{"title":"护士对布雷登量表的见解及其对人工智能创新的看法：一项混合方法研究。","authors":"Tuba Sengul, Holly Kirkland-Kyhn, Dilek Yilmaz Akyaz, Tugba Cevizci","doi":"10.1111/jocn.70038","DOIUrl":null,"url":null,"abstract":"<p><strong>Aims: </strong>This study aimed to explore nurses' experiences with the Braden Scale, assess their readiness for artificial intelligence (AI) technologies, and understand the innovations they envision for clinical practice.</p><p><strong>Design: </strong>Explanatory sequential mixed design.</p><p><strong>Methods: </strong>The study included 118 nurses in the quantitative data and 42 in focus groups. Quantitative data were collected using the MAIRS-MS. Qualitative data were analysed using phenomenological approaches and MAXQDA.</p><p><strong>Results: </strong>The average age was 33.38 ± 7.42 years and 88.1% were women. The average length of professional experience is 11.66 ± 8.22 years. The average time to administer the Braden Scale was 5.02 ± 4.36 min. While 55.1% of the participants found the Braden Scale inadequate, 55.9% stated that a more comprehensive risk assessment scale was needed and the MAIRS-MS score was 78.48 ± 16.66. The sub-themes were identified: Simple and quick applicability, early risk identification, validity and reliability issues, neglecting other risk factors, making it more comprehensive and specific, developing of a new risk assessment scale, technological improvements, patient data treasure chest, creating avatars and converting speech-to-text.</p><p><strong>Conclusions: </strong>This study highlights critical gaps in the Braden Scale's effectiveness. Nurses identified significant shortcomings, including non-specificity and the neglect of key risk factors, which undermine its utility in clinical settings. They emphasised that stronger risk predictions and personalised care plans can be achieved by AI technology.</p><p><strong>Implications for professional care: </strong>This study emphasises the need to revise the Braden Scale or develop a new one due to its limitations in risk assessment, providing crucial information to improve patient care and offering new perspectives on AI integration in PI risk assessment for nursing practice.</p><p><strong>Impact: </strong>This study highlights nurses' experiences and suggestions for improving the Braden Scale in clinical practice, emphasising their expectations for AI technology and its potential to revolutionise patient care.</p><p><strong>Reporting method: </strong>The study report was prepared following the Good Reporting of A Mixed Methods Study (GRAMMS) checklist.</p><p><strong>Patient or public contribution: </strong>No patient or public contribution.</p>","PeriodicalId":50236,"journal":{"name":"Journal of Clinical Nursing","volume":" ","pages":""},"PeriodicalIF":3.5000,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Nurses' Insights on the Braden Scale and Their Vision for Artificial Intelligence Innovations: A Mixed Methods Study.\",\"authors\":\"Tuba Sengul, Holly Kirkland-Kyhn, Dilek Yilmaz Akyaz, Tugba Cevizci\",\"doi\":\"10.1111/jocn.70038\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Aims: </strong>This study aimed to explore nurses' experiences with the Braden Scale, assess their readiness for artificial intelligence (AI) technologies, and understand the innovations they envision for clinical practice.</p><p><strong>Design: </strong>Explanatory sequential mixed design.</p><p><strong>Methods: </strong>The study included 118 nurses in the quantitative data and 42 in focus groups. Quantitative data were collected using the MAIRS-MS. Qualitative data were analysed using phenomenological approaches and MAXQDA.</p><p><strong>Results: </strong>The average age was 33.38 ± 7.42 years and 88.1% were women. The average length of professional experience is 11.66 ± 8.22 years. The average time to administer the Braden Scale was 5.02 ± 4.36 min. While 55.1% of the participants found the Braden Scale inadequate, 55.9% stated that a more comprehensive risk assessment scale was needed and the MAIRS-MS score was 78.48 ± 16.66. The sub-themes were identified: Simple and quick applicability, early risk identification, validity and reliability issues, neglecting other risk factors, making it more comprehensive and specific, developing of a new risk assessment scale, technological improvements, patient data treasure chest, creating avatars and converting speech-to-text.</p><p><strong>Conclusions: </strong>This study highlights critical gaps in the Braden Scale's effectiveness. Nurses identified significant shortcomings, including non-specificity and the neglect of key risk factors, which undermine its utility in clinical settings. They emphasised that stronger risk predictions and personalised care plans can be achieved by AI technology.</p><p><strong>Implications for professional care: </strong>This study emphasises the need to revise the Braden Scale or develop a new one due to its limitations in risk assessment, providing crucial information to improve patient care and offering new perspectives on AI integration in PI risk assessment for nursing practice.