Quality improvement project to improve immediate postpartum family planning utilisation at Dilla University Teaching Hospital.

چکیده اصلی

BACKGROUND: Immediate postpartum family planning (IPPFP) is a proven intervention to reduce unintended and closely spaced pregnancies, yet its utilisation remains low in Ethiopia. At Dilla University Teaching Hospital (DUTH), only 10.2% of postpartum mothers received contraception before discharge. This quality improvement (QI) project was implemented to address gaps in counselling, provider readiness and interdepartmental linkages affecting IPPFP uptake. METHODS: A QI initiative guided by the Model for Improvement and Plan-Do-Study-Act (PDSA) cycles was conducted from 8 July 2025 G.C. to 1 October 2025 G.C. Five sequential PDSA cycles were implemented, including staff orientation, assignment of a focal person, integration of IPPFP indicators into the hospital's daily system bottleneck focused reform (SBFR) monitoring dashboard, strengthening maternal, newborn and child health unit linkages and continuous monitoring with weekly feedback. Data were analysed using run charts and descriptive statistics following SQUIRE 2.0 guidelines. RESULTS: During the baseline period, 10.2% of eligible postpartum mothers received IPPFP. Major barriers included inadequate counselling, limited provider training and poor documentation systems. After implementing the QI interventions, IPPFP uptake increased to 45.8% among 786 postpartum mothers over 14 weeks. Implants were the most commonly chosen method (54.2%), followed by intrauterine devices (4.4%) and bilateral tubal ligation (1.1%). Although run charts demonstrated intermittent weekly improvements, the data did not meet criteria for a sustained performance shift, reflecting variable adherence to newly introduced processes. CONCLUSION: Implementation of the QI intervention was associated with increased IPPFP uptake at DUTH and may have been supported by strengthened counselling, improved accountability, enhanced provider readiness and better data-driven decision making.

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