Speech recognition for medical documentation: an analysis of time, cost efficiency and acceptance in a clinical setting

Abstract

Background/Aims Medical documentation is an important and unavoidable part of a health professional's working day. However, the time required for medical documentation is often viewed negatively, particularly by clinicians with heavy workloads. Digital speech recognition has become more prevalent and is being used to optimise working time. This study evaluated the time and cost savings associated with speech recognition technology, and its potential for improving healthcare processes. Methods Clinicians were directly observed while completing medical documentation. A total of 313 samples were collected, of which 163 used speech recognition and 150 used typing methods. The time taken to complete the medical form, the error rate and error correction time were recorded. A survey was also completed by 31 clinicians to gauge their level of acceptance of speech recognition software for medical documentation. Two-sample t-tests and Mann–Whitney U tests were performed to determine statistical trends and significance. Results On average, medical documentation using speech recognition software took just 5.11 minutes to complete the form, compared to 8.9 minutes typing, representing significant time savings. The error rate was also found to be lower for speech recognition software. However, 55% of clinicians surveyed stated that they would prefer to type their notes rather than use speech recognition software and perceived the error rate of this software to be higher than typing. Conclusions The results showed that there are both temporal and financial advantages of speech recognition technology over text input for medical documentation. However, this technology had low levels of acceptance among staff, which could have implications for the uptake of this method.

Review of artificial intelligence‐based question‐answering systems in healthcare

“…Although, conversational agents are seen as useful (Bibault et al, 2019; Inkster et al, 2018) and easy to use (Cameron et al, 2019). Zuchowski and Göller (2022) reported that health care professionals preferred to type medical notes, rather than dictate using speech recognition software. Speech recognition software reduces the amount of time to complete medical documentation.…”

Linguistic and ontological challenges of multiple domains contributing to transformed health ecosystems

“…A randomized study from 2015 showed an increase in productivity by physicians using this technology ( 64 ). In the meantime, speech recognition software has been shown to be faster and more accurate than typing, but the acceptance by clinicians still leaves a lot to be desired ( 65 ).…”

Design module for speech recognition graphical user interface browser to supports the web speech applications

“…Once pressed, recording will commence immediately and will not stop until the system recognizes that the user has spoken something and then stopped speaking. This is the main input modality for telling the system to take in user input, and the only way for the system to initiate recognition and subsequently synthesize a response as propsed in [28]- [32]. Finally, the show/hide output button can be pressed to show or hide the area below the button which automatically records the conversation being had as visual text data.…”