</p><p><strong>Impact: </strong>This study highlights nurses' experiences and suggestions for improving the Braden Scale in clinical practice, emphasising their expectations for AI technology and its potential to revolutionise patient care.</p><p><strong>Reporting method: </strong>The study report was prepared following the Good Reporting of A Mixed Methods Study (GRAMMS) checklist.</p><p><strong>Patient or public contribution: </strong>No patient or public contribution.</p>\",\"PeriodicalId\":50236,\"journal\":{\"name\":\"Journal of Clinical Nursing\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2025-07-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Clinical Nursing\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1111/jocn.70038\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"NURSING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Clinical Nursing","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/jocn.70038","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NURSING","Score":null,"Total":0}
Nurses' Insights on the Braden Scale and Their Vision for Artificial Intelligence Innovations: A Mixed Methods Study.
Aims: This study aimed to explore nurses' experiences with the Braden Scale, assess their readiness for artificial intelligence (AI) technologies, and understand the innovations they envision for clinical practice.
Design: Explanatory sequential mixed design.
Methods: The study included 118 nurses in the quantitative data and 42 in focus groups. Quantitative data were collected using the MAIRS-MS. Qualitative data were analysed using phenomenological approaches and MAXQDA.
Results: The average age was 33.38 ± 7.42 years and 88.1% were women. The average length of professional experience is 11.66 ± 8.22 years. The average time to administer the Braden Scale was 5.02 ± 4.36 min. While 55.1% of the participants found the Braden Scale inadequate, 55.9% stated that a more comprehensive risk assessment scale was needed and the MAIRS-MS score was 78.48 ± 16.66. The sub-themes were identified: Simple and quick applicability, early risk identification, validity and reliability issues, neglecting other risk factors, making it more comprehensive and specific, developing of a new risk assessment scale, technological improvements, patient data treasure chest, creating avatars and converting speech-to-text.
Conclusions: This study highlights critical gaps in the Braden Scale's effectiveness. Nurses identified significant shortcomings, including non-specificity and the neglect of key risk factors, which undermine its utility in clinical settings. They emphasised that stronger risk predictions and personalised care plans can be achieved by AI technology.
Implications for professional care: This study emphasises the need to revise the Braden Scale or develop a new one due to its limitations in risk assessment, providing crucial information to improve patient care and offering new perspectives on AI integration in PI risk assessment for nursing practice.
Impact: This study highlights nurses' experiences and suggestions for improving the Braden Scale in clinical practice, emphasising their expectations for AI technology and its potential to revolutionise patient care.
Reporting method: The study report was prepared following the Good Reporting of A Mixed Methods Study (GRAMMS) checklist.
Patient or public contribution: No patient or public contribution.
期刊介绍：
The Journal of Clinical Nursing (JCN) is an international, peer reviewed, scientific journal that seeks to promote the development and exchange of knowledge that is directly relevant to all spheres of nursing practice. The primary aim is to promote a high standard of clinically related scholarship which advances and supports the practice and discipline of nursing. The Journal also aims to promote the international exchange of ideas and experience that draws from the different cultures in which practice takes place. Further, JCN seeks to enrich insight into clinical need and the implications for nursing intervention and models of service delivery. Emphasis is placed on promoting critical debate on the art and science of nursing practice.
JCN is essential reading for anyone involved in nursing practice, whether clinicians, researchers, educators, managers, policy makers, or students. The development of clinical practice and the changing patterns of inter-professional working are also central to JCN''s scope of interest. Contributions are welcomed from other health professionals on issues that have a direct impact on nursing practice.
We publish high quality papers from across the methodological spectrum that make an important and novel contribution to the field of clinical nursing (regardless of where care is provided), and which demonstrate clinical application and international relevance.
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