@inproceedings{wendelken-etal-2024-roux,
title = "Roux-lette at {``}Discharge Me!{''}: Reducing {EHR} Chart Burden with a Simple, Scalable, Clinician-Driven {AI} Approach",
author = "Wendelken, Suzanne and
Antony, Anson and
Korutla, Rajashekar and
Pachipala, Bhanu and
Mahajan, Dushyant and
Shanahan, James and
Saba, Walid",
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-1.63",
doi = "10.18653/v1/2024.bionlp-1.63",
pages = "719--723",
abstract = "Healthcare providers spend a significant amount of time reading and synthesizing electronic health records (EHRs), negatively impacting patient outcomes and causing provider burnout. Traditional supervised machine learning approaches using large language models (LLMs) to summarize clinical text have struggled due to hallucinations and lack of relevant training data. Here, we present a novel, simplified solution for the {``}Discharge Me!{''} shared task. Our approach mimics human clinical workflow, using pre-trained LLMs to answer specific questions and summarize the answers obtained from discharge summaries and other EHR sections. This method (i) avoids hallucinations through hybrid-RAG/zero-shot contextualized prompting; (ii) requires no extensive training or fine-tuning; and (iii) is adaptable to various clinical tasks.",
}
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%0 Conference Proceedings
%T Roux-lette at “Discharge Me!”: Reducing EHR Chart Burden with a Simple, Scalable, Clinician-Driven AI Approach
%A Wendelken, Suzanne
%A Antony, Anson
%A Korutla, Rajashekar
%A Pachipala, Bhanu
%A Mahajan, Dushyant
%A Shanahan, James
%A Saba, Walid
%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 wendelken-etal-2024-roux
%X Healthcare providers spend a significant amount of time reading and synthesizing electronic health records (EHRs), negatively impacting patient outcomes and causing provider burnout. Traditional supervised machine learning approaches using large language models (LLMs) to summarize clinical text have struggled due to hallucinations and lack of relevant training data. Here, we present a novel, simplified solution for the “Discharge Me!” shared task. Our approach mimics human clinical workflow, using pre-trained LLMs to answer specific questions and summarize the answers obtained from discharge summaries and other EHR sections. This method (i) avoids hallucinations through hybrid-RAG/zero-shot contextualized prompting; (ii) requires no extensive training or fine-tuning; and (iii) is adaptable to various clinical tasks.
%R 10.18653/v1/2024.bionlp-1.63
%U https://aclanthology.org/2024.bionlp-1.63
%U https://doi.org/10.18653/v1/2024.bionlp-1.63
%P 719-723
Markdown (Informal)
[Roux-lette at “Discharge Me!”: Reducing EHR Chart Burden with a Simple, Scalable, Clinician-Driven AI Approach](https://aclanthology.org/2024.bionlp-1.63) (Wendelken et al., BioNLP-WS 2024)
ACL
- Suzanne Wendelken, Anson Antony, Rajashekar Korutla, Bhanu Pachipala, Dushyant Mahajan, James Shanahan, and Walid Saba. 2024. Roux-lette at “Discharge Me!”: Reducing EHR Chart Burden with a Simple, Scalable, Clinician-Driven AI Approach. In Proceedings of the 23rd Workshop on Biomedical Natural Language Processing, pages 719–723, Bangkok, Thailand. Association for Computational Linguistics.