NURS 6051 – Transforming Nursing and Healthcare Through Technology
Discussion Board Assignment: Big Data, Risks, and Rewards in Nursing Practice

This assignment brief outlines a graduate-level online discussion for NURS 6051 focused on big data in healthcare, its benefits and risks for nursing practice, and the implications for patient outcomes and decision making. The structure, expectations, and evaluation criteria reflect current NURS 6051–style discussion activities used in similar informatics courses in 2024–2025.

Discussion Overview

Big data, advanced analytics, and large clinical data sets are increasingly used to guide decisions, allocate resources, and predict risk across healthcare systems. As a graduate nurse, you are expected to understand both the potential and the limitations of big data for improving safety, quality, and equity in care.

In this discussion, you will critically examine how big data is used (or could be used) in your practice or a preferred setting, assess the associated risks and rewards, and consider the nurse’s role in ensuring meaningful, ethical, and safe use of large data sets.

Learning Objectives

By the end of this discussion, you should be able to:

• Explain what “big data” means in the context of nursing and healthcare delivery.

• Evaluate potential benefits of big data analytics for patient outcomes, safety, and system performance.

• Identify key risks and challenges related to data quality, privacy, bias, and misinterpretation.

• Articulate how nurses and nurse informaticists can influence responsible, patient-centered uses of big data in their organizations.

Discussion Prompt

Primary Post (Initial Contribution)

By the due date specified in your course shell, write a substantive initial post (approximately 500–700 words) that responds to all parts of the prompt below.

1. Define big data in your context

   • Briefly define big data in healthcare and describe one real or plausible example of big data use in your current or chosen practice setting (e.g., predictive models for readmissions, sepsis alerts, population health dashboards, staffing analytics).

2. Rewards and opportunities

   • Discuss at least two specific ways this big data application could improve patient outcomes, safety, care coordination, or workflow efficiency.

   • Identify which patient, organizational, or equity outcomes might be positively affected and explain why.

3. Risks, limitations, and unintended consequences

   • Analyze at least two key risks or limitations (e.g., data quality issues, incomplete or biased data sets, algorithmic bias, alert fatigue, loss of clinical judgment, privacy threats).

   • Describe at least one equity, ethical, or trust-related concern (for example, how data are collected, who is under‑represented, or how predictions might reinforce disparities).

4. Nurse and nurse informaticist roles

   • Explain how frontline nurses and nurse informaticists can help ensure that big data tools are clinically meaningful, interpretable, and aligned with patient-centered care.

   • Suggest at least two concrete actions nurses can take (e.g., participating in data quality initiatives, giving feedback on analytics dashboards, raising concerns about bias or workflow impact, educating colleagues).

5. Evidence support

   • Integrate at least two current peer-reviewed sources (2019–2025) that address big data, data quality, decision support, or nursing informatics, and briefly summarize how each source informs your view.

Peer Responses

Respond to at least two classmates (approximately 150–200 words each) by the end of the discussion week.

• Compare your setting, big data example, or perspective with theirs and highlight one area of agreement and one area where you see a different risk, opportunity, or implication.

• Offer at least one additional insight, relevant question, or suggestion that helps deepen their analysis, supported by course concepts or an additional source when appropriate.

Participation and Netiquette Expectations

• Post your initial response early enough to allow time for meaningful interaction.

• Use professional, respectful language, and maintain confidentiality by avoiding real patient identifiers or sensitive organizational details.

• Support key points with scholarly or authoritative sources; format citations according to your program’s required style (e.g., APA or Harvard).

Discussion Grading Rubric (Indicative)

Use the criteria below as a guide when crafting your posts. Specific point values may be adapted to your institution’s gradebook.

1. Content Understanding and Application (40%)

• Demonstrates clear, accurate understanding of big data in healthcare and correctly applies concepts to a specific practice context.

• Explores both rewards and risks in a balanced, analytical way, going beyond surface description.

• Makes explicit connections between big data use, nursing practice, and patient or system outcomes.

2. Critical Thinking and Insight (25%)

• Identifies meaningful ethical, equity, or safety concerns related to big data, including data quality or bias issues.

• Proposes realistic, practice-based strategies for how nurses and nurse informaticists can address risks and enhance benefits.

• Shows depth of reflection by questioning assumptions, considering alternative perspectives, or linking to broader health system implications.

3. Use of Evidence and Course Resources (15%)

• Integrates at least two recent scholarly sources (2019–2025) that are relevant to big data, analytics, nursing informatics, or decision support.

• Accurately paraphrases and cites evidence to support claims rather than relying on unsupported opinion.

4. Engagement and Collegial Interaction (10%)

• Provides timely, substantive responses to peers that advance the discussion (e.g., building on ideas, offering alternatives, asking probing questions).

• Demonstrates professional netiquette, respect, and attention to others’ viewpoints.

5. Writing Quality and Mechanics (10%)

• Writing is clear, organized, and coherent, with logical flow and appropriate paragraph structure.

• Grammar, spelling, and sentence structure support readability; citations and references follow the required format.

APA References

Alharbi, M., & Alqurashi, S. (2019). The application of big data and the development of nursing science. International Journal of Nursing Sciences, 6(2), 229–234. https://doi.org/10.1016/j.ijnss.2019.03.003​

Barton, A. J., Kwok, J., McDuff, E., & Kappes, C. (2024). The role of nurse-led telehealth interventions in bridging healthcare gaps and expanding access. Nursing Open, 11(1), e2092. https://doi.org/10.1002/nop2.2092​

Jeffs, E., Pont, A., Harbour, J., & Dale, J. (2021). Effects of computerised clinical decision support systems on nursing and allied health professional performance and patient outcomes: A systematic review. BMJ Open, 11(12), e053886. https://doi.org/10.1136/bmjopen-2021-053886​

Zhang, Y., Ren, W., & Fang, J. (2019). Big data and its application in nursing: Opportunities and challenges. International Journal of Nursing Sciences, 6(3), 367–373. https://doi.org/10.1016/j.ijnss.2019.06.004