UC Irvine

Background: Heart failure (HF) readmissions remain a significant healthcare challenge, often driven by medication nonadherence. A needs assessment in a transitional care clinic identified diuretic nonadherence as a key contributor to 30-day HF readmissions. The Teach-Back Method (TBM), a structured communication strategy, has been shown to improve patient understanding and adherence. Purposes/Aims: This DNP quality improvement project aimed to increase provider utilization of TBM through structured training and electronic medical record (EMR) integration to support diuretic adherence and reduce 30‑day HF readmissions. Methods: A pre–post intervention quality improvement project was implemented in a heart failure transitional care clinic in Southern California using the Johns Hopkins Evidence-Based Practice model. Providers completed a 30-minute TBM training, and TBM and diuretic adherence SmartPhrases were integrated into the EMR. Provider confidence was assessed pre-intervention, immediately post-intervention, and at 30 days. TBM adherence was evaluated through chart audit and provider self-report due to EMR data limitations. Diuretic adherence was tracked using EMR-documented SmartPhrase data over two months, and 30-day HF readmissions were obtained from regional reports. Data were analyzed using descriptive statistics and chi-square testing. Findings: Provider confidence increased from 9.5 pre-intervention to 9.8 immediately post-intervention and 10 at one month. A 26.9% chart audit showed 70% TBM adherence, consistent with provider self-report. Diuretic adherence remained low (21.2% and 20.6%) and was limited by inconsistent documentation. Thirty-day HF readmission decreased slightly from 22% to 20.3%, but this change was not statistically significant (χ² = 0.17, p = 0.68). Conclusions: Structured TBM training with EMR integration improved provider confidence but did not meaningfully change patient outcomes. Findings highlight the need for consistent implementation, reliable documentation, and system-level support. Future efforts should optimize EMR workflows, improve data capture, and evaluate long-term impact.