NURS 8210 Week 5 discussion: DATA SCIENCE APPLICATIONS AND PROCESSES

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.

 ProjectManager. (2016, March 11). Gantt charts, simplified – project management

trainingLinks to an external site. [Video]. YouTube.

 Simplilearn. (2017, August 3). Data science vs big data vs data analyticsLinks to an

external site. [Video]. YouTube.

 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.

 

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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