The Use of Artificial Intelligence Techniques in Nursing Data Systems: Scoping Review
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Redalyc
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| Formato: | Artículo científico |
| Lenguaje: | en |
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Universidad Autónoma de Bucaramanga
2023
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| _version_ | 1876472680087027712 |
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| author | Shina Singla |
| author_facet | Shina Singla |
| contents | The Use of Artificial Intelligence Techniques in Nursing Data Systems: Scoping Review Shina Singla Lyndsay Howitt Christina Medeiros Doris Grinspun Shanoja Naik Medicina Evidence Based Nursing Machine Learning Artificial Intelligence Health Information Systems Introduction. Artificial intelligence and machine learning are technologies that assist in uncovering patterns in data that can inform clinical decision-making. The Registered Nurses’ Association of Ontario has used artificial intelligence techniques to assist in understanding impactful clinical practices and implementation strategies. This scoping review aimed to discover the adaptation and implementation of various artificial intelligence and machine learning techniques in various healthcare settings using different data systems that house nursing-related data. Methodology. In March 2022, a scoping review was conducted to search for peer-reviewed literature using the following terms: “nursing”, “artificial intelligence”, “data systems”, “statistics”, and “aggregated data”. Studies were excluded if they were not relevant to nursing, utilized qualitative or mixed-methods analyses, were literature review articles, and did not focus on artificial intelligence or the use of patient-level data. Results. A total of 2,627 articles were retrieved, with 1,518 articles remaining after de-duplication. Through title and abstract screening, 1,347 articles remained. Following the full-text screening, 13 studies remained. Artificial intelligence techniques used by healthcare data systems include regression, neural networks, classification, and graph-based methods, among others. Discussion. There is a gap in the application of artificial intelligence methods in data systems that evaluate the impact of implementing best practices in nursing. More data systems are needed that employ artificial intelligence techniques to support the evaluation of best practices in nursing and other health professions. Conclusions. Various artificial intelligence techniques in data systems housing nursing-related data were retrieved. However, more data systems and research are needed in this area. 2023 artículo científico 0123-7047 https://www.redalyc.org/articulo.oa?id=71979263016 https://www.redalyc.org/journal/719/71979263016/ https://www.redalyc.org/journal/719/71979263016/html/ https://www.redalyc.org/journal/719/71979263016/71979263016.epub https://www.redalyc.org/journal/719/71979263016/movil https://doi.org/10.29375/01237047.4634 en http://www.redalyc.org/revista.oa?id=719 MedUNAB application/pdf Universidad Autónoma de Bucaramanga MedUNAB (Colombia) Num.3 Vol.26 |
| format | Artículo científico |
| id | redalyc_71979263016 |
| institution | Redalyc |
| language | en |
| publishDate | 2023 |
| publisher | Universidad Autónoma de Bucaramanga |
| spellingShingle | The Use of Artificial Intelligence Techniques in Nursing Data Systems: Scoping Review Shina Singla Medicina Evidence Based Nursing Machine Learning Artificial Intelligence Health Information Systems The Use of Artificial Intelligence Techniques in Nursing Data Systems: Scoping Review Shina Singla Lyndsay Howitt Christina Medeiros Doris Grinspun Shanoja Naik Medicina Evidence Based Nursing Machine Learning Artificial Intelligence Health Information Systems Introduction. Artificial intelligence and machine learning are technologies that assist in uncovering patterns in data that can inform clinical decision-making. The Registered Nurses’ Association of Ontario has used artificial intelligence techniques to assist in understanding impactful clinical practices and implementation strategies. This scoping review aimed to discover the adaptation and implementation of various artificial intelligence and machine learning techniques in various healthcare settings using different data systems that house nursing-related data. Methodology. In March 2022, a scoping review was conducted to search for peer-reviewed literature using the following terms: “nursing”, “artificial intelligence”, “data systems”, “statistics”, and “aggregated data”. Studies were excluded if they were not relevant to nursing, utilized qualitative or mixed-methods analyses, were literature review articles, and did not focus on artificial intelligence or the use of patient-level data. Results. A total of 2,627 articles were retrieved, with 1,518 articles remaining after de-duplication. Through title and abstract screening, 1,347 articles remained. Following the full-text screening, 13 studies remained. Artificial intelligence techniques used by healthcare data systems include regression, neural networks, classification, and graph-based methods, among others. Discussion. There is a gap in the application of artificial intelligence methods in data systems that evaluate the impact of implementing best practices in nursing. More data systems are needed that employ artificial intelligence techniques to support the evaluation of best practices in nursing and other health professions. Conclusions. Various artificial intelligence techniques in data systems housing nursing-related data were retrieved. However, more data systems and research are needed in this area. 2023 artículo científico 0123-7047 https://www.redalyc.org/articulo.oa?id=71979263016 https://www.redalyc.org/journal/719/71979263016/ https://www.redalyc.org/journal/719/71979263016/html/ https://www.redalyc.org/journal/719/71979263016/71979263016.epub https://www.redalyc.org/journal/719/71979263016/movil https://doi.org/10.29375/01237047.4634 en http://www.redalyc.org/revista.oa?id=719 MedUNAB application/pdf Universidad Autónoma de Bucaramanga MedUNAB (Colombia) Num.3 Vol.26 |
| title | The Use of Artificial Intelligence Techniques in Nursing Data Systems: Scoping Review |
| topic | Medicina Evidence Based Nursing Machine Learning Artificial Intelligence Health Information Systems |
| url | https://www.redalyc.org/articulo.oa?id=71979263016 https://www.redalyc.org/journal/719/71979263016/ https://www.redalyc.org/journal/719/71979263016/html/ https://www.redalyc.org/journal/719/71979263016/71979263016.epub https://www.redalyc.org/journal/719/71979263016/movil https://doi.org/10.29375/01237047.4634 |