From 18.1% Toward 15%: A Performance Evaluation and Eighteen-Month Monitoring and Improvement Plan for Home Health Hospitalization
[Student Name]
University of Phoenix
NSG/577: Continuous Quality Monitoring and Outcomes Improvement
Week 6 Assignment
[Instructor Name]
[Date]
The agency, its data and all figures are a composite written for a model paper.
Over five weeks, the agency identified quality markers, defined a hospitalization measure, set a target with safeguards, built control charts and examined the variables behind the rate. This paper evaluates where the agency stands and presents the plan to improve and to keep improving.
Performance Evaluation
Across 24 months, the share of patients admitted to a hospital in their first two months of care has held steady at an average of 18.1%, with every month inside the control limits. Against the regional collaborative median of 15.6%, the agency performs worse. Risk-adjusted analysis shows the rural team's observed rate exceeds its expected rate by about 2 points, while the city team does slightly better than expected. Patients who live alone and those with dual eligibility have higher rates. Process performance is mixed: timely initiation is at 88% against a target of 95%, medication reconciliation with follow-up at 91% and early concentrated visits reaching only 58% of the highest-risk patients, well short of the 85% goal.
Where to Act
The evaluation points to three areas where the agency's own data suggest a gain is possible and the agency has control. Front-loaded visits for high-risk patients were associated with lower hospitalization in Week 5 and are done for fewer than two thirds of eligible patients. After-hours response in rural areas is slow. And patients who live alone lack a daily check between visits.
Change 1: Front-Loaded Visit Scheduling
Beginning in month 1, the scheduling system will automatically propose three nurse visits during the first week for patients in the top two risk groups, using the admission risk score built on the approach of Rosati and Huang (2007). The first cycle will test this on the city team for four weeks, with a prediction that front-loading will rise above 80%. If it does, the rural team will adopt it, with a travel-time adjustment to protect caseloads.
Change 2: Rural After-Hours Response
A second on-call nurse based in the rural counties will be tested for three months, with the prediction that median time from a patient call to nurse callback will fall from 34 to under 15 minutes and that urgent home visits after hours will rise.
Change 3: Daily Check-Ins for Patients Living Alone
For patients who live alone and are in the top three risk groups, an aide or nurse will call daily for the first 14 days, using a short script about symptoms, weight for heart failure patients and medication questions. The test will start with 20 patients and expand if the calls are completed at least 85% of the time.
Each change was chosen because the agency's own data point to it, and each is tested small enough that failure would cost little and teach a lot.
What the Plan Does Not Include
Some ideas were deliberately left out. Remote monitoring equipment for every rural patient was considered but deferred because of cost and because the daily call test can show whether more frequent contact helps before the agency invests in devices. A change in referral acceptance was rejected outright, since declining high-risk patients would improve the measure without improving care, as discussed in Week 3.
Resources Required
The plan requires modest resources: scheduling system configuration, estimated at 40 hours of analyst and vendor time; the second on-call nurse for three months, funded from the quality budget; and aide time for daily calls, about 15 minutes per patient per day. The board will be asked to approve these costs, with a commitment to report after three months whether each change is worth continuing.
Involving Patients and Families
Patients and families will be told at admission that they may receive early visits or daily calls and why. Their feedback will be collected in the discharge survey, with one added question about whether the calls helped. A change that patients find intrusive will be adapted.
The Improvement Method
Each change is tested with Plan-Do-Study-Act cycles. Plans state the prediction; results are compared with it; and the team decides whether to adopt, adapt or abandon. Process measures show whether each change is happening as intended within weeks; the outcome measure responds more slowly because of the 60-day window.
The Monitoring System
The p-charts from Week 4 are the core of monitoring. The lead measure and the three process measures are updated monthly, with center lines and limits recalculated only after a confirmed special-cause shift (Benneyan et al., 2003). Balancing measures from Week 3, including emergency visits without admission, observation stays, referral acceptance by risk group, the average admission risk score and length of service, appear beside the outcome. Results are stratified by team, living situation and dual eligibility, following the decision in Week 5 to keep social risk visible rather than adjusting it away (National Academies of Sciences, Engineering, and Medicine, 2016).
