Secure Data Integration Frameworks for Omni-channel Healthcare Marketing Systems

The rapid digitalisation and increasing interconnection of healthcare marketing ecosystems require secure and interoperable data-integration frameworks. Omnichannel systems, a combination of clinical, behavioural and marketing data, are becoming increasingly important to healthcare organisations as they aim to personalise contact and stay under regulatory requirements. This research is a critical synthesis of twenty-seven peer-reviewed articles (2020-2025) in IEEE, Elsevier, and Springer Nature, as well as the most popular sources in the field of machine learning, privacy-preserving machine learning (PPML) in healthcare marketing. In this review, the qualitative meta-analytic approach is used to study technological architectures, encryption, and federated-learning methods and omni-experience platform designs. The results show that blockchain-based interoperability and AI-based analytics can enhance trust, auditability, and personalization to a considerable extent, whereas federated and split-learn systems reduce privacy risks in distributed marketing data. Hybrid cloud-edge infrastructure-based omnichannel experience platforms increase the real-time decision-making and campaign flexibility, but they encounter governance and integration issues. The paper suggests a theoretical framework of the association of secure data exchange, privacy preservation, and efficiency of omnichannel engagement. The analysis focuses on the scholarship of healthcare informatics as well as strategic marketing by emphasizing the ability of secure integration technologies to maintain compliance (HIPAA / GDPR) and improve the level of patient-centric marketing.