<![CDATA[Secure, scalable, and compliant solutions are becoming a requirement for healthcare systems handling sensitive medical data. Blockchain presents unique opportunities to create transparency and trust that is decentralized, yet has inherent challenges posed by scalability, sustainability and regulation. This study presents CA-HBCA, a Cognitive and Adaptive Software Engineering Framework for intelligent healthcare blockchain applications. The novel contribution of the research is the combination of four sledging modules, such as an AIbased cognitive security layer that triggers real-time anomaly detection, an adaptive sustainability engine that optimises energyperformance, a DevSecOps-based continuous delivery pipline, and a HL7/FHIR-compliant interoperability and consent management layer. Methodologically, the FEACAN was realized with Solidity, TensorFlow, and Ethereum/Hyperledger testnets, and tested by simulating healthcare scenarios such as EHR exchange, and adversary search. We obtained 93.2% precision of anomaly detection, 17.6% reduction of energy consumption, 42 transactions per second throughput in Hyperledger, and 98.7% of success rate of HL7-FHIR transformation, etc. The framework also demonstrated 100% smart contract–based consent compliance under test cases. The results indicate that CA-HBCA can be employed for the establishment of secure, sustainable and regulation-compliant blockchains in digital health infrastructures. In the future, we will also carry out validation with clinical real data sets and investigate the scalability in a variety of healthcare settings.]]>
