| Course | NR 532 Healthcare Operational Planning and Management |
|---|---|
| Week | 2 |
| Paper type | Workload forecast |
| Length | About 1,150 words, 3 pages |
| Format | APA 7 student paper |
| School | Chamberlain University |
| Program | MSN, Nurse Executive |
| Updated | September 2026 |
Free sample paper for NR 532 Week 2
Workload Forecast: Next Year's Admissions to a 24-Bed Spine Unit, Built From Twelve Months Chosen Before the Answer Was Known and Pushed to the Worst Week Rather Than the Average One
Student Name
College of Nursing, Chamberlain University
NR 532: Healthcare Operational Planning and Management
Instructor Name
Month Day, Year
Workload Forecast: Next Year's Admissions to a 24-Bed Spine Unit, Built From Twelve Months Chosen Before the Answer Was Known and Pushed to the Worst Week Rather Than the Average One
Purpose
This forecast estimates weekly admissions and patient days for the 24-bed spine and neurosurgery unit I manage for the next fiscal year, July through June, so that the staffing grid and budget can be built on something better than last year's total. Two choices shape any forecast more than its method: which history it starts from and which week it plans for. I made both choices before running the numbers and explain them below, because a forecast whose history is chosen after the fact can be made to say almost anything.
Choosing the History
I used the twelve months from July of last year through June of this year. The obvious alternative was the most recent twelve months ending in August, which would have included a July with unusually high volume after a new spine surgeon joined the medical staff. I rejected that window before looking at its total, for a stated reason: the new surgeon's first July included a backlog of patients who had waited for his arrival, which will not repeat. Using it would have raised the forecast by about 6% and built a staffing plan on a one-time event.
The chosen year has its own distortion. The new surgeon started in October, so the first three months of the year reflect a smaller surgical staff. I kept those months but adjusted them, as described below, rather than drop them, because dropping them would leave only nine months and lose the summer pattern entirely.
What Drives Admissions Here
About 82% of our admissions are elective spine or cranial procedures, and 18% are unplanned: trauma, spinal cord compression and patients transferred from other hospitals. The two streams behave differently and are forecast separately.
Elective admissions are driven by operating room block time. Each surgeon has assigned days, and the unit's admissions follow those days with almost no lag. This means that elective demand is not random at all. It is scheduled, and the variation in it is created by the schedule. A study of intensive care demand found that day-to-day variability among scheduled surgical patients exceeded that of emergencies and was more strongly associated with diversions (McManus et al., 2003). Our unit shows the same thing on a smaller scale: our worst weeks are not the ones with trauma, but the ones in which three surgeons operate on the same two days.
Unplanned admissions average about six a week and vary modestly with the season, rising slightly in summer with trauma.
The Method
For elective admissions, I started from each surgeon's cases per block day over the last year, then applied next year's block schedule, which the operating room committee has already approved. The new surgeon's first three months were replaced by his average from the nine months he has worked, which adds about 40 cases to the year's base. I then reduced every surgeon's total by 3% to allow for vacation weeks not yet on the schedule, a figure taken from the difference between planned and actual blocks last year.
For unplanned admissions, I used the weekly average by month from last year, without adjustment, since nothing in the service has changed.
Patient days were estimated from admissions by procedure type, using last year's median length of stay: 1.4 days for single-level fusions and decompressions, 3.8 days for multilevel fusions, 4.6 days for cranial procedures and 5.1 days for unplanned admissions. Medians were used rather than means because a few long stays, such as a patient awaiting rehabilitation placement for three weeks, pull the mean up without reflecting a typical week.
The Forecast
Table 1 shows the result for an average week and for the worst week the method predicts.
Table 1
Weekly Workload Forecast, Next Fiscal Year
| Measure | Last year, actual weekly average | Next year, average week | Next year, 90th percentile week |
|---|---|---|---|
| Elective admissions | 25.1 | 27.4 | 33 |
| Unplanned admissions | 5.9 | 5.9 | 8 |
| Total admissions | 31.0 | 33.3 | 41 |
| Patient days | 133 | 142 | 171 |
| Average daily census | 19.0 | 20.3 | 24.4 |
Pushing the Forecast to Its Worst Week
An average week of 20.3 patients looks comfortable on a 24-bed unit. But the unit does not experience averages. The 90th percentile week, calculated from the spread of last year's weekly admissions around their own monthly average and applied to next year's forecast, reaches 171 patient days, an average census of 24.4. That is above our staffed capacity. Exposure to shifts staffed below target was linked to more inpatient deaths in a large study of hospital units (Needleman et al., 2011), so the plan must be built around this week rather than a typical one. In such a week, the census would exceed 24 on at least two afternoons, and patients would wait in recovery or go to other units.
The worst week is also predictable. Nine of last year's ten busiest weeks were weeks in which three surgeons had block time on Tuesday and Wednesday together. Next year's block schedule has that pattern in 14 weeks. The forecast therefore says not only that bad weeks will happen, but which weeks they will be, which is the most useful thing it can tell a manager.
What the Forecast Does Not Know
The forecast assumes the approved block schedule holds, that no surgeon leaves and that length of stay stays at last year's levels. A shift of routine single-level procedures to the ambulatory surgery center, which the hospital is discussing, would reduce elective admissions by perhaps 15% and shorten our average stay, since those are our shortest-stay patients. I have not built that in, because it has not been decided. The forecast should be rerun if it is.
