Impact Of Predictive Analytics In Nursing Discussion Chapter

Length: 3 pages Sources: 3 Type: Discussion Chapter Paper: #14544172 Related Topics: Chronic Disease, Big Data, Machine Learning, Data Analytics Published August 03, 2022
Excerpt from Discussion Chapter :

Data Science Applications and Processes

In nursing, big data refers to the large amount of patient care and health data. Nurses can use data analysis to determine the best and most efficient treatment methods. Big data will allow you to analyze gazillions of data elements, which is beneficial for evidence-based best practices for nursing. Using big data, nurses can streamline their workflows by analyzing the data to determine the best way to treat patients efficiently (Elsaleh et al., 2020). Due to the frequent nursing shift changes and schedules, big data can be used to ensure enough nursing staff are available during peak hours. Big data can enhance the nursing leaders efficiency to determine how many nurses they need at any given time. Predictive models can be developed to ensure accurate planning and staffing.

Nurses can use data science to develop, evaluate, and assess patient outcomes using clinical decision support tools, care coordination activities, and health portals. Data science is the process of analyzing, asking questions, and manipulating large datasets to uncover patterns and derive knowledge. Data mining is the understanding and interpreting of data through computation techniques from statistics, pattern recognition, and machine learning to identify relationships within the information. Nurses can use data mining to make real-time decisions on patient care based on the patterns they uncover for a disease progression or treatment.

Data analytics is the procedure of examining raw datasets to discover trends, draw conclusions, and identify the potential for improvement (Spachos et al., 2020). Nurses can use data analytics to determine the best treatment…will respond differently to treatment, and using predictive analytics, health care facilities can determine patients who are likely to respond positively to treatment. For example, ongoing research aims to predict if a blood test can predict if the treatment for HPV-positive throat cancer is working months earlier. Currently, doctors have to rely on imaging scans every few months to determine if the patients tumors are shrinking (Haring et al., 2022). Therefore, the predictive blood test can benefit patients and reduce their health care costs. Early determination of treatment effectiveness allows providers to switch their treatment course if the current one is not working. Quality of care is improved because patient satisfaction is high and costs are lowered. Patients benefit from recovering sooner than if they had to go through…

Sources Used in Documents:

References


Elsaleh, T., Enshaeifar, S., Rezvani, R., Acton, S. T., Janeiko, V., & Bermudez-Edo, M. (2020). IoT-Stream: A lightweight ontology for internet of things data streams and its use with data analytics and event detection services. Sensors, 20(4), 953. https://doi.org/doi:10.3390/s20040953


Haring, C. T., Dermody, S. M., Yalamanchi, P., Kang, S. Y., Old, M. O., Brenner, J. C., Spector, M. E., & Rocco, J. W. (2022). The future of circulating tumor DNA as a biomarker in HPV related oropharyngeal squamous cell carcinoma. Oral Oncology, 126, 105776. https://doi.org/10.1016/j.oraloncology.2022.105776


Spachos, D., Siafis, S., Bamidis, P., Kouvelas, D., & Papazisis, G. (2020). Combining big data search analytics and the FDA Adverse Event Reporting System database to detect a potential safety signal of mirtazapine abuse. Health Informatics Journal, 26(3), 2265-2279. https://doi.org/doi:10.1177/1460458219901232


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