NURS 5051 Module 3: Data-Information-Knowledge-Wisdom (DIKW) (Week 5)
NURS 5051 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
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| Due By | Assignment |
| Week 5, Days 1–2 | Read/Watch/Listen to the Learning Resources. Compose your initial Discussion post. |
| Week 5, Day 3 | Post your initial Discussion post. |
| Week 5, Days 4-5 | Review peer Discussion posts. Compose your peer Discussion responses. |
| Week 5, Day 6 | Post at least two peer Discussion responses on two different days (and not the same day as the initial post). |
| Week 5, Day 7 | Wrap up Discussion. |
Learning Resources
Required Readings
McGonigle, D., & Mastrian, K. G. (2017). Nursing informatics and the foundation of knowledge (4th ed.). Burlington, MA: Jones & Bartlett Learning. NURS 5051 Module 3: Data-Information-Knowledge-Wisdom (DIKW) (Week 5)
- Chapter 22, “Data Mining as a Research Tool” (pp. 477-493)
- Chapter 24, “Bioinformatics, Biomedical Informatics, and Computational Biology” (pp. 537-551)
Required Media
Laureate Education (Producer). (2018). Health Informatics and Population Health: Analyzing Data for Clinical Success [Video file]. Baltimore, MD: Author.
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. NURS 5051 Module 3: Data-Information-Knowledge-Wisdom (DIKW) (Week 5)
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 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. NURS 5051 Module 3: Data-Information-Knowledge-Wisdom (DIKW) (Week 5)
*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:
