Nursing workload: use of artificial intelligence to develop a classifier model

• Published in: Revista latino-americana de enfermagem Vol. 32; p. e4239
• Main Authors: Rosa, Ninon Girardon da, Vaz, Tiago Andres, Lucena, Amália de Fátima
• Format: Journal Article
• Language: English; Portuguese; Spanish
• Published: Brazil Universidade de São Paulo-USP, Escola de Enfermagem de Ribeirão Preto - USP 01.01.2024; Escola de Enfermagem de Ribeirão Preto / Universidade de São Paulo; Universidade de São Paulo
• Subjects: Adult; Aged; Artificial Intelligence; Electronic Health Records; Electronic Health Records - statistics & numerical data; Female; Humans; Machine Learning; Male; Medical records; Middle Aged; NURSING; Nursing administration; Nursing Informatics; Original; Retrospective Studies; Workload; Workload - statistics & numerical data; Workloads; Young Adult; Aprendizaje Automático; Inteligência Artificial; Aprendizado de Máquina; Artificial Intelligence; Enfermagem; Expedientes Electrónicos de Salud; Machine Learning; Nursing Informatics; Workload; Informática Aplicada a la Enfermería; Nursing; Inteligencia Artificial; Informática em Enfermagem; Carga de Trabajo; Carga de Trabalho; Enfermería; Electronic Health Records; Registros Eletrônicos em Saúde
• ISSN: 1518-8345; 0104-1169; 1518-8345
• DOI: 10.1590/1518-8345.7131.4239

to describe the development of a predictive nursing workload classifier model, using artificial intelligence. 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. 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%. 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.