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Hospital Readmissions: Preventive Project Results

The prevention of hospital readmissions among patients with chronic or congestive heart failure (CHF) was chosen as the focus of the course project. According to the process thoroughly described in the third phase, the effectiveness of post-discharge patient visits and telephone support was measured. This essay is aimed at describing the sample, presenting the research results, and discussing potential limitations to be addressed within the frame of future studies.

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Demographic Characteristics of the Sample

Out of 54 potential participants who were hospitalized due to CHF, 42 people, or 77.78% met all of the inclusion criteria and signed the participation agreement. The reasons for exclusion were presented by participants’ being outside the required year range in 25% of cases. Also, 58.3% of exclusions were due to the presence of additional diagnoses that required close collaboration with healthcare teams. Finally, 16.6% of exclusions were related to patients’ inability to communicate with English-speaking specialists without their relatives’ help.

Two groups of patients, each of which included 21 people older than 45 and hospitalized with CHF, were formed with the help of random sampling techniques. Before the stage of group formation, all participants included in the sample approved the use of their demographic and health data and agreed to share some additional details, such as their financial position. When it comes to age, the following groups can be singled out:

  • 45-50 years old – 28.57%;
  • 51-55 years old – 16.6%;
  • 55-60 years old – 45.24%;
  • 61-65 years old – 9.52%.

In addition to diversity in terms of age, it is critical to describe the sample concerning the degrees of ethnic and racial heterogeneity. Modern researchers note that racial minority groups are often underrepresented in experimental studies, which can potentially lead to the exacerbation of health disparities (Goosby, Malone, Richardson, Cheadle, & Williams, 2015). In the discussed study, the racial composition of the sample does not run counter to the general demographic trends for different racial groups in the region. The sample characteristics by race are presented below:

  • Caucasians – 57.14%;
  • African Americans – 21.43%;
  • Hispanic and Latin Americans – 16.67%;
  • People of mixed-race origin – 4.76%.

Participants’ sex also presents an important characteristic when it comes to studies focused on CHF. As Aimo et al. (2018) claim, despite the presence of sex-based differences in people’s responses to surgical/pharmacological treatment, patient prognosis, and complication rates, women continue to be underrepresented in CHF clinical trials. Taking the problem into account, the researchers focused on achieving a balanced representation between the sexes. It was possible to form two groups with appropriate male-to-female rates – 1.1 and 1.3 in the intervention and control groups, respectively.

Statistical Tests and Descriptive Data Points

As it has been mentioned in the implementation phase document, apart from popular online databases for nursing and medical professionals, data analysis and interpretation should involve the use of the software. SPSS version 25 was utilized to conduct statistical tests and draw comparisons between the readmission rates in the control and intervention groups. Information concerning hospital readmissions was collected and generalized concerning two points in time: 30 days and 90 days from the date of discharge.

The results of statistical tests for independent samples indicated that both 30- and 90-days mean hospital readmission rates in the two groups were significantly different with the P-value being 0.05. For instance, the 30-day rates of readmission to the hospital were 14.29% in the control group and only 4.76% in the intervention group. Thus, there is more than a three-time difference between the groups, which speaks in favor of the intervention’s effectiveness. When it comes to the potential long-term effects of the implemented program, the 90-days readmission rates in the studied groups also varied significantly. For the control group, the rate was almost 24%, whereas, in the intervention group, it did not exceed 15% of the research participants.

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A few descriptive data points were utilized to outline potential tendencies and make generalizations on the intervention’s effectiveness for different demographic subgroups. They included, for instance, the number of previous hospitalizations with CHF, the history of drug or alcohol abuse, and patients’ ethnic origin. In both intervention and control groups, almost 65% of people who were readmitted to the hospital had at least two hospitalizations with CHF earlier in life.

Importantly, the findings peculiar to race and addictive behaviors in the past are reflective of the risk factors for CHF readmissions described by Chamberlain, Sond, Mahendraraj, Lau, and Siracuse (2018). Thus, 66% of people in the intervention group readmitted within 90 days after discharge had a history of addictions and belonged to racial minorities.


The conducted study is associated with several limitations that are to be considered in future research. To begin with, from considerations of credibility and applicability to different medical contexts, the sample size will need to be increased to provide more accurate conclusions. More than that, additional measures will need to be taken to reduce the influence of external variables on results. Such factors may include patients’ access to information sources on CHF, adherence to recommendations, having health professionals among relatives/friends, or obesity status.


Finally, as the results of the study indicate, the use of post-discharge home visits and structured telephone conversations is associated with decreases in rehospitalization rates in CHF patients. However, before implementing the intervention into practice, it is pivotal to test it using larger and more diverse samples. As for additional limitations, the impact of patients’ behaviors and willingness to learn on the results will need to be studied.


Aimo, A., Vergaro, G., Barison, A., Maffei, S., Borrelli, C., Morrone, D.,… Savino, K. (2018). Sex-related differences in chronic heart failure. International Journal of Cardiology, 255, 145-151.

Chamberlain, R. S., Sond, J., Mahendraraj, K., Lau, C. S., & Siracuse, B. L. (2018). Determining 30-day readmission risk for heart failure patients: The readmission after heart failure scale. International Journal of General Medicine, 11, 127-141.

Goosby, B. J., Malone, S., Richardson, E. A., Cheadle, J. E., & Williams, D. T. (2015). Perceived discrimination and markers of cardiovascular risk among low-income African-American youth. American Journal of Human Biology, 27(4), 546-552.

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