User-Driven Research of Medical Note Generation Software (2022)

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.

## introduction with the introduction of electronic health records (ehr), clinicians are required to keep a detailed record of each patient interaction @xcite . while this creates a wealth of useful data and may lead to better medical outcomes, @xcite show that the burden of administrative tasks is a major contributor to clinician burnout. to address this, some recent studies @xcite @xcite propose to use speech recognition to transcribe the audio of a medical consultation and then to train sequence-to-sequence models to summarise the transcript into a consultation note (figure 1 ). while intrinsic evaluations foot_0 @xcite @xcite show that these methods may be effective at capturing the salient points of a medical consultation, most studies focus on the technical difficulties of developing the systems with little consideration for the human-computer interaction (hci) and usability challenges involved in putting such a system into clinical practice. this is especially important for "human in the loop" systems, like note generation, that still involve manual checking of any automatically generated content. in this paper we present methodology and findings of three rounds of user research and design: (i) current note-taking discovery to gather user requirements and initial impressions; (ii) initial user interface (ui) testing where clinicians were shown video mock-ups of three potential design options and asked to provide feedback; and (iii) mock consultations, in which the clinicians interacted with a wizard-of-oz @xcite prototype, with another clinician acting as the automated system. following these, we developed a note generation system informed by the user studies and carried out a live test with five clinicians who used the system to aid them in the task of writing a consultation note for each consultation. the experiment lasted three weeks, during which over 300 consultations were carried out with the system. we document the insights gathered through interviews, post-consultation surveys, and analysis of the generated notes, reporting the major requirements for developing a note generation system into a useful product while highlighting the importance of user research and design alongside nlp research. among our main findings are the emergence of five note-taking behaviours (figure 2 ) and the need for the note generation system to produce output in real time during the consultation. we also identify three clinical use cases that could prove challenging to a note generation system: patient dishonesty, multiple presenting complaints, and clinicians' non-verbal observations. ## related work the task of automatically generating consultation notes from clinician-patient interactions is rapidly gaining interest in the research community @xcite @xcite @xcite @xcite . the prevalent methods employ pre-trained, generic summarisation models and fine-tune them on dedicated datasets of consultation transcripts and corresponding notes. however, the two tasks have differences; in particular, note generation has a component of language translation as well as summarisation, whereby the note uses medical terminology and inferred statements that aren't present in the original transcript (figure 1 and some of the note generation studies include an intrinsic human evaluation of the generated consultation notes to measure common natural language generation (nlg) criteria such as accuracy, fluency, and completeness; also, @xcite propose post-editing as a way clinicians may incorporate the generated notes into their workflows. however, there is very limited work on user needs, design decisions, and system requirements for a note-taking tool to be useful in live clinical practice. in this paper, we aim to address these questions for the task of medical note generation. in the broader medical domain, there has been a marked increase in studies on the interaction between human users and different tools with clinical applications since 2013 @xcite , with usability-related studies making up 25% of them. @xcite review 17 papers that identify human-computer interaction (hci) issues in the use of electronic health records in medical practice, and categorise them into four types: poor display of information, cognitive overload, navigation issues and workflow issues. @xcite investigate the ux experience of patients managing their type 2 diabetes with an in-home monitoring device. the authors conduct a contextual inquiry followed by semi-structured interviews with 9 patients and report the patients' experiences and emotions while using the device, perceived benefits and limitations. @xcite present a usability study of wearable gps devices for people with dementia. the study ran for 4 weeks and comprised two alternative devices and 17 participants. it measured clinical effectiveness, revealed a clear preference for one of the two devices, and informed subsequent design decisions (such as software features, buttons, and battery life). in the nlg space, @xcite conduct a clinical trial with 2553 smokers to show whether system-generated tailored cessation letters would be more effective than a generic cessation letter written by medical practitioners. @xcite carry out a clinical off-ward experiment in a neonatal intensive care unit where medical practitioners compare the effect on decision-making that a data-to-text system has versus human expert textual descriptions of neonatal signals. @xcite describe an