Observational studyRevista latino-americana de enfermagem2024
Nursing workload: use of artificial intelligence to develop a classifier model.
Observational study in Revista latino-americana de enfermagem, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Nurses' Experiences Using AI in Clinical Practice: Systematic Review.JMIR nursing · 2026Pooled it
- Integrative review of artificial intelligence applications in nursing: education, clinical practice, workload management, and professional perceptions.Frontiers in public health · 2025Pooled it
- Effects of a transformer-based AI-based application to support incontinence-associated dermatitis and pressure injury assessment, nursing care and documentation: Controlled pilot intervention study.International journal of nursing studies advances · 2026Article
- Digital Innovations in Nursing Workforce Management: A Scoping Review of Advanced Applications and Implementation Challenges.Journal of nursing management · 2026Article
- Development of a Data-Based Method for Predicting Nursing Workload in an Acute Care Hospital: Methodological Study.Journal of medical Internet research · 2025Observational
- Estimating Nurse Workload Using a Predictive Model From Routine Hospital Data: Algorithm Development and Validation.JMIR medical informatics · 2025Article
- Artificial intelligence in Brazilian Primary Health Care: scoping review.Revista brasileira de enfermagem · 2025Article
- Artificial intelligence in nursing: an integrative review of clinical and operational impacts.Frontiers in digital health · 2025Review
- Navigating Society 5.0: Integrating Strength-Based Leadership and Job Crafting for Improved Nursing Outcomes.Journal of nursing management · 2025Article
- Digital health: contributions from Nursing.Revista latino-americana de enfermagem · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveto describe the development of a predictive nursing workload classifier model, using artificial intelligence.
methodretrospective observational study, using secondary sources of electronic patient records, using machine learning. The convenience sample consisted of 43,871 assessments carried out by clinical nurses using the Perroca Patient Classification System, which served as the gold standard, and clinical data from the electronic medical records of 11,774 patients, which constituted the variables. In order to organize the data and carry out the analysis, the Dataiku® data science platform was used. Data analysis occurred in an exploratory, descriptive and predictive manner. The study was approved by the Ethics and Research Committee of the institution where the study was carried out.
resultsthe use of artificial intelligence enabled the development of the nursing workload assessment classifier model, identifying the variables that most contributed to its prediction. The algorithm correctly classified 72% of the variables and the area under the Receiver Operating Characteristic curve was 82%.
conclusiona predictive model was developed, demonstrating that it is possible to train algorithms with data from the patient's electronic medical record to predict the nursing workload and that artificial intelligence tools can be effective in automating this activity.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.