Data Science Applications and Processes
How might data compiled and analyzed in your healthcare organization or nursing practice help support efforts aimed at patient quality and safety? Why might it be important to consider the how’s and why’s of data collection, application, and implementation? How might these practices shape your nursing practice or even the future of nursing?
For this Discussion, you will explore various topics related to data and consider the process and application of each. Reflect on the use of these applications, but also consider the implications of how these applications might shape the future of nursing and healthcare practice.
Resources
Be sure to review the Learning Resources before completing this activity.
Click the weekly resources link to access the resources.
earning Resources
· Sipes, C. (2025).
Project management for the advanced practice nurse (3rd ed.). Springer Publishing.
· Chapter 4, “Planning: Project Management—Phase 2” (pp. 85–130)
· American Nurses Association. (2015).
Nursing informaticsLinks to an external site.
: Scope and standards of practice (2nd ed.).
· “Standard 3: Outcomes Identification” (p. 71)
· “Standard 4: Planning” (p. 72)1
· Brennan, P. F., & Bakken, S. (2015).
Nursing needs big data and big data needs nursingLinks to an external site..
Journal of Nursing Scholarship, 47 (5), 477–484. doi:10.1111/jnu.12159 National Institutes of Health, Office of Data Science Strategy. (2021).
Data science .
· Carter-Templeton, H., Nicoll, L. H., Wrigley, J., & Wyatt, T. H. (2021).
Big data in nursing: A bibliometric analysisLinks to an external site..
Online Journal of Issues in Nursing,
26(3).
· 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 servicesLinks to an external site..
Sensors, 20 (4), 953. doi:10.3390/s20040953
· Zhu, R., Han, S., Su, Y., Zhang, C., Yu, Q., & Duan, Z. (2019).
The application of big data and the development of nursing science: A discussion paperLinks to an external site..
International Journal of Nursing Sciences, 6 (2), 229–234. doi:10.1016/j.ijnss.2019.03.001
· IDG TECHTalk. (2020, March 27).
What is predictive analyticsLinks to an external site.
? Transforming data into future insights [Video]. YouTube.
· Simplilearn. (2019, December 10).
Big data in 5 minutes Links to an external site.
| What is big data?| introduction to big data | big data explained | simplilearn [Video]. YouTube.
· Parikh, R. B., Gdowski, A., Patt, D. A., Hertler, A., Mermel, C., & Bekelman, J. E. (2019). Using big data and predictive analytics to determine patient risk in oncology.
American Society of Clinical Oncology Educational BookLinks to an external site.
, 39 , e53–e58. doi:10.1200/EDBK_238891
· 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 abuseLinks to an external site..
Health Informatics Journal, 26 (3), 2265–2279. doi:10.1177/1460458219901232
To Prepare
· Review the Learning Resources for this week related to the topics: Big Data, Data Science, Data Mining, Data Analytics, and Machine Learning.
· Consider the process and application of each topic.
· Reflect on how each topic relates to nursing practice.
By Day 3 of Week 5
Post a summary on how predictive analytics might be used to support healthcare.
Note: These topics may overlap as you will find in the readings (e.g., some processes require both Data Mining and Analytics).
In your post include the following:
· Describe a practical application for predictive analytics in your nursing practice. What challenges and opportunities do you envision for the future of predictive analytics in healthcare?
By Day 6 of Week 5
Read a selection of your colleagues’ responses and
respond to
at least two of your colleagues on
two different days. Expand upon your colleague’s posting or offer an alternative perspective.
RESPOND TO THIS DISCUSSION POST
Latonya B
Data Science Applications and Processes
High-quality healthcare is driven by several factors that foster an environment of safety and healing. Predictive analytics is progressively changing the definition of care by enabling physicians and nurses to further anticipate their patients’ requirements. Despite the challenges that health systems face, patients expect and demand safe, high-quality care along with accurate and trustworthy hospital quality reporting. Predictive analytics could prove helpful in organizations that struggle with medical diagnosis and disease progression. Healthcare providers are expected to demonstrate cultural competence and deliver equitable treatment that facilitates healing. Predictive analytics aids with administrative tasks by optimizing processes and cutting expenses, while data analytics can assist hospitals in anticipating admissions and allocating staff and resources appropriately (Bogiri et al., 2025). There are various ways to evaluate an organization’s performance, and if certain areas do not perform well, they can lower the overall quality score. An organization’s successes, challenges, and struggles are often measured by its star rating, which indicates how well a facility is performing.
To accomplish the objective of providing patients with higher-quality treatment, health practitioners can use advanced digital analytics to assist them in making logical judgments about how to optimize resources and what tactics to use to avoid overspending. The use of digital technology can help achieve the best scores, which will prompt first actions meant to stop further health decline. Centers for Medicare and Medicaid Services (CMS) is a federal organization that oversees the administration of Medicare and Medicaid services to hospitals in the United States and uses a 5-star rating system to assess and compare the caliber of treatment that each hospital offers (Pollok et al., 2024). The overall star rating provides a summary of performance across several quality metrics, such as patient experience, staffing, customer service, readmission rates, mortality, safety of care, and prompt and efficient service.
The use of predictive models in healthcare has expanded beyond merely forecasting disease outbreaks to estimating the likelihood of disease progression (Bogiri et al., 2025). These models will be integrated into current healthcare systems and will utilize advanced AI methods, such as machine learning models, to process and analyze large data sets (Bogiri et al., 2025). During the pandemic, the 5-star ratings were heavily challenged due to the high mortality rates associated with the disease when the vaccine was not yet available. The study shows that despite these mortality rates, hospitals with high ratings were unaffected. This data did not include small hospitals or those already scoring low. Incorporating data science and technology into healthcare delivery could lead to a better healthcare experience for all healthcare organizations. The barriers that may affect the use of the applications are the ability to purchase and provide training for adequate usage.
References
Bogiri, N., Maral, V., Pagar, S., Mane, A., Babde, O., & Pandey, V. (2025). Predictive Analytics in Virtual Healthcare using Decision-Tree. 2025 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI), Data Science, Agents & Artificial Intelligence (ICDSAAI), 2025 International Conference On, 1–6.
to an external site.
Pollock, B. D., Devkaran, S., & Dowdy, S. C. (2024). Missed opportunities in hospital quality measurement during the COVID-19 pandemic: a retrospective investigation of US hospitals’ CMS Star Ratings and 30-day mortality during the early pandemic. BMJ Open, 14(2).
to an external site.
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