The impact of implementing speech recognition technology on the accuracy and efficiency (time to complete) clinical documentation by nurses: A systematic review

Joseph, Joseph; Moore, Zena; Patton, Declan; O’Connor, Tom; Nugent, Linda

doi:10.1111/jocn.15261

Cited by 24 publications

(16 citation statements)

References 35 publications

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“…Speech recognition technology can dramatically reduce the time required to write medical records for doctors and nurses by automatically inputting data in the medical records. It has been reported that this technology has reduced the burden and fatigue experienced by doctors and nurses and increased the time spent caring for patients [ 15 ]. In addition, conversational AI can be used to automate patient data collection as the SDS used in this study can collect important medical history and patient-reported outcomes.…”

Section: Discusssionmentioning

confidence: 99%

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Conversational Artificial Intelligence for Spinal Pain Questionnaire: Validation and User Satisfaction

Nam

Kim

et al. 2022

Neurospine

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Objective: The purpose of our study is to develop a spoken dialogue system (SDS) for pain questionnaire in patients with spinal disease. We evaluate user satisfaction and validated the performance accuracy of the SDS in medical staff and patients.Methods: The SDS was developed to investigate pain and related psychological issues in patients with spinal diseases based on the pain questionnaire protocol. We recognized patients’ various answers, summarized important information, and documented them. User satisfaction and performance accuracy were evaluated in 30 potential users of SDS, including doctors, nurses, and patients and statistically analyzed.Results: The overall satisfaction score of 30 patients was 5.5 ± 1.4 out of 7 points. Satisfaction scores were 5.3 ± 0.8 for doctors, 6.0 ± 0.6 for nurses, and 5.3 ± 0.5 for patients. In terms of performance accuracy, the number of repetitions of the same question was 13, 16, and 33 (13.5%, 16.8%, and 34.7%) for doctors, nurses, and patients, respectively. The number of errors in the summarized comment by the SDS was 5, 0, and 11 (5.2%, 0.0%, and 11.6 %), respectively. The number of summarization omissions was 7, 5, and 7 (7.3%, 5.3%, and 7.4%), respectively.Conclusion: This is the first study in which voice-based conversational artificial intelligence (AI) was developed for a spinal pain questionnaire and validated by medical staff and patients. The conversational AI showed favorable results in terms of user satisfaction and performance accuracy. Conversational AI can be useful for the diagnosis and remote monitoring of various patients as well as for pain questionnaires in the future.

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Section: Discusssionmentioning

confidence: 99%

“…In addition, conversational AI can be used to automate patient data collection as the SDS used in this study can collect important medical history and patient-reported outcomes. It can also be used for remote home monitoring [ 9 , 10 , 15 ].…”

Section: Discusssionmentioning

confidence: 99%

Conversational Artificial Intelligence for Spinal Pain Questionnaire: Validation and User Satisfaction

Nam

Kim

et al. 2022

Neurospine

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“…We are often called "open spoken English." With the development of speech recognition technology, the first question can be well evaluated by comparing and analyzing the examinee's pronunciation with the standard pronunciation [4], such as using the classical Goodness of Pronunciation (GOP) algorithm. In addition, it is necessary to comprehensively evaluate candidates' answers from multiple dimensions, for example, fluency, rhythm, intonation, richness of vocabulary, and meaning.…”

Section: Introductionmentioning

confidence: 99%

Automatic Scoring of Spoken Language Based on Basic Deep Learning

Zhong

Zong-hua

2022

Scientific Programming

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The oral English test in domestic universities requires teachers to modify a large number of candidates’ oral recordings. This is the work of using time repeatedly. Using the CALL system to realize the automation of conversation recording can reduce the burden of teachers’ work. Therefore, it is of great practical significance to develop an automatic and accurate scoring system for oral English. With the development of artificial intelligence, deep learning technology has been gradually applied in various fields. Similarly, in the application of oral scoring, deep learning technology makes the implementation of such a system possible. Based on the deep learning technology, this paper proposes an automatic scoring algorithm for spoken language and implements a detailed design and evaluation system. The system consists of two modules. The pronunciation standard of spoken pronunciation and the content of spoken pronunciation are scored, and the sum of these two scores is the final score. Finally, this paper uses 650 oral English recordings from a college English test to train the artificial neural network. Experimental results show that if the training data set is small, the BP network model can obtain better comprehensive evaluation performance.

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“…New technologies can address specific existing problems: nurses spend at least thirty percent of their working day filling out forms and documenting patient data. Digitized documentation processes reduce paperwork, create optimized schedules, and increase productivity, allowing nurses more time for patient care [ 7 , 8 , 9 , 10 ]. Electronic medical records encourage health professionals’ exchange and decision-making [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Digitization in Everyday Nursing Care: A Vignette Study in German Hospitals

Korte

Bohnet-Joschko

2022

IJERPH

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(1) Background: Digitization in hospital nursing promises to transform the organization of care processes and, therefore, provide relief to nurse staffing shortages. While technological solutions are advanced and application fields numerous, comprehensive implementation remains challenging. Nursing leadership is crucial to digital change processes. This vignette study examined the effects of the motives and values on nurses’ motivation to use innovative technologies. (2) Methods: We asked hospital nurses in an online vignette study to assess a fictitious situation about the introduction of digital technology. We varied the devices on the degree of novelty (tablet/smart glasses), addressed motives (intrinsic/extrinsic), and values (efficiency/patient orientation). (3) Results: The analysis included 299 responses. The tablet vignettes caused more motivation than those of the smart glasses (Z = −6.653, p < 0.001). The dataset did not show significant differences between intrinsic and extrinsic motives. The nursing leader was more motivating when emphasizing efficiency rather than patient orientation (Z = −2.995, p = 0.003). (4) Conclusions: The results suggest efficiency as a motive for using known digital technologies. The nursing staff’s willingness to use digital technology is generally high. Management actions can provide a structural framework and training so that nursing leaders can ensure their staff’s engagement in using also unknown devices.

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The impact of implementing speech recognition technology on the accuracy and efficiency (time to complete) clinical documentation by nurses: A systematic review

Cited by 24 publications

References 35 publications

Conversational Artificial Intelligence for Spinal Pain Questionnaire: Validation and User Satisfaction

Conversational Artificial Intelligence for Spinal Pain Questionnaire: Validation and User Satisfaction

Automatic Scoring of Spoken Language Based on Basic Deep Learning

Digitization in Everyday Nursing Care: A Vignette Study in German Hospitals

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