Congenital heart defects (CHD) are the most common birth defects, impacting ~1 in every 100 births. While surgical outcomes have improved over time, morbidity and mortality remain high for certain lesions, and significant variation exists across the ~120 U.S. congenital heart centers. This has heightened the scrutiny of congenital heart programs, with several national high-profile reports leading to program closure and several quality improvement initiatives to improve national outcomes. All such initiatives depend upon rigorous measures of program quality, but how best to measure and report quality remains controversial and understudied. Several features of CHD data and current analytic methods complicate quality assessments. 1) CHD is a widely heterogeneous, sparse mix of different lesions. Current approaches combine all CHD surgeries together for quality reporting and adjust for risk based only on operation type and few general factors, despite the important influence of other routinely collected variables. Machine learning approaches are better able to incorporate these high-dimensional clinical data but remain understudied, and current methods rely upon outdated regression. 2) Preliminary studies suggest wide variability in the types of CHD treated across hospitals, yet differences in hospital case-mix have not been quantified and are not used in quality reporting. Approaches to assess hospital case-mix distributions could provide an improved understanding of which hospitals treat comparable patients and which patient subsets are large enough to allow meaningful quality assessments but have not been studied. 3) Current methods support only indirect assessments of CHD program quality, prohibiting direct comparisons of interest and ignore important sources of uncertainty, leading to inaccuracies in the quality estimates. A causal inference framework could support more direct and clinically relevant quality comparisons across hospitals but has not been investigated. In response to NOT-HL-19-712, we propose to apply contemporary statistical approaches to address these challenges. Aim 1 will optimize risk- adjustment using machine learning approaches to characterize operative morbidity and mortality. Aim 2 will identify overlapping and unique CHD patient subgroups across hospitals to improve characterization of hospital case-mix and enable valid hospital comparisons. Aim 3 will develop robust hospital quality estimates that will support direct and clinically relevant quality comparisons across hospitals using causal inference approaches, focusing on areas of overlap, and including hospital random effects to account for currently ignored sources of uncertainty. Our methods will be applied within the STS Congenital Heart Surgery Database which captures clinical information >90% of U.S. congenital heart hospitals and validated in an external source, the Pediatric Cardiac Critical Care Consortium (PC4) Registry. These advancements will support nation...