NURS 626 Week 7 Capstone Project Part 4: Visual Presentation Example

Five Patients Is a Count, Not a Workload: A Capstone Presentation on Acuity-Based Nurse Assignments for Medical-Surgical Units

Abstract

At a composite community hospital of 220 beds, charge nurses on two thirty-bed medical-surgical floors give each nurse five patients without any structured measure of care needs, and most nurses describe the resulting assignments as unfair at least weekly. This capstone reviewed nine recent studies and proposes a six-month implementation of a validated patient acuity tool for assignments and staffing requests. The evidence indicates that workload intensity predicts outcomes better than ratios alone, that classification scores track measured nursing time, that staffing matched to acuity-based required hours is associated with fewer adverse events, and that an adapted tool can reach high compliance and improve nurses' perceived equity. The plan includes local validation, charge nurse ownership, three Plan-Do-Study-Act cycles, measures of equity, adequacy, falls, pressure injuries and agency hours, and a first-year cost near $28,000. A Christian worldview frames fair assignments as the sharing of burdens.

Slide 1: Five Patients Is a Count, Not a Workload

• Acuity-based assignments for 4 North and 5 North
• MSN Nursing Administration capstone
• Findings, research application, practice application

Speaker notes: Good evening. My capstone began with a sentence I heard from a night nurse on 4 North: "Everyone has five, but my five are not your five." That sentence is the whole problem in eleven words, and this presentation is about how to measure the difference and act on it. I will cover what the evidence found, how the research shaped the plan, and how the plan will work on the units.

Slide 2: The Problem on Our Units

• Assignments by bed number and continuity
• 71% of nurses: unfair assignments weekly
• Falls with injury above benchmark in four of six quarters

Speaker notes: On both units, the charge nurse divides thirty patients among six nurses by geography and continuity. No one measures how much care each patient needs. In our staff survey, nearly three-quarters of nurses said they had an unfair assignment at least once a week, and seven of last year's eleven exit interviews mentioned heavy or uneven workloads.

Slide 3: Finding 1, Ratios Miss the Workload

• Workload intensity may outpredict static ratios
• Perceived adequacy tracked falls on medical-surgical units
• The count is not the burden

Speaker notes: The first finding is that ratios do not capture what drives outcomes. A rapid review of 86 critical care staffing studies judged the intensity of nurses' work a likelier predictor of outcomes than any fixed ratio (Greaves et al., 2026). In a national study of 1,269 units in 217 US hospitals, nurses' perception of staffing adequacy was associated with fall rates on acute care units, while hours per patient day were not (Lake et al., 2026). Nurses sense a workload that our numbers do not record. On our units, a nurse with a fresh hip replacement, a patient withdrawing from alcohol and three discharges counts the same as a nurse with five stable patients, and our staffing reports treat those two assignments as identical.

Slide 4: Finding 2, Acuity Can Be Measured

• Classification scores track measured care time
• Tools can be theory-based and validated
• Local validation is possible

Speaker notes: The second finding is that we can measure what nurses sense. Across five hospitals, each point on a widely used classification tool corresponded to several additional minutes of measured nursing care, even after accounting for differences between wards (Zaboli et al., 2026). Another team built a tool from Orem's and Henderson's theories and validated it against care time in a prospective sample (Ren et al., 2026). Measurement is no longer the obstacle. What remains is choosing a tool that fits our patients and building the habit of using it at every shift change.

Slide 5: Finding 3, Matching Staff to Acuity Protects Patients

• Required hours calculated from acuity
• Safe coverage linked to fewer adverse events
• Nursing cost rises with acuity

Speaker notes: When hospitals in Catalonia compared staffed hours with the hours acuity required, staffing that met at least 90% of the requirement was protective against nursing-sensitive adverse events among nearly seven thousand cardiac patients, though average coverage was well below that level (Asensio-Flores et al., 2026). A separate cost analysis found that acuity and patient load drive daily nursing cost (Lee et al., 2026), so acuity data also show finance where nursing dollars go.

Slide 6: Applying the Research, Choosing the Tool

• Published validation against care time
• Scored in under two minutes per patient
• Two-day local time study before use

Speaker notes: Moving to application, the research set our selection rules. We will choose only a tool with published validation against measured care time, and only one that a charge nurse can score quickly. Before using it for assignments, we will time care for a sample of patients on each unit for two days, following the method of the multicenter validation study, to confirm that scores track our own patients' needs.

Slide 7: Applying the Research, a Model Rollout

• Adapted tool, PDSA cycles, weekly compliance
• 88% median weekly use over 24 weeks
• Better perceived workload and equity

Speaker notes: Our implementation copies a published rollout. A pediatric oncology unit adapted a validated acuity tool, introduced it through Plan-Do-Study-Act cycles and tracked weekly compliance, reaching a median of 88% over 24 weeks, after which nurses rated their workload, the fairness of their assignments and their work environment significantly better (Lim et al., 2026). The setting differs from ours, but the method transfers.

Slide 8: Practice, the Six-Month Plan

• Months 1 to 2: select, validate, educate
• Months 3 to 6: three PDSA cycles
• Charge nurses keep override authority

Speaker notes: In practice, the first two months are spent choosing and validating the tool and educating all 64 nurses. Six charge nurses from both shifts sit on the selection team. Then three cycles: day shift on 4 North, both shifts on 4 North, then both units with scores sent to staffing every shift. Charge nurses may override a tool-based assignment with a reason, because the tool informs judgment rather than replacing it.

