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, Anja Belz, Aleksandar Savkov

Research output: Working paper

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.
Original languageEnglish
PublisherArXiv
Number of pages13
DOIs
Publication statusPublished - 5 May 2022

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