NURS FPX 6424 Assessment 3 Proposal to Administration: Spreadsheet and Video Presentation

NURS FPX 6424 Assessment 3 Proposal to Administration: Spreadsheet and Video Presentation

Student Name

Capella University

NURS-FPX6424

Professor Name

Submission Date

Proposal to Administration: Spreadsheet and Video Presentation

Hello, senior managers. I am ….. It is a video presentation and a spreadsheet. It is performed to provide conclusions and recommendations about the 30-day hospital readmission rate of patients with heart failure because this indicator was chosen due to its direct relation to the efficiency of care coordination. It is proven that the discharge planning gaps, the absence of follow-up, or the inadequate self-management of the patients are likely to cause the high readmission rates (Macchio et al., 2020). Combining the Iowa Model with the implementation of health informatics to track at-risk patients in the initial stages, personalization of intervention and, ultimately, reducing the readmission rate, it will be possible to identify at-risk, predisposed patients and act at the appropriate time.

In NURS FPX 6424 Assessment 4, students need to prepare a Proposal to Administration, which involves creating an Excel spreadsheet and a video presentation with respect to quality improvement data from the healthcare sector. In this assessment, students have to evaluate the chosen quality outcome, find its measurable indicators, determine its trends, and make recommendations for evidence-based improvements through the use of health informatics and Iowa Model. This guide covers all sections of the assessment along with an APA 7 template.

Analyzing What, Why, and How to Measure a Specific Quality Outcome

In order to identify the causes of readmission of heart failure patients within 30 days, we observe the degree of medication adherence, the number of patients who have obtained follow-up care, the level of patient satisfaction, and weight gain. The What encompasses readmission rate and secondary indicators, and is addressed monthly to comprehend how the situation is unfolding and the improvement that is made. This is supposed to facilitate in coordinating care, reducing the number of readmissions back to the hospital, and keeping up with the demand of CMS (Anawade et al., 2024). In How the electronic medical records are involved in the information collection, analysis of the data to detect threats, patient surveys, and logs of medicine used. In all these ways, we can completely trace the progress of the patients, and it allows us to make decisions that should have data behind them and will prove to be fruitful in the long run.

To begin with, the main outcome that will be analyzed is the 30-day readmission rate of heart failure patients. It is an indicator that is promoted by the Centers of Medicare and Medicaid Services (CMS) and aimed at less than 20 percent (Macchio et al., 2020). Enhancing this benchmark will mean that care teams have been able to close transitional care gaps. Secondary benchmarks are being used alongside readmission rates, which are medication adherence, attendances at follow-ups, patient satisfaction, and average weight gain, all of which are associated with successful chronic disease management (Baryakova et al., 2023). These standards are gauged every month so that corrections with regard to the course can be made in time. Frequent measurement is also used in order to comply with the national performance standards and facilitate the use of data in decision-making.

The data collection procedures were meticulously chosen in order to measure both clinical and behavioral readmission determinants. EHR extraction is objective clinical data, including discharge dates, readmission cases, and recorded symptoms such as weight gain or dyspnea. This data is then subjected to predictive analytics algorithms to alert potential high-risk patients with such patterns as unexpected weight gain of more than two pounds within 24 hours. Data sources on medication adherence include pharmacy refill histories and patient self-reports, and data on outpatient attendance are in the form of follow-up attendance rates. The feedback on the involvement and perceived quality of care is offered through patient satisfaction surveys that are conducted using validated instruments such as the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) (Cui et al., 2025). By gathering such data every month, one can analyze trends, promote early intervention, and comply with the focus of the Iowa Model, which is evidence-based practice and collaboration with stakeholders.

Analyzing Quality Outcome Data Trends

The analysis of the data measures and trending is done based on the interpretation of the changes in the monthly metrics as compared to the predetermined benchmarks. To illustrate, the number of readmissions has gone down from 22% in January to 16% in June, a relative reduction of 6%. This tendency is associated with the increase in medication adherence (70% to 85%) and follow-up rates (55% to 75%) during the same time period. Pham et al. (2022) showed that every ten percent rise in the share of days covered (PDC) by cardiovascular drugs was linked to a six percent decrease in the number of hospital admissions. As well, there was an increase in patient satisfaction, which increased by 65% to 78% indicating the enhancement of engagement and perceived quality of care. The average weight gain went down to 3.5 pounds instead of 5.2 pounds in six months, which is one of the first warning signs of fluid in heart failure.

