An online intelligent electronic medical record system via speech recognition
Traditional electronic medical record systems in hospitals rely on healthcare workers to manually enter patient information, resulting in healthcare workers having to spend a significant amount of time each day filling out electronic medical records. This inefficient interaction seriously affects th...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-11-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/15501329221134479 |
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| _version_ | 1832555251812532224 |
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| author | Xin Xia Yunlong Ma Ye Luo Jianwei Lu |
| author_facet | Xin Xia Yunlong Ma Ye Luo Jianwei Lu |
| author_sort | Xin Xia |
| collection | DOAJ |
| description | Traditional electronic medical record systems in hospitals rely on healthcare workers to manually enter patient information, resulting in healthcare workers having to spend a significant amount of time each day filling out electronic medical records. This inefficient interaction seriously affects the communication between doctors and patients and reduces the speed at which doctors can diagnose patients’ conditions. The rapid development of deep learning–based speech recognition technology promises to improve this situation. In this work, we build an online electronic medical record system based on speech interaction. The system integrates a medical linguistic knowledge base, a specialized language model, a personalized acoustic model, and a fault-tolerance mechanism. Hence, we propose and develop an advanced electronic medical record system approach with multi-accent adaptive technology for avoiding the mistakes caused by accents, and it improves the accuracy of speech recognition obviously. For testing the proposed speech recognition electronic medical record system, we construct medical speech recognition data sets using audio and electronic medical records from real medical environments. On the data sets from real clinical scenarios, our proposed algorithm significantly outperforms other machine learning algorithms. Furthermore, compared to traditional electronic medical record systems that rely on keyboard inputs, our system is much more efficient, and its accuracy rate increases with the increasing online time of the proposed system. Our results show that the proposed electronic medical record system is expected to revolutionize the traditional working approach of clinical departments, and it serves more efficient in clinics with low time consumption compared with traditional electronic medical record systems depending on keyboard inputs, which has less recording mistakes and lows down the time consumption in modification of medical recordings; due to the proposed speech recognition electronic medical record system is built on knowledge database of medical terms, so it has a good generalized application and adaption in the clinical scenarios for hospitals. |
| format | Article |
| id | doaj-art-023295ccb0514714993cfba4e897a65e |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2022-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-023295ccb0514714993cfba4e897a65e2025-02-03T05:48:40ZengWileyInternational Journal of Distributed Sensor Networks1550-14772022-11-011810.1177/15501329221134479An online intelligent electronic medical record system via speech recognitionXin Xia0Yunlong Ma1Ye Luo2Jianwei Lu3East Hospital, School of Medicine, Tongji University, Shanghai, ChinaCollege of Electronic and Information Engineering, Tongji University, Shanghai, ChinaSchool of Software, Tongji University, Shanghai, ChinaEngineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, ChinaTraditional electronic medical record systems in hospitals rely on healthcare workers to manually enter patient information, resulting in healthcare workers having to spend a significant amount of time each day filling out electronic medical records. This inefficient interaction seriously affects the communication between doctors and patients and reduces the speed at which doctors can diagnose patients’ conditions. The rapid development of deep learning–based speech recognition technology promises to improve this situation. In this work, we build an online electronic medical record system based on speech interaction. The system integrates a medical linguistic knowledge base, a specialized language model, a personalized acoustic model, and a fault-tolerance mechanism. Hence, we propose and develop an advanced electronic medical record system approach with multi-accent adaptive technology for avoiding the mistakes caused by accents, and it improves the accuracy of speech recognition obviously. For testing the proposed speech recognition electronic medical record system, we construct medical speech recognition data sets using audio and electronic medical records from real medical environments. On the data sets from real clinical scenarios, our proposed algorithm significantly outperforms other machine learning algorithms. Furthermore, compared to traditional electronic medical record systems that rely on keyboard inputs, our system is much more efficient, and its accuracy rate increases with the increasing online time of the proposed system. Our results show that the proposed electronic medical record system is expected to revolutionize the traditional working approach of clinical departments, and it serves more efficient in clinics with low time consumption compared with traditional electronic medical record systems depending on keyboard inputs, which has less recording mistakes and lows down the time consumption in modification of medical recordings; due to the proposed speech recognition electronic medical record system is built on knowledge database of medical terms, so it has a good generalized application and adaption in the clinical scenarios for hospitals.https://doi.org/10.1177/15501329221134479 |
| spellingShingle | Xin Xia Yunlong Ma Ye Luo Jianwei Lu An online intelligent electronic medical record system via speech recognition International Journal of Distributed Sensor Networks |
| title | An online intelligent electronic medical record system via speech recognition |
| title_full | An online intelligent electronic medical record system via speech recognition |
| title_fullStr | An online intelligent electronic medical record system via speech recognition |
| title_full_unstemmed | An online intelligent electronic medical record system via speech recognition |
| title_short | An online intelligent electronic medical record system via speech recognition |
| title_sort | online intelligent electronic medical record system via speech recognition |
| url | https://doi.org/10.1177/15501329221134479 |
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