What the Forecast Suggests
The most important finding is not the average, which rises only about 7%. It is that our peaks are created by the block schedule and could be reduced by it. Litvak and Fineberg (2013) argue that hospitals can reduce the artificial variability created by elective scheduling by separating elective and urgent flows and smoothing elective cases across the week. For our unit, moving one surgeon's Wednesday block to Monday in the fourteen crowded weeks would bring the 90th percentile week down to roughly our staffed capacity. I will take the forecast and that proposal to the operating room committee before building next year's staffing grid, because a grid built for the current peaks would pay for staff to stand idle on Mondays and Fridays.
References
Litvak, E., & Fineberg, H. V. (2013). Smoothing the way to high quality, safety, and economy. New England Journal of Medicine, 369(17), 1581-1583. https://doi.org/10.1056/NEJMp1307699
McManus, M. L., Long, M. C., Cooper, A., Mandell, J., Berwick, D. M., Pagano, M., & Litvak, E. (2003). Variability in surgical caseload and access to intensive care services. Anesthesiology, 98(6), 1491-1496. https://doi.org/10.1097/00000542-200306000-00029
Needleman, J., Buerhaus, P., Pankratz, V. S., Leibson, C. L., Stevens, S. R., & Harris, M. (2011). Nurse staffing and inpatient hospital mortality. New England Journal of Medicine, 364(11), 1037-1045. https://doi.org/10.1056/NEJMsa1001025
Reading the NR 532 Week 2 instructions
The Week 2 assignment typically asks you to forecast patient volume or workload for your unit for a future period. Prompts ask for the historical data used, the method, assumptions, the forecast itself and its implications for staffing or resources. Choose the history before running the numbers and explain the choice, since a window selected after seeing its total can be made to say anything. Separate demand streams that behave differently, such as scheduled and unscheduled admissions. State how you convert admissions into patient days. Then push the forecast to its worst plausible week, not only the average. Say what the forecast cannot know. Cite operations or forecasting sources in APA 7, and include a table if your section expects one.
How this NR 532 Week 2 example is built
The paper begins with its purpose and the two choices that shape any forecast. A section explains the chosen year and the rejected window, with the size of the distortion avoided. Demand is split into elective and unplanned streams, supported by research showing scheduled surgical caseload can vary more than emergencies. The method section adjusts for a surgeon's partial year and unplanned vacations and uses median stays by procedure. Table 1 gives last year's actuals, next year's average week and the 90th percentile week. A section on the worst week shows it exceeds staffed capacity and names when it will occur. The paper states its assumptions and closes with a proposal to smooth the block schedule.
Where the points sit in the NR 532 Week 2 rubric
Forecast rubrics usually reward appropriate historical data, a sound and explained method, clearly stated assumptions, an accurate forecast and implications for operations. Graders favor forecasts that justify the choice of data period and separate demand streams with different drivers. Testing the forecast against peak demand, rather than stopping at the average, shows understanding of how units experience volume. Evidence on variability in surgical scheduling supports the analysis. Identifying what the forecast cannot know earns credit for critical thinking. A table with actual and projected values helps the reader check the arithmetic. Clear writing and APA 7 citations support the presentation score.
NR 532 Week 2 help: mistakes that cost points
Students often forecast from whatever year is most convenient without explaining why it was chosen or how unusual events affect it. State the choice first. Another common problem is forecasting only the average, which hides the weeks that cause shortages. Report a high percentile as well. Some forecasts treat all admissions alike when scheduled and emergency patients follow different patterns. Using mean length of stay when a few long stays distort it is another trap; medians often describe a typical week better. Avoid false precision in the final numbers. Finally, say how the forecast should change if a known decision, such as moving procedures to an outpatient center, is made later.
Get a NR 532 Week 2 example written to your instructions
Send the assignment page, the rubric and any spreadsheet your section supplies, and a Week 2 forecast is written to your own volumes and returned inside 24-48h, free the first time. One caution on what you send: internal reports your employer treats as confidential should stay with your employer. A described setting and rounded volumes are enough for us to build from.
More NR 532 papers and MSN samples
- NR 532 Week 1: Discussion on the Midnight Census
- NR 532 Week 3: Patient Flow Analysis
- NR 532 Week 4: Staffing Grid Proposal
- NR 532 Week 5: Skill Mix Rationale
- NR 532 Week 6: Capacity Analysis
- NR 532 Week 7: Productivity Report Review
- NR 532 Week 8: Operational Plan
- NR 531 Week 2: Org Chart Analysis With Hidden Lines
- NR 529 Week 2: Delegation Analysis of a Standing Job
NR 532 Week 2 questions, answered
How much history is enough?
Enough to contain at least one full cycle of whatever varies, which for most settings means a year rather than a quarter. Two years is better because it lets you separate a trend from a single unusual season. Where only a few months exist, say so and widen the range you report, since a narrow projection from thin data is the version markers treat as overconfident.
What if my volumes are still recovering from an unusual year?
Say it in the purpose section and then decide, on the record, whether that year is included or excluded. Both choices are reasonable and neither survives being made silently. If it is excluded, show the projection with and without it so the size of that choice is visible to the reader. That comparison usually earns more than the forecast itself.
Should the forecast include a target, or only what history says?
Keep them apart. A projection describes what is likely to arrive; a target describes what somebody wants. Blending them produces a document that cannot be checked against either, and the assumption marks are usually the first thing it costs. Where a target has been handed to you, state it as an input in its own line and show what would have to change for the projection to reach it.
Where can I find a free NR 532 Week 2 sample paper?
Printed above with its forecast table, the spine unit projection makes up the entire NR 532 Week 2 sample, and there is no paywall in front of it. If your unit has different demand drivers, share the history with us and a forecast built from it can be drafted.
Should the NR 532 forecast report the worst week?
Yes. A unit experiences peaks, not averages. The sample reports a 90th percentile week alongside the average and traces it to weeks in which three surgeons operate on the same midweek days.