Diagnosis-Driven, Cross-Disciplinary QA System for Coronary Artery Disease - Study Protocol.

Background PCI (Percutaneous Coronary Intervention) and CABG (Coronary Artery Bypass Grafting) are invasive treatment options for CAD (coronary artery disease), aiming to improve quality of life and reduce cardiovascular morbidity and mortality. Guidelines-based revascularization decisions should consider anatomical complexity, comorbidities, and patient preferences, with procedural risk assessed through validated scoring systems. However, the current legal quality assurance (QA) programs in Germany remain procedure specific and therefore lack a patient-centered, diagnosis-oriented approach. This study proposes a paradigm shift toward diagnosis-based QA to optimize individualized treatment selection, improve outcome attribution, and ensure transparent quality assessment. By integrating guideline recommendations with enhanced data linkage, this framework aims to standardize and improve CAD care quality while addressing limitations of existing QA-schemes. Methods This mixed-methods study aims to develop a cross-disciplinary QA-framework for CAD patients undergoing elective PCI or CABG. Qualitative methods will be employed to formulate preliminary evidence-based Quality Indicators (QI), while secondary data analyses will provide empirical support for QI prioritization, modelling and future evaluation. Findings from both approaches will undergo a structured consensus process to establish validated QI as basis of a redesigned QA-scheme. Results The resulting framework seeks to standardize and improve QA-procedures across CAD care pathways, integrating clinical expertise with real-world data to enhance patient outcome. Conclusion The study proposes a patient-centered, diagnosis-based quality assurance framework for coronary artery disease care, aiming to improve treatment decisions and outcomes. By integrating guideline, expert input, and real-world data, it seeks to enhance transparency and standardization in quality assessment across CAD treatment pathways.