Nursing workload: use of artificial intelligence to develop a classifier model
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OBJECTIVE: to describe the development of a predictive nursing workload classifier model, using artificial intelligence. METHOD: retrospective 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. RESULTS: the 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%. CONCLUSION: a 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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Rosa et al. (44) developed an AI-based nursing workload classifier that achieved 72% accuracy and 82% AUC-ROC.
“Rosa et al. (44) developed an AI-based nursing workload classifier that achieved 72% accuracy and 82% AUC-ROC.”
From Artificial intelligence in nursing: an integrative review of clinical and operational impacts (2025) · cites this paper as [44]
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Artificial intelligence in nursing: an integrative review of clinical and operational impacts
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- Authors
- Rosa, N. G. D., Vaz, T. A., & Lucena, A. D. F.
- Affiliations
- Universidade Federal do Rio Grande do Sul (BR)
- University Medical Center Utrecht (NL)
- Conselho Nacional de Desenvolvimento Científico e Tecnológico (BR)
- Venue
- Revista Latino-Americana de Enfermagem
- Publisher
- FapUNIFESP (SciELO)
- Published
- 2024
- Volume
- 32
- Pages
- e4239-e4239
- ISSN
- 0104-1169 (ISSN-L)
- 1518-8345
- PMC
- PMC11251687
- OpenAlex
- W4400362060
- Cited by
- 13
- References
- 34
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 3
- FWCI
- 14.45
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Revista Latino-Americana de Enfermagem (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Nursing › Issues, ethics and legal aspects › Nursing Diagnosis and Documentation
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- nursing workload, workload prediction, electronic health records, machine learning classifiers, artificial intelligence in nursing
- MeSH terms
- Adult, Aged, Artificial Intelligence, Female, Humans, Male, Middle Aged, Nursing, Retrospective Studies, Workload, Young Adult, Electronic Health Records
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