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

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.

Expert Radiology Application Softwares Enhance Radiology Diagnosis Contributions And Challenges By A Historical Review of Informetric Analysis From 1991 To 2021

In all areas of medicine, especially in radiology, computers are increasing year by year. Filmless radiology, speech recognition software, electronic application forms, and teleradiology are recent developments that have greatly improved radiologists' performance. This research explores radiology software trends, predictions, and the challenges posed by informatics and historical trend analysis. The rationale behind this research is that information technology (IT) is overgrowing almost every day. We must continuously seek new ways to apply IT to make more use of resources. Consequently, IT becomes increasingly crucial to radiology organizations' innovative thinking, workflow, and business models. This study aimed to analyze all radiology software publications in the Science Citation Index (SCI). From 1991 to July 2021, SCI was used to search for publications systematically. We have also widely used this historical method in radiology software research. The findings and discussions are base on an assessment of trends, predictions, contributions, and challenges in radiology software, and we are exploring radiology software with six evolutionary stages. The gift of this research is that radiology managers realize that the use of new information technologies is closely related to survival in a competitive environment. Radiology companies can review these new technologies to develop more innovative business models and services to improve operational deficiencies.

De tolk als respeaker: een kwestie van training

As a result of political decisions, all Dutch-spoken television programmes that are broadcast by the Flemish Broadcasting Company (VRT) should be provided with subtitles for the deaf and hard of hearing by the year 2010. In order to meet these high expectations, the VRT is constantly improving and changing its subtitling techniques, as are other broadcasting companies worldwide. One of the main areas of change concerns the technique of live subtitling, i. e. real time subtitling of live television programmes. This type of subtitling has definitely benefited from the use of modern speech recognition software. Live subtitling, therefore, requires not only technical skills but also excellent ‘respeaking’ skills that are reminiscent of the skills of conference interpreters. The central question in the first part of this pa- per is to what extent ‘re-speaking’ is related to simultaneous (and other forms of) interpreting: is a good interpreter automatically a good re- speaker? In the second part, I adopt a didactic point of view in order to investigate the teaching aspects of real time subtitling skills: the conclusion is that it is best included in an education programme for conference interpreters

Speech Recognition Apps for the Hearing Impaired and Deaf

Speech recognition software has become increasingly sophisticated and accurate due to progress in information technology. The software converts speech into text using artificial intelligence. The intended purpose of most developed apps is taking voice commands and taking notes. Nevertheless, some apps are specially developed for the hearing impaired and deaf. This project aims to examine the performance of speech recognition apps and to explore which audiological tests are a representative measure of the ability of these apps to convert speech into text.

Acceptability of collecting speech samples from the elderly via the telephone

Objective There is a critical need to develop rapid, inexpensive and easily accessible screening tools for mild cognitive impairment (MCI) and Alzheimer’s disease (AD). We report on the efficacy of collecting speech via the telephone to subsequently develop sensitive metrics that may be used as potential biomarkers by leveraging natural language processing methods. Methods Ninety-one older individuals who were cognitively unimpaired or diagnosed with MCI or AD participated from home in an audio-recorded telephone interview, which included a standard cognitive screening tool, and the collection of speech samples. In this paper we address six questions of interest: (1) Will elderly people agree to participate in a recorded telephone interview? (2) Will they complete it? (3) Will they judge it an acceptable approach? (4) Will the speech that is collected over the telephone be of a good quality? (5) Will the speech be intelligible to human raters? (6) Will transcriptions produced by automated speech recognition accurately reflect the speech produced? Results Participants readily agreed to participate in the telephone interview, completed it in its entirety, and rated the approach as acceptable. Good quality speech was produced for further analyses to be applied, and almost all recorded words were intelligible for human transcription. Not surprisingly, human transcription outperformed off the shelf automated speech recognition software, but further investigation into automated speech recognition shows promise for its usability in future work. Conclusion Our findings demonstrate that collecting speech samples from elderly individuals via the telephone is well tolerated, practical, and inexpensive, and produces good quality data for uses such as natural language processing.

Embracing the Complexity

This paper presents the key findings of the pilot phase of SMART (Shaping Multilingual Access through Respeaking Technology), a multidisciplinary international project focusing on interlingual respeaking (IRSP) for real-time speech-to-text. SMART addresses key questions around IRSP feasibility, quality and competences. The pilot project is based on experiments involving 25 postgraduate students who performed two IRSP tasks (English–Italian) after a crash course. The analysis triangulates subtitle accuracy rates with participants’ subjective ratings and retrospective self-analysis. The best performers were those with a composite skillset, including interpreting/subtitling and interpreting/subtitling/respeaking. Participants indicated multitasking, time-lag, and monitoring of the speech recognition software output as the main difficulties; together with the great variability in performance, personal traits emerged as likely to affect performance. This pilot lays the conceptual and methodological foundations for a larger project involving professionals, to address a set of urgent questions for the industry.

Effects of Automatic Speech Recognition Software on Pronunciation for Adults With Different Learning Styles

This study investigated how learning styles (visual/verbal) and the use of Automatic Speech Recognition (ASR) software affect English as a Second Language adult learners’ improvement during a 12-week course focusing on pronunciation. In the control group (n = 28), the teacher corrected and gave feedback on the adult learners’ pronunciation; experimental group 1 (n = 33) used dictation ASR along with peers’ correction; and experimental group 2 (n = 31) used dictation ASR alone. Their pre- and post-tests on pronunciation in reading tasks and live conversation were analyzed with their learning styles taken into account, using 2-way ANCOVA. The results suggest that learning styles made a significant difference in the pronunciation performance of the reading task in all groups. Visual style learners outperformed verbal style learners in the reading task. The combination of ASR and peer correction yielded the highest improvement in both reading tasks and live conversation.

Probabilistic Machine Learning Using Bayesian Inference

Machine Learning is a branch of AI (Artificial Intelligence) which expands on the idea of a computational system extending its knowledge about set methodical behaviors from the data that is fed to it to essentially develop analytical skills that can help in identifying patterns and making decisions with little to no participation of a real human being. Computer algorithms help in gaining experience to improve the facility over time for use by both consumers and corporations. In today’s technologically advanced world, Machine Learning has given us self-driving cars, speech recognition software, and AI agents like Siri and Google assistant. This project evaluates how the Beta function came to be and how Stirling’s formula is implemented in calculating the magnitude of this function for large input values. The Beta function can then be used to produce a Beta distribution of probabilities to find whether people will actually watch a video they come across on their recommendations feed or search feed and then using Bayesian inference update the prior set predictions.