| Course | NURS 6051 Transforming Nursing and Healthcare Through Technology |
|---|---|
| Week | Week 3 |
| Paper type | Infographic with narrative |
| Length | About 890 words, 6 pages |
| Format | APA 7 student paper |
| School | Walden University |
| Program | Master of Science in Nursing (MSN) |
| Updated | October 2026 |
Free sample paper for NURS 6051 Week 3
Big Data in One Emergency Department: An Infographic on Staffing by the Hour and the Risks That Travel With the Data
Student Name
College of Nursing, Walden University
NURS 6051: Transforming Nursing and Healthcare Through Technology
Instructor Name
Month Day, Year
Big Data in One Emergency Department: An Infographic on Staffing by the Hour and the Risks That Travel With the Data
The Infographic (text of the single-page visual)
Top band: What big data looks like in our department
Headline: Our ED already produces big data. Three icons, each with a label. Volume: every triage entry, order, and result for about 58,000 visits a year. Velocity: tracking-board timestamps updated minute by minute, from arrival to triage, room, provider, and disposition. Variety: structured fields, free-text triage notes, and continuous monitor feeds. Footer line: Big data is defined by volume, velocity, and variety (Raghupathi & Raghupathi, 2014).
Middle band: One reward, one risk
Left panel, labeled Reward: a simple line chart of average arrivals by hour of day, drawn from three years of tracking-board data, rising from a low around 5 a.m. to a peak in late morning and a second plateau in early evening. Caption: Staff to the curve. Several years of arrival data can forecast volume by hour and day, so nurse schedules match when patients actually come (Jones et al., 2008).
Right panel, labeled Risk: two short lines with icons. Gaps: missing or late timestamps and inconsistent triage entries make the data look cleaner than the department is. Exposure: every system the data passes through, dashboards, exports, vendor analytics, is another place where patient information can leak or be reidentified (Price & Cohen, 2019).
Bottom band: One mitigation nurses can lead
Headline: Check the data before trusting the dashboard. Three steps shown as numbered boxes: 1. A charge nurse and the data analyst review one month of timestamps for missing and impossible values. 2. Staff correct the workflow that causes the gaps, such as rooming patients before triage is documented. 3. Only then is the forecast used for scheduling, with access limited to those who need it. Footer: short-form citations for all sources.
Narrative
Big data in the department
Big data is usually described by three properties: volume, the sheer amount of data; velocity, the speed at which it is generated and must be processed; and variety, the range of formats from structured fields to free text and signals (Raghupathi & Raghupathi, 2014). An emergency department illustrates all three without any special technology. Each visit generates dozens of structured entries, timestamps accumulate continuously on the tracking board, and monitors stream physiological data. Most of this is collected for immediate clinical and operational use and then left unused. The infographic's first band makes the point that big data is not only something large research centers have; this department already has it.
The reward: staffing to the arrival curve
The most direct benefit for nursing is forecasting. Emergency department arrivals follow predictable patterns by hour of day, day of the week, and season, and studies have shown that models built on historical visit data can forecast daily patient volumes with useful accuracy (Jones et al., 2008). When nurse schedules are built from those patterns rather than from fixed shift templates, staffing can rise before the late-morning surge and fall in the early-morning trough. The benefit is not the chart; it is the decision it supports, which is why the infographic states the reward as an action.
The risk: data quality and exposure
The same data carry two risks that grow as they are reused. The first is quality. Timestamps may be missing or entered late, and triage documentation varies by nurse and by shift; a forecast built on flawed data can look precise while misrepresenting the department. Kahn et al. (2016) describe data quality in terms of conformance, completeness, and plausibility, and each applies here. The second risk is exposure. Every export, dashboard, and vendor analytics platform that receives the data is another point where privacy can be breached, and large datasets can sometimes be reidentified even after names are removed (Price & Cohen, 2019). Big data's value comes from linking and reusing information, and that same linking is what enlarges the risk.
The mitigation: a nurse-led data quality review
The mitigation is deliberately modest and within nursing's reach. Before any staffing forecast is trusted, a charge nurse and the department's data analyst review a month of timestamps for missing, duplicate, and impossible values, such as a patient roomed before arrival. Nurses are best placed to explain why those errors happen, because they know the workflow that produces the timestamps, and they can change that workflow. Limiting access to the forecast to those who need it addresses exposure. The review turns nurses from sources of data into stewards of it, which is the role the infographic asks them to take.
Using the Infographic
The page is designed to be posted in the charge nurse office and shared at the monthly staff meeting where schedules are discussed. Its test is practical: a nurse manager should be able to glance at it, see that the department's own data could guide staffing, see what could go wrong, and know what first step to approve. For that reason the visual uses two colors, avoids jargon beyond the three labeled terms, and keeps every claim short enough to read standing up. The prose behind it holds the detail and the full references for anyone who wants to check the claims, including the analyst who will run the review and the privacy officer who must approve any new use of the data.
