Nursing and Data: Powering Nursing Leaders for Big Data Science

Pruinelli, Lisiane

doi:10.1590/0034-7167.2021740401

Cited by 2 publications

(3 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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