NURS 6051: Transforming Nursing and Healthcare Through Technology

Learning Objectives

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Students will:

  • Analyze benefits, challenges, and risks of using big data in clinical systems
  • Recommend strategies to mitigate challenges and risks of using big data in clinical systems
  • Analyze the importance of standardized terminologies for nursing informatics and healthcare delivery
  • Analyze the benefits and challenges of implementing standardized nursing terminologies for nursing practice
Due By Assignment
Week 4, Days 1–2 Read/Watch/Listen to the Learning Resources.
Compose your initial Discussion post.
Week 4, Day 3 Post your initial Discussion post.
Begin to compose your Assignment.
Week 4, Days 4-5 Review peer Discussion posts.
Compose your peer Discussion responses.
Continue to compose your Assignment.
Week 4, Day 6 Post at least two peer Discussion responses on two different days (and not the same day as the initial post).
Week 4, Day 7 Wrap up Discussion.
Week 5, Days 1-6 Continue to compose your Assignment.
Week 5, Day 7 Deadline to submit your Assignment.

 

Learning Resources

Note: To access this week’s required library resources, please click on the link to the Course Readings List, found in the Course Materials section of your Syllabus.

Required Readings

McGonigle, D., & Mastrian, K. G. (2017). Nursing informatics and the foundation of knowledge (4th ed.). Burlington, MA: Jones & Bartlett Learning.

  • Chapter 25, “The Art of Caring in Technology-Laden Environments” (pp. 525–535)
  • Chapter 26, “Nursing Informatics and the Foundation of Knowledge” (pp. 537–551)

American Nurses Association. (2018). Inclusion of recognized terminologies supporting nursing practice within electronic health records and other health information technology solutions. Retrieved from https://www.nursingworld.org/practice-policy/nursing-excellence/official-position-statements/id/Inclusion-of-Recognized-Terminologies-Supporting-Nursing-Practice-within-Electronic-Health-Records/

Macieria, T. G. R., Smith, M. B., Davis, N., Yao, Y., Wilkie, D. J., Lopez, K. D., & Keenan, G. (2017). Evidence of progress in making nursing practice visible using standardized nursing data: A systematic review. AMIA Annual Symposium Proceedings, 2017, 1205–1214. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5977718/

Office of the National Coordinator for Health Information Technology. (2017). Standard nursing terminologies: A landscape analysis. Retrieved from https://www.healthit.gov/sites/default/files/snt_final_05302017.pdf

Rutherford, M. A. (2008). Standardized nursing language: What does it mean for nursing practice? Online Journal of Issues in Nursing, 13(1), 1–12. doi:10.3912/OJIN.Vol13No01PPT05.

Note: You will access this article from the Walden Library databases.

Thew, J. (2016, April 19). Big data means big potential, challenges for nurse execs. Retrieved from https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execs

Topaz, M. (2013). The hitchhiker’s guide to nursing theory: Using the Data-Knowledge-Information-Wisdom framework to guide informatics research. Online Journal of Nursing Informatics, 17(3).

Note: You will access this article from the Walden Library databases.

Wang, Y. Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126(1), 3–13. doi:10.1016/j.techfore.2015.12.019.

Note: You will access this article from the Walden Library databases.

Required Media

Laureate Education (Executive Producer). (2012). Data, information, knowledge and wisdom continuum [Multimedia file]. Baltimore, MD: Author. Retrieved from http://mym.cdn.laureate-media.com/2dett4d/Walden/NURS/6051/03/mm/continuum/index.html

Laureate Education (Producer). (2018). Health Informatics and Population Health: Analyzing Data for Clinical Success [Video file]. Baltimore, MD: Author.

When you wake in the morning, you may reach for your cell phone to reply to a few text or email messages that you missed overnight. On your drive to work, you may stop to refuel your car. Upon your arrival, you might swipe a key card at the door to gain entrance to the facility. And before finally reaching your workstation, you may stop by the cafeteria to purchase a coffee.

From the moment you wake, you are in fact a data-generation machine. Each use of your phone, every transaction you make using a debit or credit card, even your entrance to your place of work, creates data. It begs the question: How much data do you generate each day? Many studies have been conducted on this, and the numbers are staggering: Estimates suggest that nearly 1 million bytes of data are generated every second for every person on earth.

As the volume of data increases, information professionals have looked for ways to use big data—large, complex sets of data that require specialized approaches to use effectively. Big data has the potential for significant rewards—and significant risks—to healthcare. In this Discussion, you will consider these risks and rewards.

To Prepare:

  • Review the Resources and reflect on the web article Big Data Means Big Potential, Challenges for Nurse Execs.
  • Reflect on your own experience with complex health information access and management and consider potential challenges and risks you may have experienced or observed.

By Day 3 of Week 4

Post a description of at least one potential benefit of using big data as part of a clinical system and explain why. Then, describe at least one potential challenge or risk of using big data as part of a clinical system and explain why. Propose at least one strategy you have experienced, observed, or researched that may effectively mitigate the challenges or risks of using big data you described. Be specific and provide examples.

By Day 6 of Week 4

Respond to at least two of your colleagues* on two different days, by offering one or more additional mitigation strategies or further insight into your colleagues’ assessment of big data opportunities and risks.

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