NAACL 2022long22 citations

User-Driven Research of Medical Note Generation Software

Tom Knoll, Francesco Moramarco, Alex Papadopoulos Korfiatis, Rachel Young, Claudia Ruffini, Mark Perera, Christian Perstl, Ehud Reiter

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.

BibTeX
@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"
}
User-Driven Research of Medical Note Generation Software · NAACL 2022