Responsibilities
The quality director owns the monitoring system and presents quarterly results to the board. Each team manager owns team-level process measures and leads the monthly review of charts with staff. The quality analyst maintains the data, runs the monthly quality checks and flags signals. Two nurses conduct monthly case reviews of five hospitalizations.
Timeline
Months 1 to 3: test and adopt front-loaded scheduling on both teams; begin the rural on-call test and the daily call test. Months 4 to 6: decide on each change, expand what works and begin a second round of cycles if needed. Months 7 to 12: monitor for a sustained shift on the outcome chart. Months 13 to 18: build adopted changes into standard work and evaluate against the 15.0% target.
How We Will Know
The agency will conclude that it has improved when the hospitalization chart shows a special-cause signal, such as eight consecutive months below the center line, with the balancing measures stable, particularly the admission risk score and referral acceptance. A falling rate alongside a falling risk score would suggest selection rather than improvement.
Communicating Results
Results will be shared in three ways. Field staff will see their team's charts at monthly meetings with a short discussion of what the latest cycle showed. The board will receive a quarterly one-page summary with the lead chart, balancing measures and the status of each change. Referral partners, including the two hospitals that send most patients, will receive a brief update every six months, since hospital discharge planners notice when fewer of their patients return and can reinforce the agency's work by sending complete medication lists at discharge.
Sustaining the Gain
Adopted changes will be written into scheduling rules, the plan-of-care template and on-call policy so they do not depend on individual effort. Monitoring continues after the 18 months, and a signal of deterioration triggers a review by the quality director and the relevant team manager.
Conclusion
The agency's hospitalization rate is stable at 18.1% and worse than its peers, with gaps concentrated in rural patients and those who live alone. Three tested changes target those gaps, and a monitoring system built on control charts, balancing measures and stratified reporting will show whether care has truly improved. Donabedian (1988) linked structure, process and outcome; this plan changes structure and process where the agency's evidence points and watches the outcome to learn whether the link holds.
References
Benneyan, J. C., Lloyd, R. C., & Plsek, P. E. (2003). Statistical process control as a tool for research and healthcare improvement. Quality and Safety in Health Care, 12(6), 458-464. https://doi.org/10.1136/qhc.12.6.458
Donabedian, A. (1988). The quality of care: How can it be assessed? JAMA, 260(12), 1743-1748. https://doi.org/10.1001/jama.1988.03410120089033
National Academies of Sciences, Engineering, and Medicine. (2016). Accounting for social risk factors in Medicare payment: Identifying social risk factors. The National Academies Press. https://doi.org/10.17226/21858
Rosati, R. J., & Huang, L. (2007). Development and testing of an analytic model to identify home healthcare patients at risk for a hospitalization within the first 60 days of care. Home Health Care Services Quarterly, 26(4), 21-36. https://doi.org/10.1300/J027v26n04_03
How this NSG 577 Week 6 example is structured
The NSG/577 description ends with evaluating performance and outcomes improvement. This paper brings the course together: an evaluation of current performance, a plan of targeted changes tied to the variables in Week 5, a monitoring structure using the charts from Week 4, the safeguards from Week 3 and a plan for sustaining improvement. Students search this week as NSG 577 Week 6, NSG577 Wk 6 or NSG/577 Wk 6; all three are the same assignment.
NSG/577 Week 6 questions, answered
What does NSG/577 Week 6 usually ask for?
Many sections ask students to evaluate performance on a quality measure and present a plan for continuous monitoring and improvement, often drawing on the work of earlier weeks.
What is a Plan-Do-Study-Act cycle?
A small, rapid test of a change: plan the change and prediction, carry it out, study the results against the prediction and act by adopting, adapting or abandoning it.
How do you sustain an improvement?
Build the change into standard work, such as scheduling rules and documentation templates, keep monitoring on the control chart and assign an owner to respond if performance slips.
Write yours, or have the desk draft it
This paper is an original model document written by our desk, not a submitted student paper and not an official University of Phoenix document. Read it for the moves, then write your own to the instructions in your classroom. If you want one built to your exact prompt and rubric, the first custom sample is free and arrives in 24 to 48 hours.