References
Jones, S. S., Thomas, A., Evans, R. S., Welch, S. J., Haug, P. J., & Snow, G. L. (2008). Forecasting daily patient volumes in the emergency department. Academic Emergency Medicine, 15(2), 159-170. https://doi.org/10.1111/j.1553-2712.2007.00032.x
Kahn, M. G., Callahan, T. J., Barnard, J., Bauck, A. E., Brown, J., Davidson, B. N., Estiri, H., Goerg, C., Holve, E., Johnson, S. G., Liaw, S.-T., Hamilton-Lopez, M., Meeker, D., Ong, T. C., Ryan, P., Shang, N., Weiskopf, N. G., Weng, C., Zozus, M. N., & Schilling, L. (2016). A harmonized data quality assessment terminology and framework for the secondary use of electronic health record data. eGEMs, 4(1), 1244. https://doi.org/10.13063/2327-9214.1244
Price, W. N., II, & Cohen, I. G. (2019). Privacy in the age of medical big data. Nature Medicine, 25(1), 37-43. https://doi.org/10.1038/s41591-018-0272-7
Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: Promise and potential. Health Information Science and Systems, 2, 3. https://doi.org/10.1186/2047-2501-2-3
NURS 6051 Week 3 instructions, in plain terms
The third NURS 6051 week usually asks for an infographic on big data in a health care setting, with a narrative. You show what big data looks like, one benefit, one risk and one mitigation, then explain each with evidence in the narrative. Faculty split credit between the visual and the prose. Cite sources on the visual briefly and in full in the narrative's APA 7 references. Focusing on one setting keeps the graphic specific. One reward and one risk are enough. The infographic should be readable at a glance. NURS 6051 faculty want the visual described band by band and each claim in the narrative supported with a source. Explain how the graphic would be shared with staff. Keep the visual text short.
How this NURS 6051 Week 3 example is built
The visual is a single portrait page in three bands. The top band defines big data through the familiar volume, velocity and variety framing, using the department's own streams as the illustration: triage entries, tracking-board timestamps, monitor feeds. The middle band sets one reward against one risk. The reward is arrival patterns across several years, read to plan staffing by hour; the risk is that the same data carries documentation gaps and privacy exposure that grow with every system it touches. The bottom band offers a single mitigation, a nurse-led data quality review before any dashboard is trusted, and carries short-form citations. Behind the page sit two pages of APA prose, one paragraph per band, with full references. Colors are limited and every icon has a text label.
Reading the NURS 6051 Week 3 rubric
Graders usually split this week's credit between the visual and the prose, and the split punishes lopsided submissions from either side. A striking page with unsupported claims loses the evidence rows; a well-cited paper attached to a cluttered page loses the communication rows, which often ask directly whether the visual suits its audience. Audience fit is judged by the manager test: could the nurse manager named in the narrative act on the page in a minute? The risk panel is read closely because many submissions list risks without explaining how they arise. Citation consistency between the visual and the reference list is checked line by line, and an icon or statistic without a source is treated like an uncited sentence.
NURS 6051 Week 3 help from the desk
Infographics lose credit when the visual is striking but the narrative thin, or the reverse. Balance both. Another common gap is listing many benefits and risks. Choose one of each. Writers also forget sources on the visual. Add short citations. Avoid tiny text. Keep each band to one idea. Name a mitigation nurses can lead. Close the narrative with how staff would use the graphic. Writers also forget patient privacy when data are combined across years. Address it. Another frequent slip is a mitigation that nurses cannot lead. Choose one they can. Check that numbers on the visual match the narrative.
Get a NURS 6051 Week 3 example written to your instructions
Some classrooms aim the piece at a chief nursing officer rather than a unit manager, so name the audience along with the infographic prompt and rubric. A custom visual and its narrative come back in 24-48h, the first free, as an editable design file with the paper attached and every citation already matched.
More NURS 6051 papers and MSN samples
- NURS 6051 Week 1: Knowledge Worker Post
- NURS 6051 Week 2: Data to Wisdom Analysis
- NURS 6051 Week 4: Clinical System Review
- NURS 6051 Week 5: Regulation Fact Sheet
- NURS 6051 Week 6: Alert Override Discussion
- NURS 6051 Week 7: Standardized Terminology Argument
- NURS 6051 Week 8: Informatics Project Proposal
- NURS 6051 Week 9: Informatics Literature Review
- NURS 6051 Week 10: Patient Portal Appraisal
- NURS 6051 Week 11: Specialty Technology Reflection
- NURS 6052 Week 3: Methodology Crosswalk
- NURS 6050 Week 3: Legislation Analysis
- NURS 6053 Week 3: Healthcare Issue Analysis
- NURS 6002 Week 3: Nursing Philosophy
NURS 6051 Week 3 questions, answered
Does the emergency department setting matter?
Only as an illustration. Big data concepts travel across settings, and the department was chosen because its data streams are fast and familiar. A community health nurse might use immunization registry data; a perioperative leader, case timing records. The example's department is composite, invented to show the concepts rather than drawn from any hospital, and the desk can rebuild the panels around your setting.
Why only one reward and one risk?
A one-page visual cannot carry a balanced survey, and trying to fit five of each produces the cluttered page graders mark down. Choosing one of each forces a defensible selection, which the narrative then justifies. If the prompt wants more, such as two rewards and two risks, the bands expand while the definition-trade-off-action order stays intact.
How are sources cited on the visual?
Author and year appear in small type beneath each band, the way a figure caption would carry them, and each matches a full APA entry in the narrative's reference list. The pairing is what lets a grader verify a claim without leaving the page. Visuals that cite nothing, or cite a website's name instead of an author, tend to lose the evidence row.
Where can I find a free NURS 6051 Week 3 sample paper?
The emergency department big data infographic text is printed here with its three bands and the narrative explaining each one.
What reward does the NURS 6051 Week 3 infographic show?
Staffing nurses to the hourly arrival curve, set against the risk of poor data quality and privacy exposure.