Nursing and Data: Powering Nursing Leaders for Big Data Science

Pruinelli, Lisiane

doi:10.1590/0034-7167.2021740401

Cited by 9 publications

(13 citation statements)

References 6 publications

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“…In this context, the dimensions and infrastructure of the institution, its information system, and the quality of the records must be considered (17)(18) . Pruinelli argues that "AI models need to be built in a safe, ethical and human-centered way" (19) . In this way, the models would follow the progression of diseases, respecting their temporal trajectory and assisting in the provision of care (12,19) .…”

Section: Discussionmentioning

confidence: 99%

“…Pruinelli argues that "AI models need to be built in a safe, ethical and human-centered way" (19) . In this way, the models would follow the progression of diseases, respecting their temporal trajectory and assisting in the provision of care (12,19) . The refinement of the parameters informed to the ML tool is crucial for its performance while respecting the peculiarities of the public served.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, AI can help healthcare professionals make correct judgments, obtain accurate information, at the right time, to support better clinical decision making and provide timely care to patients. Such a dynamic would take place "through the dissemination of cognitive knowledge and decision support" (19) , by visualizing patient trends, which can provide input for both immediate patient care and long-term planning and management (12,18) .…”

Section: Discussionmentioning

confidence: 99%

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Beyond technology: Can artificial intelligence support clinical decisions in the prediction of sepsis?

et al. 2022

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Objective: To analyze the critical alarms predictors of clinical deterioration/sepsis for clinical decision making in patients admitted to a reference hospital complex. Methods: An observational retrospective cohort study. The Machine Learning (ML) tool, Robot Laura®, scores changes in vital parameters and lab tests, classifying them by severity. Inpatients and patients over 18 years of age were included. Results: A total of 122,703 alarms were extracted from the platform, classified as 2 to 9. The pre-selection of critical alarms (6 to 9) indicated 263 urgent alerts (0.2%), from which, after filtering exclusion criteria, 254 alerts were delimited for 61 inpatients. Patient mortality from sepsis was 75%, of which 52% was due to sepsis related to the new coronavirus. After the alarms were answered, 82% of the patients remained in the sectors. Conclusions: Far beyond technology, ML models can speed up assertive clinical decisions by nurses, optimizing time and specialized human resources.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Beyond technology: Can artificial intelligence support clinical decisions in the prediction of sepsis?

et al. 2022

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“…Thus, nurses’ approach to data science expands the possibilities of developing innovative solutions, with useful information, extracted from databases ( 7 ) , especially in institutions where there is an environment favorable to the computerization of all stages of the Nursing Process (NP), which promotes the systematization of data and the use of standardized language, such as the Nursing Diagnoses (NDs) of NANDA International (NANDA-I) ( 8 ) and the interventions and activities of the Classification of Nursing Interventions (NIC) ( 9 ) .…”

Section: Introductionmentioning

confidence: 99%

“…Analytical databases introduce concepts related to data volumes, speed, variety, veracity and value, which characterize the definition of Big Data, demonstrating identity with this dynamic context of hospitals, which are benefiting from the development of intelligent systems ( 7 ) .…”

Section: Introductionmentioning

confidence: 99%

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

Rosa,

Vaz,

Lucena

2024

Rev. Latino-Am. Enfermagem

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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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