Reducing neonatal infection rate among the neonates born at Remera Rukoma hospital: a report of quality improvement project

Abstract

Background Neonatal infection is one of the leading causes of neonatal mortality globally, significantly higher mortality rates are observed in Lower and middle-income countries, where Rwanda is located. Remera Rukoma District Hospital reported an increasing rate of neonatal infection necessitating assessment of the contributing causes and urgent intervention. Methods The project was conducted from January to June 2024. The Fishbone diagram was adopted to conduct a comprehensive root cause analysis. A multidisciplinary team of a quality improvement officer, clinicians, and different stakeholders identified contributing factors across multiple domains were categorized into healthcare practices, equipment, hospital environment, and patient factors. Interventions were implemented and monitored using monthly Plan-Do-Study-Act (PDSA) cycles to track progress and make necessary adjustments. Results There was a downward trend from a high rate of neonatal infection of 22.9% in January to 13% in June 2024. Despite not meeting the ambitious target of 10%, the results indicated a significant improvement. The systematic approach, involving targeted training of all hospital personnel, and fostering infection control measures, contributed to this progress. Conclusion The project demonstrated that a structured, data-driven approach to quality improvement can reduce neonatal infection rates. Key lessons include the effectiveness of comprehensive root cause analysis and the importance of continuous staff engagement and education. While the initial target was not met, the reduction achieved lays a foundation for continued efforts to improve neonatal outcomes at Remera Rukoma Hospital. Sustained commitment and further refinement of strategies are essential for ongoing progress in infection control and overall patient care. Lessons learned include the value of staff engagement, continuous education, and data-driven decision-making in infection control efforts.