User-Driven Research of Medical Note Generation Software

• URL: http://arxiv.org/abs/2205.02549v2
• Date: Fri, 6 May 2022 07:48:48 GMT
• Title: User-Driven Research of Medical Note Generation Software
• Authors: Tom Knoll, Francesco Moramarco, Alex Papadopoulos Korfiatis, Rachel Young, Claudia Ruffini, Mark Perera, Christian Perstl, Ehud Reiter, Anya Belz, Aleksandar Savkov
• Abstract summary: We present three rounds of user studies carried out in the context of developing a medical note generation system. We discuss the participating clinicians' impressions and views of how the system ought to be adapted to be of value to them. We describe a three-week test run of the system in a live telehealth clinical practice.
• Score: 49.85146209418244
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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