Machine learning-based speech recognition system for nursing documentation – A pilot study
•A trained machine learning-based speech recognition (SR) system was developed and evaluated for its feasibility for nursing documentation.•The SR system may reduce the burden of documentation for nurses by providing flexibility in entry type, and the opportunity to record at point-of-care may impro...
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Published in | International journal of medical informatics (Shannon, Ireland) Vol. 178; p. 105213 |
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Main Authors | , , , , , , , |
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01.10.2023
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Abstract | •A trained machine learning-based speech recognition (SR) system was developed and evaluated for its feasibility for nursing documentation.•The SR system may reduce the burden of documentation for nurses by providing flexibility in entry type, and the opportunity to record at point-of-care may improve workflow.•Further studies are needed to improve the integration of SR in the digital documentation of nursing records, in terms of both productivity and accuracy.
Considering the significant workload of nursing tasks, enhancing the efficiency of nursing documentation is imperative. This study aimed to evaluate the effectiveness of a machine learning-based speech recognition (SR) system in reducing the clinical workload associated with typing nursing records, implemented in a psychiatry ward.
The study was conducted between July 15, 2020, and June 30, 2021, at Cheng Hsin General Hospital in Taiwan. The language corpus was based on the existing records from the hospital nursing information system. The participating ward's nursing activities, clinical conversation, and accent data were also collected for deep learning-based SR-engine training. A total of 21 nurses participated in the evaluation of the SR system. Documentation time and recognition error rate were evaluated in parallel between SR-generated records and keyboard entry over 4 sessions. Any differences between SR and keyboard transcriptions were regarded as SR errors.
Findings: A total of 200 data were obtained from four evaluation sessions, 10 participants were asked to use SR and keyboard entry in parallel at each session and 5 entries were collected from each participant. Overall, the SR system processed 30,112 words in 32,456 s (0.928 words per second). The mean accuracy of the SR system improved after each session, from 87.06% in 1st session to 95.07% in 4th session.
This pilot study demonstrated our machine learning-based SR system has an acceptable recognition accuracy and may reduce the burden of documentation for nurses. However, the potential error with the SR transcription should continually be recognized and improved. Further studies are needed to improve the integration of SR in digital documentation of nursing records, in terms of both productivity and accuracy across different clinical specialties. |
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AbstractList | •A trained machine learning-based speech recognition (SR) system was developed and evaluated for its feasibility for nursing documentation.•The SR system may reduce the burden of documentation for nurses by providing flexibility in entry type, and the opportunity to record at point-of-care may improve workflow.•Further studies are needed to improve the integration of SR in the digital documentation of nursing records, in terms of both productivity and accuracy.
Considering the significant workload of nursing tasks, enhancing the efficiency of nursing documentation is imperative. This study aimed to evaluate the effectiveness of a machine learning-based speech recognition (SR) system in reducing the clinical workload associated with typing nursing records, implemented in a psychiatry ward.
The study was conducted between July 15, 2020, and June 30, 2021, at Cheng Hsin General Hospital in Taiwan. The language corpus was based on the existing records from the hospital nursing information system. The participating ward's nursing activities, clinical conversation, and accent data were also collected for deep learning-based SR-engine training. A total of 21 nurses participated in the evaluation of the SR system. Documentation time and recognition error rate were evaluated in parallel between SR-generated records and keyboard entry over 4 sessions. Any differences between SR and keyboard transcriptions were regarded as SR errors.
Findings: A total of 200 data were obtained from four evaluation sessions, 10 participants were asked to use SR and keyboard entry in parallel at each session and 5 entries were collected from each participant. Overall, the SR system processed 30,112 words in 32,456 s (0.928 words per second). The mean accuracy of the SR system improved after each session, from 87.06% in 1st session to 95.07% in 4th session.
