Transforming Nursing
DUE MARCH 25
TRANSFORMING NURSING
DUE MARCH 25
This is a graded discussion: 100 points possible
Week 5: Discussion
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.
LEARNING RESOURCES
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 minutes Links 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
· Sipes, C. (2020). Project management for the advanced practice nurse (2nd ed.). Springer Publishing.
· Chapter 4, “Planning: Project Management—Phase 2” (pp. 75–120)
· 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.
· 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
· 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
· 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
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.
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?
Assignment Rubric Details Close
Rubric
NURS_8210_Week5_Discussion_Rubric
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NURS_8210_Week5_Discussion_Rubric |
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Criteria |
Ratings |
Pts |
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This criterion is linked to a Learning OutcomeRESPONSIVENESS TO DISCUSSION QUESTION (20 possible points) Discussion post minimum requirements: The original posting must be completed by Day 3 at 10:59 pm CT. Two response postings to two different peer original posts, on two different days, are required by Day 6 at 10:59 pm CT. Faculty member inquiries require responses, which are not included in the peer posts. Your Discussion Board postings should be written in Standard Academic English and follow APA 7 style for format and grammar as closely as possible given the constraints of the online platform. Be sure to support the postings with specific citations from this week's learning resources as well as resources available through the Walden University library and other credible online resources (guidelines, expert opinions etc.) |
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20 pts |
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This criterion is linked to a Learning OutcomeCONTENT REFLECTION and MASTERY: Initial Post (30 possible points) |
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30 pts |
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This criterion is linked to a Learning OutcomeCONTRIBUTION TO THE DISCUSSION: First Response (20 possible points) |
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20 pts |
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This criterion is linked to a Learning OutcomeCONTRIBUTION TO THE DISCUSSION: Second Response (20 possible points) |
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20 pts |
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This criterion is linked to a Learning OutcomeQUALITY OF WRITING (10 possible points) |
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10 pts |
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Total Points: 100 |