Me!”). RRG24 involves generating the ‘Findings’ and ‘Impression’ sections of radiology reports given chest X-rays. “Discharge Me!” involves generating the ‘Brief Hospital Course’ and ’‘Discharge Instructions’ sections of discharge summaries for patients admitted through the emergency department. “Discharge Me!” submissions were subsequently reviewed by a team of clinicians. Both tasks emphasize the goal of reducing clinician burnout and repetitive workloads by generating documentation. We received 201 submissions from across 8 teams for RRG24, and 211 submissions from across 16 teams for “Discharge Me!”.
%U https://aclanthology.org/2024.bionlp-1.7
%P 85-98
Markdown (Informal)
[Overview of the First Shared Task on Clinical Text Generation: RRG24 and “Discharge Me!”](https://aclanthology.org/2024.bionlp-1.7) (Xu et al., BioNLP-WS 2024)
ACL
- Justin Xu, Zhihong Chen, Andrew Johnston, Louis Blankemeier, Maya Varma, Jason Hom, William J. Collins, Ankit Modi, Robert Lloyd, Benjamin Hopkins, Curtis Langlotz, and Jean-Benoit Delbrouck. 2024. Overview of the First Shared Task on Clinical Text Generation: RRG24 and “Discharge Me!”. In Proceedings of the 23rd Workshop on Biomedical Natural Language Processing, pages 85–98, Bangkok, Thailand. Association for Computational Linguistics.