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.

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.

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.

Week 5: Discussion
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DATA SCIENCE APPLICATIONS AND PROCESSES

NURS 8210 WEEK 5 DISCUSSION
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.

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RESOURCES

Be sure to review the Learning Resources before completing this activity.
Click the weekly resources link to access the resources.
WEEKLY RESOURCES
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?
LEARNING RESOURCES
Required Readings
Begin your review of required Learning Resources with these quick media resources to define some of the many terms you will hear in Nursing Informatics and Project Management today. If you are more interested in a particular one, there are many longer videos available.
• GovLoop. (2016, June 15). Defining data analyticsLinks to an external site. [Video]. YouTube. https://www.youtube.com/watch?v=RAw55JEcnEs
• IDG TECHTalk. (2020, March 27). What is predictive analyticsLinks to an external site.? Transforming data into future insights [Video]. YouTube. https://www.youtube.com/watch?v=cVibCHRSxB0
• ProjectManager. (2016, March 11). Gantt charts, simplified – project management trainingLinks to an external site. [Video]. YouTube. https://www.youtube.com/watch?v=cGkHjby1xKM
• Simplilearn. (2017, August 3). Data science vs big data vs data analyticsLinks to an external site. [Video]. YouTube. https://www.youtube.com/watch?v=yR2wWQYiVKM
• Simplilearn. (2019, December 10). Big data in 5 minutesLinks to an external site. | What is big data?| introduction to big data | big data explained | simplilearn [Video]. YouTube. https://www.youtube.com/watch?v=bAyrObl7TYE
Required Media
• Sipes, C. (2024). Project management for the advanced practice nurse (3rd ed.). Springer Publishing.
o 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.).
o “Standard 3: Outcomes Identification” (p. 71)
o “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.
• National Institutes of Health, Office of Data ScienceLinks to an external site. Strategy. (2021). Data science. https://datascience.nih.gov/
• 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
Data analysis
• 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
• 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
Optional Resources
• Mehta N., & Pandit, A. (2018). Concurrence of big data analytics and healthcare: A systematic review. International Journal of Medical InformaticsLinks to an external site., 114, 57–65. doi:10.1016/j.ijmedinf.2018.03.013
• Ristevski, B., & Chen, M. (2018). Big data analytics in medicine and healthcare. Journal of Integrative BioinformaticsLinks to an external site., 15(3), 1–5. https://doi.org/10.1515/jib-2017-0030
• Shea, K. D., Brewer, B. B., Carrington, J. M., Davis, M., Gephart, S., & Rosenfeld, A. (2018). A model to evaluate data science in nursing doctoral curricula. Nursing OutlookLinks to an external site., 67(1), 39–48. https://www.nursingoutlook.org/article/S0029-6554(18)30324-5/fulltext
• Sheehan, J., Hirschfeld, S., Foster, E., Ghitza, U., Goetz, K., Karpinski, J., Lang, L., Moser. R. P., Odenkirchen, J., Reeves, D., Runinstein, Y., Werner, E., & Huerta, M. (2016). Improving the value of clinical research through the use of common data elements. Clinical Trials, 13(6), 671–676, doi:10.1177/ 1740774516653238
• Topaz, M., & Pruinelli, L. (2017). Big data and nursing: Implications for the futureLinks to an external site.. Studies in Health Technology and Informatics, 232, 165–171.
• Westra, B. L., Sylvia, M., Weinfurter, E. F., Pruinelli, L., Park, J. I., Dodd, D., Keenan, G. M., Senk, P., Richesson, R. L., Baukner, V., Cruz, C., Gao, G., Whittenburg, L., & Delaney, C. W. (2017). Big data science: A literature review of nursing research exemplarsLinks to an external site.. Nursing Outlook, 65(5), 549–561.
• Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, A., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. O., Bourne, P., Bouwman, J., Brookes, A. J., Clark. T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C., Finkers, R., … González-Beltrán, A. (2016). The FAIR guiding principles for scientific data management and stewardship. Scientific DataLinks to an external site., 3, Article 160018, 1–9. doi:10.1038/sdata.2016.18

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NURS 8210 Week 5 Discussion

Predictive analytics are advanced analytics employed to predict future outcomes, including health outcomes. The prediction is achieved using historical data, statistical modeling, data mining techniques, and machine learning. Besides, predictive analytics is also used to identify patterns and data structures and cluster them into groups or insights using logic derived from theories (Zhang, 2020). Thus, predictive analytics can be employed in healthcare to identify data patterns, which helps recognize health risks. For instance, healthcare organizations use data and patterns to identify and manage the care of patients with chronic illnesses.

In mental health nursing, predictive analysis can be used to provide personalized care to patients by monitoring individual progress toward achieving the desired health goals (Zhang, 2020). Moreover, predictive analytics supports healthcare at a patient individual level by guiding healthcare professionals and providers to provide the appropriate care to the right patient at the right time. It can also provide mental health providers with evidence-based information to guide them in clinical decision-making.

 As much as predictive analysis can improve healthcare delivery and patient outcomes, it may experience some challenges in its implementation. For instance, there may be challenges in managing and analyzing unstructured data since it requires advanced techniques for data mining, natural language processing, and image recognition to obtain meaningful insights from sources like clinical notes, research articles, and diagnostic images (Goyal & Malviya, 2023). Nonetheless, predictive analytics can improve operational efficiencies, patient safety, and patient outcomes by helping organizations and providers predict when, where, and how patient care should be provided. Furthermore, it can help develop new drugs and conduct clinical trials by analyzing all available data instead of relying on inadequate test samples.

References

Goyal, P., & Malviya, R. (2023). Challenges and opportunities of big data analytics in healthcare. Health Care Science, 2(5), 328-338. https://doi.org/10.1002/hcs2.66

Zhang, Z. (2020). Predictive analytics in the era of big data: opportunities and challenges. Annals of Translational Medicine, 8(4), 68. https://doi.org/10.21037/atm.2019.10.97