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Project and design
This DNP project evaluates a nurse-led teach-back discharge education bundle for adults hospitalized with heart failure. It uses a pre/post design: patients discharged in the six months before implementation are compared with those discharged in the six months after.
Measures and planned tests
| Measure | Data | Planned test |
|---|---|---|
| 30-day all-cause readmission (yes/no) | Nominal; independent pre and post cohorts | Chi-square test of independence; Fisher's exact test if any expected count is below 5 |
| Heart failure knowledge score before and after teaching | Interval; the same patients measured twice | Paired-samples t-test; Wilcoxon signed-rank test if the differences aren't normally distributed |
| Length of stay in days | Ratio, usually skewed; independent cohorts | Mann–Whitney U test |
| Weekly teach-back completion | A proportion tracked over time | Run chart |
Assumptions and decisions
The alpha level is .05, two-tailed. Normality of the paired differences is checked with the Shapiro–Wilk test and a Q–Q plot. Results are reported with effect sizes and 95% confidence intervals: an odds ratio for readmission and Cohen's d for knowledge scores (Field, 2018). Because the pre and post cohorts may differ, their age, ejection fraction category and comorbidities are compared at baseline; if they differ, logistic regression adjusts the readmission comparison.
Sample size
An a priori power analysis in G*Power for the paired t-test, assuming a medium effect (d = 0.5), an alpha of .05 and power of .80, requires 34 participants (Faul et al., 2007). The readmission comparison is likely underpowered for small differences, so it is reported as exploratory.
Process measures over time
Weekly teach-back completion is plotted on a run chart with the median as the center line. Signals of nonrandom change are a shift of six or more consecutive points above or below the median, a trend of five or more points all rising or all falling, too many or too few runs, and an astronomical data point (Provost & Murray, 2011).
Data management
Data are de-identified, entered in IBM SPSS Statistics and checked for out-of-range values. Missing knowledge scores are reported, and only complete pairs enter the paired analysis.
References
Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. https://doi.org/10.3758/BF03193146
Field, A. (2018). Discovering statistics using IBM SPSS Statistics (5th ed.). SAGE Publications.
Provost, L. P., & Murray, S. K. (2011). The health care data guide: Learning from data for improvement. Jossey-Bass.
