Explainable Clinician-Supervised Artificial Intelligence as an Implementation Framework for Cardiovascular-Kidney-Metabolic Population Health: Synthetic Data Validation of the CHAPERONE-CKM Framework

Abstract Background: Cardiovascular-kidney-metabolic (CKM) syndrome is an increasingly prevalent multisystem condition associated with morbidity, fragmented care, recurrent hospitalization, and rising healthcare costs. While cardiovascular risk models estimate future disease risk, fewer frameworks support multidisciplinary CKM care, clinician decision-making, and population health management. Synthetic data environments can assess implementation readiness while preserving privacy. Methods: We validated the explainable, clinician-supervised CHAPERONE-CKM framework using a reproducible synthetic cohort of 10,090 simulated patients with 128 demographic, laboratory, imaging, treatment, and healthcare utilization variables across the CKM continuum. Synthetic data generation was separated from framework evaluation through probabilistic modeling and independent validation to reduce deterministic relationships. The framework generated CKM stage assignments, implementation priorities, clinician-readable rationales, multidisciplinary referral pathways, and guideline-directed therapy prompts. Evaluation focused on implementation readiness, consistency, calibration, subgroup stability, fairness, workflow simulation, and explainability. Results: The synthetic population represented CKM-related conditions including diabetes (52%), hypertension (65%), chronic kidney disease (20%), heart failure (32%), and prior CKM hospitalization (27%). The framework showed stable internal behavior across demographic and clinical subgroups, favorable calibration, and biologically plausible prioritization of advanced CKM disease. Workflow simulations suggested earlier identification of patients suitable for multidisciplinary review, therapy optimization, and coordinated care compared with reactive workflows. Traditional performance metrics supported framework behavior but were treated as secondary evidence rather than proof of clinical effectiveness. Conclusions: In a synthetic validation environment, the CHAPERONE-CKM framework demonstrated implementation readiness, transparent decision pathways, and compatibility with multidisciplinary CKM population health management. These findings are an early translational milestone, not clinical validation, and support external validation, prospective implementation studies, and Learning Health System integration to assess effects on care delivery, equity, and value-based outcomes.