NURS 626 Week 4 Capstone Project Part 2: Annotated Bibliography Example

Capstone Annotated Bibliography: From Ratios to Acuity, Using a Validated Patient Acuity Tool to Make Nurse Assignments on Medical-Surgical Units

Capstone Question and Setting

The capstone asks whether replacing ratio-only nurse assignments with a validated patient acuity tool on two 30-bed medical-surgical units at a composite 220-bed community hospital will produce fairer assignments, better staffing decisions and safer care. On both units, charge nurses currently assign five patients to each nurse by bed number and continuity, with no structured measure of how much care each patient needs. Staff complain that one nurse may carry three fresh postoperative patients and a confused patient on fall precautions while a colleague carries five stable patients awaiting discharge. The sources below were chosen to answer four questions in turn: whether workload predicts outcomes, how acuity can be measured, how a tool can be implemented, and what it will cost.

Annotated Bibliography

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

This multicenter study used an acuity-based patient classification system that converts each patient's needs into required nurse hours per patient day, then compared the hours actually staffed with the hours required. Asensio-Flores et al. (2026) studied 6,873 cardiac inpatients in three high-complexity hospitals in Catalonia during 2022. Staffing was defined as safe when it met at least 90% of required hours, yet mean coverage was only 64.3%. About 11.4% of patients had a nursing-sensitive adverse event, most often acute pulmonary edema, and safe coverage was a protective factor, while older age, impaired consciousness, chronic disease, hemodynamic instability and transmissible infection raised risk. The design was cross-sectional and limited to cardiac patients in one health system, so the protective effect cannot be read as causal. Its value for the capstone is conceptual and practical: it shows staffing measured against acuity rather than headcount, and it gives a concrete definition of coverage that the evaluation can borrow.

Biyik, M., Durmuş, E., Ersoy, P., & Aslan, M. (2026). Nursing workload and optimal staffing in medical and surgical wards: A time-motion study using a patient classification system. Journal of Nursing Management, 2026(1), Article e3918250. https://doi.org/10.1155/jonm/3918250

Biyik et al. (2026) observed 20 nurses on a chest diseases ward and a urology ward of a Turkish teaching hospital over five weeks in 2025, timing their activities on morning and evening shifts and combining the timings with a patient classification system to calculate the optimal number of nurses. Three indicators drove the calculation: the classification score, a nursing intensity coefficient and total care time. The calculated staffing matched current levels on both wards, but the drivers differed, with care intensity dominating on the chest ward and patient volume on the urology ward. The sample of nurses was small and the wards were specialty units in one hospital, which limits generalization. For the capstone, the study is useful because it shows that two units with the same staffing can carry workload for different reasons, which is exactly the argument against assigning by numbers alone. It also models how a time study can validate a tool locally before it is used for staffing decisions.

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

The Bible gives this capstone its moral frame. Paul told the Corinthians that the aim of sharing burdens was "a matter of fairness" (English Standard Version Bible, 2001/2016, 2 Corinthians 8:13-14), so that one person's abundance supplies another's need. To the Galatians the apostle wrote that believers should bear one another's burdens (English Standard Version Bible, 2001/2016, Galatians 6:2). An assignment system that loads one nurse with the heaviest patients while another carries the lightest fails that standard, even when both nurses have five patients. For a Christian administrator, distributing work by actual need is not only an efficiency measure but an act of justice toward staff and, through them, toward patients. The capstone presentation will use these passages to explain why the project matters beyond its numbers. They also guide how the change is introduced: if fairness is the goal, the charge nurses who make assignments must help design the process, so that the tool serves their judgment rather than replacing it.

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

Commissioned for England's safer staffing program, this rapid review screened four databases from 2012 to 2024 and included 86 studies on registered nurse staffing in adult intensive care. Greaves et al. (2026) found that staffing guidelines rest mostly on expert consensus, adjusted day to day by senior nurses, and that emerging evidence links higher nurse-to-patient ratios with lower mortality and infection, although heterogeneity prevented the authors from naming an optimal ratio. Importantly, they concluded that workload intensity may predict outcomes better than static ratios, and that high workloads are linked with burnout and turnover. As a rapid review, it used streamlined methods, and its setting is critical care rather than medical-surgical units. Its contribution to the capstone is the central claim that ratios alone miss the variation in workload that drives outcomes, which supports the case for an acuity tool even in a hospital that already staffs to a ratio. Its finding that senior nurses adjust staffing daily by judgment also describes the capstone units today.

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

Lake et al. (2026) linked 2024 National Database of Nursing Quality Indicators data on 1,269 adult medical-surgical, stepdown and critical care units in 217 US hospitals to fall rates, comparing objective staffing, measured as registered nurse hours per patient day, with nurses' own perception of staffing adequacy. Hours per patient day were associated with falls in critical care and stepdown units, while perceived adequacy was associated with falls in acute care and stepdown units. A single snapshot like this cannot show cause, but its national scale and unit-type comparison make it one of the strongest sources in this list. For the capstone, it matters that on medical-surgical units nurses' perception of adequacy tracked falls when hours did not. An acuity tool may help close that gap by making visible the workload nurses perceive but numbers miss, and the evaluation will measure perceived adequacy alongside hours. A short staffing adequacy question added to the end-of-shift huddle would give the units their own version of the measure the study used.