Slide 9: Practice, How We Will Measure

• Weekly tool use, target 85%
• Equity and adequacy at 0, 3 and 6 months
• Falls, pressure injuries, agency hours

Speaker notes: Our process measure is the share of assignments made with the tool each week. Staff outcomes are perceived workload, assignment equity and staffing adequacy, surveyed three times. Patient outcomes are injurious falls and unit-acquired pressure injuries per thousand patient days. Financially, we will track agency and overtime hours and the gap between required and staffed hours.

Slide 10: Practice, Cost and Return

• About $28,000 in year one
• Roughly 190 paid hours of staff time
• Two retained nurses repay it many times over

Speaker notes: The license and electronic record build come to about $28,000, and education, the time study and inter-rater checks take about 190 paid hours. If fairer assignments keep even two nurses from leaving, the avoided replacement costs exceed the project budget several times. Acuity data will also strengthen next year's variable staffing budget, because for the first time the units will be able to show the finance office how much care their patients actually required on each shift, rather than how many beds were filled.

Slide 11: A Christian Worldview of Shared Burdens

• Burdens shared as a matter of fairness
• Bear one another's burdens
• Making the weight visible

Speaker notes: For me this project is a question of justice. When Paul asked one church to help another in need, he explained that the goal was equality, with today's plenty covering someone else's shortage (English Standard Version Bible, 2001/2016, 2 Corinthians 8:13-14), and he told the Galatians that carrying each other's loads fulfills the law of Christ (English Standard Version Bible, 2001/2016, Galatians 6:2). Burdens cannot be shared fairly if they cannot be seen. An acuity tool makes the weight of each assignment visible, so a charge nurse can share it.

Slide 12: What I Am Asking

• Approve the six-month pilot
• Release six charge nurses for the selection team
• Review results at month six

Speaker notes: I am asking unit leadership to approve the pilot, to release six charge nurses for the selection team and time study, and to review the results with me at month six. Our textbook reminds nurse administrators that staffing decisions must be justified with data (Roussel et al., 2020). This project gives our units that data, and gives our nurses assignments they can trust. I would welcome your questions now. Thank you.

References

Asensio-Flores, S., Adamuz, J., Soldevila-Cases, R., Castellà-Creus, M., Zuriguel-Perez, E., Polushkina-Merchanskaya, O., Delgado-Hito, P., & Juvé-Udina, M. E. (2026). Associations of safe nurse staffing coverage and care complexity factors with nursing-sensitive adverse events in cardiac inpatients: A cross-sectional multicenter study. Frontiers in Cardiovascular Medicine, 13, Article 1810207. https://doi.org/10.3389/fcvm.2026.1810207

English Standard Version Bible. (2016). Crossway. (Original work published 2001)

Greaves, P. J., Erfani, G., Simpson, D., Hill, B., Conner, T., Nemeckova, M., McCerery, A., Rolf, F., Monk, D., & Steven, A. (2026). Nursing in adult critical care services: Safe staffing evidence: A rapid review. Intensive and Critical Care Nursing, 98, Article 104585. https://doi.org/10.1016/j.iccn.2026.104585

Lake, E. T., Pascale, A., Warshawsky, N. E., Henderson, J. G., Smith, J. G., Staiger, D., Perez, Y., Venkataswamy, E., & Rogowski, J. A. (2026). Association of objective and subjective nurse staffing metrics with patient fall rate by unit type. Nursing Outlook, 74(3), Article 102750. https://doi.org/10.1016/j.outlook.2026.102750

Lee, S. Y., Park, C. G., Boyd, A. D., Quinn, L. T., Stogis, S. L., Davila, A., & Collins, E. G. (2026). Patient-level nursing cost analysis and cost drivers in acute care: A retrospective longitudinal study. Nursing Outlook, 74(2), Article 102684. https://doi.org/10.1016/j.outlook.2026.102684

Lim, Y. Y., Docherty, S. L., Huey Cheng, J. J. S., & Turner, B. (2026). Implementation and evaluation of a patient acuity tool in pediatric oncology. Journal of Pediatric Hematology/Oncology Nursing, 43(3), 178-188. https://doi.org/10.1177/27527530251408820

Ren, H. F., Wei, M. F., Shen, M., Lv, L., Hu, Y., Li, Y. P., Liu, C. Q., & Jiang, Y. (2026). Development of a machine learning-based patient classification system for a gastroenterology ward. Journal of Nursing Management, 2026(1), Article e2039402. https://doi.org/10.1155/jonm/2039402

Roussel, L. A., Thomas, P. L., & Harris, J. L. (2020). Management and leadership for nurse administrators (8th ed.). Jones & Bartlett Learning.

Zaboli, A., Solazzo, P., Rigger, M., Vidi, M., Ruzzante, D., Macciocu, C., Piva, M., Innerhofer, H., Ziller, M., Lioni, R., Turcato, G., & Siller, M. (2026). Association between the PERROCA patient classification system and nursing workload: A multicentre cross-sectional study. Applied Nursing Research, 90, Article 152114. https://doi.org/10.1016/j.apnr.2026.152114