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

2022 ◽  
Vol 28 (1) ◽  
pp. 30-36
Author(s):  
Matthias Zuchowski ◽  
Aydan Göller

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.

2002 ◽  
Vol 11 (4) ◽  
pp. 323-332 ◽  
Author(s):  
H. S. Venkatagiri

Speech recognition (SR) is the process whereby a microprocessor-based system, typically a computer with sound processing hardware and speech recognition software, responds in predictable ways to spoken commands and/or converts speech into text. This tutorial describes the types and the general uses of SR and provides an explanation of the technology behind it. The emerging applications of SR technology for dictation, articulation training, language and literacy development, environmental control, and communication augmentation are discussed.


2014 ◽  
Vol 980 ◽  
pp. 165-171
Author(s):  
Yun Suen Pai ◽  
Hwa Jen Yap ◽  
Ramesh Singh

Speech recognition is a technology that attempts to involve audio cues during interaction with machines, instead of being limited to just visual and touch interfaces. However, a keyboard and mouse input is an archaic method of interaction, adding on to the fact that voice control is seemingly more natural. This study aims to implement speech recognition as a form of machine control to perform simple commands in a virtual simulation process. The simulation system is an in-house developed augmented reality robotic work cell which includes a robot arm, a conveyer belt, a computer numerical control (CNC) machine, and a pellet. Issuing commands are performed via the Windows Speech Recognition software built from the Microsoft Speech Application Programming Interface (SAPI). This software is advantageous because it can be fairly accurate once trained properly, is easily modifiable by anyone regardless of the operator’s programming knowledge, and is free. A macros tool is used to support the additional features of the recognition software which includes directly programmable Extensible Markup Language (XML) codes.


Author(s):  
Shigeki Miyoshi ◽  
Hayato Kuroki ◽  
Sumihiro Kawano ◽  
Mayumi Shirasawa ◽  
Yasushi Ishihara ◽  
...  

2013 ◽  
pp. 1005-1011
Author(s):  
Andrew Kitchenham ◽  
Doug Bowes

In this chapter, the authors discuss the promise of speech or voice recognition software and provide practical suggestions for the teacher or any stakeholder working with a disabled child. The authors begin the chapter with a brief overview of the legislation mandating the accommodation of special needs students in the classroom and discuss the implications of assistive technology. The authors then move on to an examination of the promise of the software. The authors end the chapter with practical ideas for implementation should the caregiver believe that voice recognition software will assist the disabled child in the learning process.


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