animated, empathic virtual nurse interface for counseling hospital patients with low health literacy. their results indicate patients found the system easy to use, were satisfied with the system, and most said they preferred receiving the discharge information from the agent over their doctor or nurse. ## ux exploration this work was conducted at babylon, a digital healthcare company that employs clinicians to carry out virtual, video consultations with patients through a platform referred to as the clinical portal. as part of their workflow, babylon clinicians write medical notes for each consultation; the notes are useful for any future healthcare professionals interacting with the patient, but they are also made available to the patient after the consultation and serve as a medico-legal record of the interaction. in order to investigate the value of a note generation system and to design an interface that best supports clinicians, we carried out three experiments with the following aims: (i) learning about the clinicians' current note-taking behaviours (section 3.1), (ii) gathering their initial thoughts about note generation systems using low-fidelity designs (section 3.2), and (iii) collecting hands-on insights from using fully interactive designs (section 3.3). the research participants throughout the study were all uk clinicians with diverse backgrounds and at least one year's experience working at babylon, who regularly used the clinical portal to carry out consultations with patients. as part of our ethical consideration, we obtained consent from the participants to use their feedback for research and development (excluding marketing purposes). their time was paid at £70 per hour foot_2 and they were aware they could withdraw at any point. ## note generation system the ux exploration detailed so far gave us, among other insights, indications that: (i) a note generation tool could be of value to clinicians; (ii), it would need to generate notes in real time, in order for clinicians to evaluate its output as the consultation is progressing; and (iii), clinicians would still want to be able to take manual notes during the consultation. prior to this ux exploration, we had developed a note generation system based on a publicly available bart sequence-to-sequence model @xcite pre-trained on the cnn summarisation dataset @xcite , which we further fine-tuned on a proprietary babylon dataset of 10,000 transcripts and consultation notes. in @xcite we detail a non-production evaluation of the system, and in papadopoulos korfiatis et al. (2022) we release the mock transcript and note dataset used to evaluate it. examples of model inputs and outputs from this mock dataset can be found in appendix a.2. the following modifications were made as a result of the three studies in the ux exploration: ## live test in order to test the usability of the updated note generation system in clinical practice, we incorporated the real-time system into the clinical portal and allowed a limited number of clinicians to use it in a supervised environment for a period of three weeks. our goal from this live test was to answer the following questions: ## discussion and conclusion in this paper we presented a series of user research studies and described how they influenced the development of a natural language processing system for automatically generating consultation notes. some of the main challenges in putting such a system into clinical practice are not due to the system's technology or limitations, but instead to the user experience design choices around it. user research and live testing is essential to ensure the system is actually useful for clinicians. among the list of tools available, we found that a low-fidelity wizard-of-oz prototype was both incredibly valuable to gather hands-on feedback from clinicians and significantly easier to implement than a functional product prototype. more specifically, the need to generate notes in real time is a requirement that was identified through the ux exploration and confirmed in both the wizard-of-oz and live test phases. to our knowledge, this has not been covered in similar studies and we believe it will be a key challenge for future systems. similarly, the user interviews uncovered three clinical use cases that could prove challenging to a note generation system: patient dishonesty, multiple presenting complaints, and clinicians' non-verbal observations. we observed an interest for the tool beyond the live test, even from clinicians who were initially skeptical of automatically generated notes. we also highlight the individual differences in personal note-taking styles, which mean that clinicians use it in different, but still valuable ways. one limitation of our work so far is the relatively small sample of clinicians (no more than 7 in each round) and the short time frame (3 weeks) in which they used the tool in our live test. the results we present from three weeks of live testing might not be representative of long-term usage of the note generation tool. another limitation is that the design, based on data and processes from the uk, may not transfer easily to other regions without substantial changes. in conclusion, we find that user experience studies, currently not commonly carried out in nlp research, revealed essential requirements for the design of our note generation system. we believe that to design an interactive tool it is crucial to test with active users from the start and hope that this work may help further studies in the field of medical note generation.