This pilot study demonstrated our machine learning-based SR system has an acceptable recognition accuracy and may reduce the burden of documentation for nurses. However, the potential error with the SR transcription should continually be recognized and improved. Further studies are needed to improve the integration of SR in digital documentation of nursing records, in terms of both productivity and accuracy across different clinical specialties. PURPOSEConsidering the significant workload of nursing tasks, enhancing the efficiency of nursing documentation is imperative. This study aimed to evaluate the effectiveness of a machine learning-based speech recognition (SR) system in reducing the clinical workload associated with typing nursing records, implemented in a psychiatry ward.METHODSThe study was conducted between July 15, 2020, and June 30, 2021, at Cheng Hsin General Hospital in Taiwan. The language corpus was based on the existing records from the hospital nursing information system. The participating ward's nursing activities, clinical conversation, and accent data were also collected for deep learning-based SR-engine training. A total of 21 nurses participated in the evaluation of the SR system. Documentation time and recognition error rate were evaluated in parallel between SR-generated records and keyboard entry over 4 sessions. Any differences between SR and keyboard transcriptions were regarded as SR errors.FINDINGSA total of 200 data were obtained from four evaluation sessions, 10 participants were asked to use SR and keyboard entry in parallel at each session and 5 entries were collected from each participant. Overall, the SR system processed 30,112 words in 32,456 s (0.928 words per second). The mean accuracy of the SR system improved after each session, from 87.06% in 1st session to 95.07% in 4th session.CONCLUSIONThis pilot study demonstrated our machine learning-based SR system has an acceptable recognition accuracy and may reduce the burden of documentation for nurses. However, the potential error with the SR transcription should continually be recognized and improved. Further studies are needed to improve the integration of SR in digital documentation of nursing records, in terms of both productivity and accuracy across different clinical specialties. Considering the significant workload of nursing tasks, enhancing the efficiency of nursing documentation is imperative. This study aimed to evaluate the effectiveness of a machine learning-based speech recognition (SR) system in reducing the clinical workload associated with typing nursing records, implemented in a psychiatry ward. The study was conducted between July 15, 2020, and June 30, 2021, at Cheng Hsin General Hospital in Taiwan. The language corpus was based on the existing records from the hospital nursing information system. The participating ward's nursing activities, clinical conversation, and accent data were also collected for deep learning-based SR-engine training. A total of 21 nurses participated in the evaluation of the SR system. Documentation time and recognition error rate were evaluated in parallel between SR-generated records and keyboard entry over 4 sessions. Any differences between SR and keyboard transcriptions were regarded as SR errors. A total of 200 data were obtained from four evaluation sessions, 10 participants were asked to use SR and keyboard entry in parallel at each session and 5 entries were collected from each participant. Overall, the SR system processed 30,112 words in 32,456 s (0.928 words per second). The mean accuracy of the SR system improved after each session, from 87.06% in 1st session to 95.07% in 4th session. This pilot study demonstrated our machine learning-based SR system has an acceptable recognition accuracy and may reduce the burden of documentation for nurses. However, the potential error with the SR transcription should continually be recognized and improved. Further studies are needed to improve the integration of SR in digital documentation of nursing records, in terms of both productivity and accuracy across different clinical specialties. |
ArticleNumber | 105213 |
Author | Chung, Min-Huey Hung, Lung-Yun Chou, Kuei-Ru Li, Chin-Ching Lee, Tso-Ying Guo, Shu-Liu Wu, Hao-Ting Hsiao, Shu-Tai |
Author_xml | – sequence: 1 givenname: Tso-Ying surname: Lee fullname: Lee, Tso-Ying email: tsoyinglee@gmail.com organization: Director of Nursing Research Center, Nursing Department, Taipei Medical University Hospital, Taipei, Taiwan – sequence: 2 givenname: Chin-Ching surname: Li fullname: Li, Chin-Ching organization: Assistant Professor, Department of Nursing, Mackay Medical College, New Taipei City, Taiwan – sequence: 3 givenname: Kuei-Ru surname: Chou fullname: Chou, Kuei-Ru organization: Professor, College of Nursing, Taipei Medical University, Taipei, Taiwan – sequence: 4 givenname: Min-Huey surname: Chung fullname: Chung, Min-Huey organization: Professor, College of Nursing, Taipei Medical University, Taipei, Taiwan – sequence: 5 givenname: Shu-Tai surname: Hsiao fullname: Hsiao, Shu-Tai organization: Vice President, Taipei Medical University Hospital, Taipei, Taiwan – sequence: 6 givenname: Shu-Liu surname: Guo fullname: Guo, Shu-Liu organization: Director of Nursing Department, Taipei Medical University Hospital, Taipei, Taiwan – sequence: 7 givenname: Lung-Yun surname: Hung fullname: Hung, Lung-Yun organization: Head Nurse, Nursing Department, Cheng Hsin General Hospital, Taipei, Taiwan – sequence: 8 givenname: Hao-Ting surname: Wu fullname: Wu, Hao-Ting organization: Head Nurse, Nursing Department, Cheng Hsin General Hospital, Taipei, Taiwan |
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Keywords | Speech Recognition Natural language processing Nursing Documentation Artificial Intelligence Dictation |
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Snippet | •A trained machine learning-based speech recognition (SR) system was developed and evaluated for its feasibility for nursing documentation.•The SR system may... Considering the significant workload of nursing tasks, enhancing the efficiency of nursing documentation is imperative. This study aimed to evaluate the... PURPOSEConsidering the significant workload of nursing tasks, enhancing the efficiency of nursing documentation is imperative. This study aimed to evaluate the... |
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SubjectTerms | Artificial Intelligence Dictation Documentation Humans Natural language processing Nursing Documentation Perception Pilot Projects Speech Speech Recognition Speech Recognition Software |
Title | Machine learning-based speech recognition system for nursing documentation – A pilot study |
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