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Construction and effectiveness evaluation of a knowledge-based infectious disease monitoring and decision support system

To improve the hospital's ability to proactively detect infectious diseases, a knowledge-based infectious disease monitoring and decision support system was established based on real medical records and knowledge rules. The effectiveness of the system was evaluated using interrupted time series...

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Autores principales: Wang, Mengying, Jia, Mo, Wei, Zhenhao, Wang, Wei, Shang, Yafei, Ji, Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10425425/
https://www.ncbi.nlm.nih.gov/pubmed/37580359
http://dx.doi.org/10.1038/s41598-023-39931-8
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author Wang, Mengying
Jia, Mo
Wei, Zhenhao
Wang, Wei
Shang, Yafei
Ji, Hong
author_facet Wang, Mengying
Jia, Mo
Wei, Zhenhao
Wang, Wei
Shang, Yafei
Ji, Hong
author_sort Wang, Mengying
collection PubMed
description To improve the hospital's ability to proactively detect infectious diseases, a knowledge-based infectious disease monitoring and decision support system was established based on real medical records and knowledge rules. The effectiveness of the system was evaluated using interrupted time series analysis. In the system, a monitoring and alert rule library for infectious diseases was generated by combining infectious disease diagnosis guidelines with literature and a real medical record knowledge map. The system was integrated with the electronic medical record system, and doctors were provided with various types of real-time warning prompts when writing medical records. The effectiveness of the system's alerts was analyzed from the perspectives of false positive rates, rule accuracy, alert effectiveness, and missed case rates using interrupted time series analysis. Over a period of 12 months, the system analyzed 4,497,091 medical records, triggering a total of 12,027 monitoring alerts. Of these, 98.43% were clinically effective, while 1.56% were invalid alerts, mainly owing to the relatively rough rules generated by the guidelines leading to several false alarms. In addition, the effectiveness of the system's alerts, distribution of diagnosis times, and reporting efficiency of doctors were analyzed. 89.26% of infectious disease cases could be confirmed and reported by doctors within 5 min of receiving the alert, and 77.6% of doctors could complete the filling of 33 items of information within 2 min, which is a reduction in time compared to the past. The timely reminders from the system reduced the rate of missed cases by doctors; the analysis using interrupted time series method showed an average reduction of 4.4037% in the missed-case rate. This study proposed a knowledge-based infectious disease decision support system based on real medical records and knowledge rules, and its effectiveness was verified. The system improved the management of infectious diseases, increased the reliability of decision-making, and reduced the rate of underreporting.
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spelling pubmed-104254252023-08-16 Construction and effectiveness evaluation of a knowledge-based infectious disease monitoring and decision support system Wang, Mengying Jia, Mo Wei, Zhenhao Wang, Wei Shang, Yafei Ji, Hong Sci Rep Article To improve the hospital's ability to proactively detect infectious diseases, a knowledge-based infectious disease monitoring and decision support system was established based on real medical records and knowledge rules. The effectiveness of the system was evaluated using interrupted time series analysis. In the system, a monitoring and alert rule library for infectious diseases was generated by combining infectious disease diagnosis guidelines with literature and a real medical record knowledge map. The system was integrated with the electronic medical record system, and doctors were provided with various types of real-time warning prompts when writing medical records. The effectiveness of the system's alerts was analyzed from the perspectives of false positive rates, rule accuracy, alert effectiveness, and missed case rates using interrupted time series analysis. Over a period of 12 months, the system analyzed 4,497,091 medical records, triggering a total of 12,027 monitoring alerts. Of these, 98.43% were clinically effective, while 1.56% were invalid alerts, mainly owing to the relatively rough rules generated by the guidelines leading to several false alarms. In addition, the effectiveness of the system's alerts, distribution of diagnosis times, and reporting efficiency of doctors were analyzed. 89.26% of infectious disease cases could be confirmed and reported by doctors within 5 min of receiving the alert, and 77.6% of doctors could complete the filling of 33 items of information within 2 min, which is a reduction in time compared to the past. The timely reminders from the system reduced the rate of missed cases by doctors; the analysis using interrupted time series method showed an average reduction of 4.4037% in the missed-case rate. This study proposed a knowledge-based infectious disease decision support system based on real medical records and knowledge rules, and its effectiveness was verified. The system improved the management of infectious diseases, increased the reliability of decision-making, and reduced the rate of underreporting. Nature Publishing Group UK 2023-08-14 /pmc/articles/PMC10425425/ /pubmed/37580359 http://dx.doi.org/10.1038/s41598-023-39931-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Wang, Mengying
Jia, Mo
Wei, Zhenhao
Wang, Wei
Shang, Yafei
Ji, Hong
Construction and effectiveness evaluation of a knowledge-based infectious disease monitoring and decision support system
title Construction and effectiveness evaluation of a knowledge-based infectious disease monitoring and decision support system
title_full Construction and effectiveness evaluation of a knowledge-based infectious disease monitoring and decision support system
title_fullStr Construction and effectiveness evaluation of a knowledge-based infectious disease monitoring and decision support system
title_full_unstemmed Construction and effectiveness evaluation of a knowledge-based infectious disease monitoring and decision support system
title_short Construction and effectiveness evaluation of a knowledge-based infectious disease monitoring and decision support system
title_sort construction and effectiveness evaluation of a knowledge-based infectious disease monitoring and decision support system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10425425/
https://www.ncbi.nlm.nih.gov/pubmed/37580359
http://dx.doi.org/10.1038/s41598-023-39931-8
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