Reducing Heart Failure Readmissions in Urban Safety-Net Hospitals Through Data-Driven Collaboration
With heart failure (HF) readmissions projected to remain a major burden on the healthcare system, we formed an interdisciplinary team to address clinical and non-clinical factors driving rehospitalizations. By employing root cause analysis, run charts, and process control charts, we identified social determinants of health (SDOH) as critical barriers to optimal HF outcomes. Through iterative PDSA cycles, we optimized interventions, reduced 30-day readmission variability, and improved patient adherence. Key lessons included coordinating specialty care, tailoring educational strategies, and engaging all stakeholders in data-driven discussions. This approach is scalable for other complex, high-risk patient populations, especially in resource-constrained, safety-net settings.
Problem: Heart failure poses a significant clinical and financial burden, with readmissions affecting up to half of patients within six months. Contributing factors include medication non-adherence, gaps in education, and unaddressed social determinants of health. A multidisciplinary approach is needed to reduce costs, improve outcomes, and enhance patient quality of life.
Measurement: We employed multiple process analysis and visualization tools impact-effort matrices, run charts, control charts, flowcharts, bar charts, and gap analyses to guide improvements. Key measures included readmissions, mortality, follow-up attendance, behavioral health referrals, and patient-reported barriers. We also assessed SDOH prevalence through case reviews and interviews to inform ongoing interventions.
Analysis: We conducted root cause analyses of 30-day readmissions to identify key factors driving premature returns. Time-series analyses tracked outcomes alongside intervention start dates, enabling group discussions to explore correlations and refine improvement strategies over time.
Implementation: We formed a cross-functional team to coordinate specialty care, share comorbidity data, and improve outcomes. Obstacles included time constraints, data complexity, and stakeholder consensus, addressed through targeted meetings and context-specific engagement. This model is replicable and scalable for complex patients, especially in safety-net settings.
Results/Discussion:We observed decreased variability in 30-day readmissions. Run charts illuminated trends, enabling data-driven discussions and optimized interventions. Sustained results emerged through iterative PDSA cycles and continuous performance monitoring. Future improvements will focus on high-risk patients and refining care strategies to maintain stability and further reduce readmission variability.
Speakers
Quality Informatics Manager - Jefferson Einstein Philadelphia Hospital
Improvement Advisor - Jefferson Einstein Philadelphia Hospital
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Population Health and Care Transitions
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Location Virtual
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Organizer NAHQ Next
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