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

Because nursing costs are usually buried in the room charge, administrators rarely see what an individual patient's nursing care costs. Lee et al. (2026) used linked patient and staffing data to estimate patient-level nursing costs, separating labor demand from labor price. Mean daily nursing cost was $586.23 per patient, about 25.6% of the room charge, and patient acuity, diagnosis-related group, nurse experience and patient load all predicted cost. The study came from one health system and relied on existing data, which may miss care that is not documented. Its importance for the capstone is financial. If acuity drives nursing cost, then an acuity tool is also a cost-measurement tool, and the budget section can argue that acuity data will let the hospital show the value of nursing care and match staffing to need rather than to census alone. The study's method could also be repeated on the capstone units after a year, using their own payroll and acuity data, to show whether acuity-matched assignments changed cost per patient day.

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

This quality improvement project is the closest model for the capstone's implementation. Lim et al. (2026) adapted a validated patient acuity tool for a pediatric oncology unit and used Plan-Do-Study-Act cycles to implement it for nurse assignments. Over 24 weeks, the median weekly compliance rate was 88%, and nurses' perceived workload, perceived equity of assignments and satisfaction with the work environment all improved significantly. The pre-post design without a comparison unit means other changes during the period could explain part of the improvement, and pediatric oncology differs from adult medical-surgical care. Even so, the project shows that a tool can be adopted with high compliance and that nurses notice fairer assignments. The capstone will borrow its implementation approach, including local adaptation of a validated tool, PDSA cycles and weekly compliance tracking, and will measure perceived equity as a primary staff outcome. Its 88% compliance figure also gives the project a realistic target for the first six months.

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

Ren et al. (2026) built a patient classification system for a gastroenterology ward in a Chinese tertiary hospital, grounded in Orem's self-care theory and Henderson's human needs theory. Using a year of hospital information system data, they measured each patient's daily nursing care and trained a decision tree model, then validated the categories prospectively in 357 patients. The final system had five subcategories, with surgical patients on the day of surgery requiring between 1.66 and 4.15 nursing hours per day depending on complexity, and other patients between 0.96 and 3.42 hours. The system was built for one specialty in one hospital and would need validation elsewhere. For the capstone, the study shows how a classification tool can be grounded in nursing theory and validated against measured care time, which will guide the criteria for choosing among available tools. It also shows that postoperative day matters, which is relevant to the surgical patients on the capstone units.

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

The course textbook frames staffing as one of the nurse administrator's central resource decisions, connecting it to budgeting, quality and the retention of staff. Roussel et al. (2020) describe how staffing plans should reflect patient needs, the competencies of staff and the organization's resources, and they emphasize that nurse leaders must be able to justify staffing with data. That framing supports the capstone's premise that an acuity tool is an administrative instrument, not only a clinical one: it produces the data a manager needs to argue for staff, defend assignments and plan budgets. The text's treatment of change and staff involvement will also shape the implementation plan, since an acuity tool imposed without charge nurse involvement is likely to be bypassed. The textbook does not provide outcome data, which is why the recent studies above carry the empirical argument. Its chapters on budgeting will, however, shape the cost section of the outline, especially the explanation of how acuity data feed variable staffing budgets.

Tiase, V. L., Sward, K. A., Li, J., & Facelli, J. C. (2026). Quantitative insights from nursing workload measurement: An artificial intelligence-driven approach. Military Medicine, 191(Supplement 1), 543-548. https://doi.org/10.1093/milmed/usag168

Tiase et al. (2026) explored whether electronic health record audit logs could measure nursing workload objectively. Analyzing five years of logs from an academic medical center, they found 8,149 unique nurse users performing 1,461 distinct types of record activities, many of them viewing or searching for data, and they used temporal mining to identify high-intensity periods and test predictive models of workload and burnout risk. The work is exploratory, set in one center and focused on record activity rather than bedside care, so it cannot yet replace an acuity tool. For the capstone, its value lies in the future direction it suggests. The evaluation plan will note that audit log data could later be compared with acuity scores to check whether the tool captures workload accurately, and the presentation will mention this as a next step rather than part of the current project. It also raises an ethical caution the outline will address, since workload data drawn from records could be misused to judge individual nurses.

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

Zaboli et al. (2026) tested whether a widely used patient classification system tracks real nursing workload across different settings. In 12 wards of five hospitals, they recorded the time nurses spent on each of 472 adult inpatients over 24 hours. The median classification score was 17 and median care time 155 minutes, and each additional point was associated with 12.8 more minutes of care in simple regression and 7.45 more minutes after accounting for ward clustering. About 23.7% of patients had high workload, and the score independently predicted that outcome. The cross-sectional design and single-region sample are limitations. This is the strongest validation evidence in the list and will guide tool selection in the outline: the capstone will require that any tool chosen show a similar measured relationship between its scores and nursing time, and it will plan a short local time study to confirm that relationship before full use. That study can follow the method Zaboli and colleagues used, timing care for a sample of patients over a day.