Quality Improvement in Congenital Heart Surgery Requires Data: Is This Asking too Much in Low- and Middle-Income Countries?

Abstract

Quality improvement (QI) in congenital heart surgery depends mainly on the availability of reliable, organized, and usable data. While high-income countries benefit from mature national registries and well-established benchmarking systems, low- and middle-income countries (LMICs), which carry the largest global burden of congenital heart disease, continue to face significant challenges in data collection. This narrative review examines the current evolving data landscape for congenital heart surgery in LMICs, explores the relationship between existence of data systems and QI, identifies persistent barriers to data collection, and proposes context-appropriate strategies for sustainable progress. Evidence from published outcome studies, national and regional registry reports, and some major international databases demonstrates encouraging growth in progressive maturation of data systems across LMICs. Importantly, successful models illustrate that even incremental data collection, when focused on high-impact indicators such as mortality, major morbidity, and hospital length of stay, can generate meaningful improvements when linked with local clinical leadership, simple audit mechanisms, and collaborative learning networks. Although comprehensive, high-fidelity data collection may be unrealistic in many resource-constrained environments, the pursuit of perfection should not impede progress. Stepwise, local data strategies, supported by international collaboration, capacity building, and context-sensitive implementation, offer a realistic and transformative pathway toward safer, more equitable congenital cardiac care in LMICs.