These processes could be carried out by nurses and pharmacists, who could identify red flags of weight gain, nonadherence, and missed appointments, prompting modification of diuretics, reinforcing education, and scheduling telephonic check-ins. Sutton et al. (2020) demonstrated that the integration of clinical decision support in EHRs simplified the workflow, leading to a decrease in fragmented care. In fact, the service line experienced a 25% increase in Patient Satisfaction following the implementation of an EHR-driven notification system in March and an 18 percent reduction in readmissions by June. These mutually supporting improvements offer solid evidence that interventions based on informatics, in combination with the Iowa Model, are yielding significant quality improvements.

Considering the details of the spreadsheet, every row represents a calendar month, which can be January through June, and columns can store readmission rate (percent), medication adherence rate (percent), follow-up rate (percent), patient satisfaction (percent), and average weight gain (lbs.) (see Table 1). It is possible to note that the readmission rate went down as the adherence rates increased, reaching 85% in June after steadily growing since the beginning of the year, when it was at 70%. This orientation highlights the cause and the effect dependent relationship between patient behavior and clinical outcomes.

Table 1: Healthcare Outcome Trends by Month

The bar chart with the horizontal grouping of the numbers is a translation of the exact numbers into a graphic story. On the y-axis, each month of the year (January through June) has five colored bars placed beside each other, indicating each of the metrics (average weight gain, follow-up rate, patient satisfaction, medication adherence, and readmission rate) and stretching over an x-axis of 0 to 90. In

 such a layout, we can instantly compare the results of average weight gain (the shortest bars) in a given month against the follow-up rate, patient satisfaction, medication adherence, and readmission rate (longer bars over to the right) (see Figure 1). The progressive extension of the majority of the bars in the six months depicts a gradual advancement, patient contentment, and medication compliance increases most rapidly throughout the month of March, whereas the readmission rate bars diminish in length each month, which visually depicts the declining readmissions. Although the spreadsheet provides accurate figures that can be thoroughly analyzed, the bar graph gives a concise overview of these five indicators, which makes it easy to see both the positive tendencies (e.g., the growth in adherence and satisfaction) and the ensuing decline in the readmission rates.

Figure 1: Healthcare Outcome Trends by Month

Conclusions and Recommendations

On the findings, some recommendations are outlined. First, increase predictive analytics to include more data streams, including social determinants of health, in order to further refine risk stratification models. Second, improve the program of patient education in the EHR in order to automatically produce individual discharge instructions based on the risk profile of a patient. Third, introduce multidisciplinary care huddles meeting twice a week to discuss flagged high-risk patients, and to hold them accountable for the follow-up and resource allocation. Fourth, take advantage of telehealth systems to offer virtual weight tracking and medication advisory, especially for patients in remote or underserved regions (Anawade et al., 2024). Lastly, institutionalize quarterly review in order to compare with the regional and national standards, through a balanced scorecard methodology that encompasses clinical, financial, and patient-reported outcomes. All these recommendations are consistent with the integrated care model provided by CMS, which confirms that interventions based on data and collaboration are the long-term sustainability of quality improvement.

Data Collection Methods Rationale: The suggested methods of collecting information play a crucial role in finding out factors that cause people with heart failure to be readmitted to the hospital. EHR extraction assists in the provision of credible real-time information regarding the time patients are discharged and whether they are admitted, which can be utilized to monitor their overall results (Brown et al., 2022). With predictive analytics, it can be possible to identify high-risk patients early on because of examining their clinical data, including an observable weight gain, which can be an indicator of fluid retention in patients with heart failure. Patient self-report and pharmacy refill history can be used to identify the degree to which patients are adherent to their medication, which is a significant factor in preventing readmission to the hospital. Moreover, patient satisfaction surveys can be used to continuously quantify the quality of care and the level of engagement, and hence, we can make improvements in care promptly when they are required.

Related assessments for this class:

NURS FPX 6424 Assessment 1
NURS FPX 6424 Assessment 2
NURS FPX 6424 Assessment 4