User-Driven Research of Medical Note Generation Software
This addresses the problem of integrating AI-generated medical notes into clinical workflows for clinicians, but it is incremental as it focuses on user feedback rather than technical innovation.
The paper tackled the lack of research on how medical note generation systems are used in clinical practice by conducting user studies and a live test run, finding key insights such as five note-taking behaviors and the importance of real-time note generation.
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.