DATA SCIENCE APPLICATIONS AND PROCESSES
Data mining has been cited as one of the advantages scientists used in the creation of the COVID-19 vaccinations. Data mining was used in the trials of these vaccinations to signal safety concerns and trends more quickly in the trial groups. As a result, these vaccinations were quickly available to support the effort in combatting the COVID-19 pandemic.
Thinking beyond the scope of a major vaccination effort and pandemic, 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.
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 succinct summary on how each topic might apply to nursing practice. Be specific. 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:
• Explain how you see the data concepts presented aligning with your current practice. What do you need to know to apply these concepts?
• Do you currently use one of these processes in your healthcare organization or nursing practice? If so, how and in what context?
• If you do not currently use one of these processes in your healthcare organization or nursing practice, what would it take to implement it? What do you see as a benefit for use?
• How is predictive analytics applied to clinical practice? Be specific and provide examples.
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 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
Media Resources
• Sipes, C. (2020). Project management for the advanced practice nurse (2nd ed.). Springer Publishing.
o 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.).
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
Other 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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