@inproceedings{knoll-etal-2022-user,
title = "User-Driven Research of Medical Note Generation Software",
author = "Knoll, Tom and
Moramarco, Francesco and
Papadopoulos Korfiatis, Alex and
Young, Rachel and
Ruffini, Claudia and
Perera, Mark and
Perstl, Christian and
Reiter, Ehud and
Belz, Anya and
Savkov, Aleksandar",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.29",
doi = "10.18653/v1/2022.naacl-main.29",
pages = "385--394",
abstract = "A growing body of work uses Natural Language Processing (NLP) methods to automatically generate medical notes from audio recordings of doctor-patient consultations. However, there are very few studies on how such systems could be used in clinical practice, how clinicians would adjust to using them, or how system design should be influenced by such considerations. In this paper, we present three rounds of user studies, carried out in the context of developing a medical note generation system. We present, analyse and discuss the participating clinicians{'} impressions and views of how the system ought to be adapted to be of value to them. Next, we describe a three-week test run of the system in a live telehealth clinical practice. Major findings include (i) the emergence of five different note-taking behaviours; (ii) the importance of the system generating notes in real time during the consultation; and (iii) the identification of a number of clinical use cases that could prove challenging for automatic note generation systems.",
}
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<abstract>A growing body of work uses Natural Language Processing (NLP) methods to automatically generate medical notes from audio recordings of doctor-patient consultations. However, there are very few studies on how such systems could be used in clinical practice, how clinicians would adjust to using them, or how system design should be influenced by such considerations. In this paper, we present three rounds of user studies, carried out in the context of developing a medical note generation system. We present, analyse and discuss the participating clinicians’ impressions and views of how the system ought to be adapted to be of value to them. Next, we describe a three-week test run of the system in a live telehealth clinical practice. Major findings include (i) the emergence of five different note-taking behaviours; (ii) the importance of the system generating notes in real time during the consultation; and (iii) the identification of a number of clinical use cases that could prove challenging for automatic note generation systems.</abstract>
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%0 Conference Proceedings
%T User-Driven Research of Medical Note Generation Software
%A Knoll, Tom
%A Moramarco, Francesco
%A Papadopoulos Korfiatis, Alex
%A Young, Rachel
%A Ruffini, Claudia
%A Perera, Mark
%A Perstl, Christian
%A Reiter, Ehud
%A Belz, Anya
%A Savkov, Aleksandar
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F knoll-etal-2022-user
%X A growing body of work uses Natural Language Processing (NLP) methods to automatically generate medical notes from audio recordings of doctor-patient consultations. However, there are very few studies on how such systems could be used in clinical practice, how clinicians would adjust to using them, or how system design should be influenced by such considerations. In this paper, we present three rounds of user studies, carried out in the context of developing a medical note generation system. We present, analyse and discuss the participating clinicians’ impressions and views of how the system ought to be adapted to be of value to them. Next, we describe a three-week test run of the system in a live telehealth clinical practice. Major findings include (i) the emergence of five different note-taking behaviours; (ii) the importance of the system generating notes in real time during the consultation; and (iii) the identification of a number of clinical use cases that could prove challenging for automatic note generation systems.
%R 10.18653/v1/2022.naacl-main.29
%U https://aclanthology.org/2022.naacl-main.29
%U https://doi.org/10.18653/v1/2022.naacl-main.29
%P 385-394
Markdown (Informal)
[User-Driven Research of Medical Note Generation Software](https://aclanthology.org/2022.naacl-main.29) (Knoll et al., NAACL 2022)
ACL
- Tom Knoll, Francesco Moramarco, Alex Papadopoulos Korfiatis, Rachel Young, Claudia Ruffini, Mark Perera, Christian Perstl, Ehud Reiter, Anya Belz, and Aleksandar Savkov. 2022. User-Driven Research of Medical Note Generation Software. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 385–394, Seattle, United States. Association for Computational Linguistics.