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Patient access performance—scheduling efficiency, referral completion, wait times, and appointment attendance—varies widely across healthcare organizations. These organizations rarely learn from each other because operational data are sensitive, locally governed, and often competitively protected. Isolated access analytics limit the discovery of generalizable patterns and prevent hospitals from learning from peer institutions with different patient populations and workflows. No current operational platform fully enables multi-hospital learning about patient access without exposing patient-level scheduling, referral, and attendance data. This article proposes a privacy-preserving AI platform that uses federated learning and secure aggregation to support cross-hospital modeling of scheduling demand, referral completion, appointment lead time, and no-show risk. Raw data remain within each participating hospital, while only protected model updates or aggregate statistics contribute to shared learning. The platform consists of local data adapters, standardized access-feature pipelines, a federated model trainer, a secure aggregation layer, differential privacy controls, and local operational dashboards. Each hospital receives a shared model that can be adapted locally while preserving institutional data control. The framework could enable hospitals to benefit from broader operational learning while maintaining confidentiality, competitive neutrality, and governance accountability. It would be expected to support more consistent access analytics across heterogeneous health systems without requiring centralized pooling of sensitive records. Privacy-preserving AI could support a new collaborative analytics paradigm for patient access and healthcare operations. Such platforms should be evaluated through multi-institutional pilots that assess technical feasibility, governance readiness, privacy protection, and operational usefulness.