NURS 6051 Module 3: Data-Information-Knowledge-Wisdom (DIKW) (Week 5)

/in /by

NURS 6051 Module 3: Data-Information-Knowledge-Wisdom (DIKW) (Week 5)

Laureate Education (Producer). (2018). Data-Information-Knowledge-Wisdom [Video file]. Baltimore, MD: Author.

Learning Objectives

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

Learning Resources

Required Readings

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

  • Chapter 22, “Data Mining as a Research Tool” (pp. 477-493)
  • Chapter 24, “Bioinformatics, Biomedical Informatics, and Computational Biology” (pp. 537-551)

Glassman, K. S. (2017). Using data in nursing practice. American Nurse Today, 12(11), 45–47. Retrieved from https://www.americannursetoday.com/wp-content/uploads/2017/11/ant11-Data-1030.pdf. NURS 6051 Module 3: Data-Information-Knowledge-Wisdom (DIKW) (Week 5)

 

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

 

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.

 

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.

 

Vinay Shanthagiri. (2014). Big Data in Health Informatics [Video file]. Retrieved from https://www.youtube.com/watch?v=4W6zGmH_pOw

 

Week 5 Discussion

Discussion: Big Data Risks and Rewards

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.

ORDER   A PLAGIARISM-FREE PAPER   NOW

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. NURS 6051 Module 3: Data-Information-Knowledge-Wisdom (DIKW) (Week 5)

By Day 3 of Week 5

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 5

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.

*Note: Throughout this program, your fellow students are referred to as colleagues.

Submission and Grading Information

Grading Criteria

To access your rubric:

Week 5 Discussion Rubric

 

Post by Day 3 and Respond by Day 6 of Week 5

To participate in this Discussion:

Week 5 Discussion

 

Next Module

To go to the next module:

Module 4