Exploring Applications of Artificial Intelligence in Critical Care Nursing: A Systematic Review.
Background: Artificial intelligence (AI) has been increasingly employed in healthcare across diverse domains, including medical imaging, personalized diagnostics, therapeutic interventions, and predictive analytics using electronic health records. Its integration is particularly impactful in critica...
| Publicado en: | Nursing Reports Vol. 15; no. 2; pp. 55 - 73 |
|---|---|
| Autores principales: | , , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
MDPI
Feb2025
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=183341891&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 183341891 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2039439X EGNT jtl: Nursing Reports issn: 2039439X maglogo: N pubinfo: dt: Feb2025 vid: 15 iid: 2 pid: 97109 pub: MDPI artinfo: ui: 183341891 183341891 183341891 10.3390/nursrep15020055 183341891 ppf: 55 ppct: 18 formats: fmt: @attributes: type: P tig: atl: Exploring Applications of Artificial Intelligence in Critical Care Nursing: A Systematic Review. aug: au: Porcellato, Elena Lanera, Corrado Ocagli, Honoria Danielis, Matteo affil: Laboratory of Studies and Evidence Based Nursing, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Via Loredan 18, 35131 Padova, Italy sug: subj: Artificial Intelligence Evaluation Critical Care Nursing Emergency Nursing Operating Room Nursing Recovery Room Nursing Prehospital Care Nursing Outcomes Human Adolescence Adult Systematic Review Medline PubMed CINAHL Database Machine Learning Support Vector Machine Postoperative Complications Patient Admission Patient Discharge Triage Pressure Ulcer Sepsis Delirium Electronic Health Records Nursing Practice Adolescent: 13-18 years Adult: 19-44 years ab: Background: Artificial intelligence (AI) has been increasingly employed in healthcare across diverse domains, including medical imaging, personalized diagnostics, therapeutic interventions, and predictive analytics using electronic health records. Its integration is particularly impactful in critical care, where AI has demonstrated the potential to enhance patient outcomes. This systematic review critically evaluates the current applications of AI within the domain of critical care nursing. Methods: This systematic review is registered with PROSPERO (CRD42024545955) and was conducted in accordance with PRISMA guidelines. Comprehensive searches were performed across MEDLINE/PubMed, SCOPUS, CINAHL, and Web of Science. Results: The initial review identified 1364 articles, of which 24 studies met the inclusion criteria. These studies employed diverse AI techniques, including classical models (e.g., logistic regression), machine learning approaches (e.g., support vector machines, random forests), deep learning architectures (e.g., neural networks), and generative AI tools (e.g., ChatGPT). The analyzed health outcomes encompassed postoperative complications, ICU admissions and discharges, triage assessments, pressure injuries, sepsis, delirium, and predictions of adverse events or critical vital signs. Most studies relied on structured data from electronic medical records, such as vital signs and laboratory results, supplemented by unstructured data, including nursing notes and patient histories; two studies also integrated audio data. Conclusion: AI demonstrates significant potential in nursing, facilitating the use of clinical practice data for research and decision-making. The choice of AI techniques varies based on the specific objectives and requirements of the model. However, the heterogeneity of the studies included in this review limits the ability to draw definitive conclusions about the effectiveness of AI applications in critical care nursing. Future research should focus on more robust, interventional studies to assess the impact of AI on nursing-sensitive outcomes. Additionally, exploring a broader range of health outcomes and AI applications in critical care will be crucial for advancing AI integration in nursing practices. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|