The recent trend of using cloud solutions to support electronic health records (EHRs) poses some serious questions about the confidentiality, integrity and compliance with the regulations. Classical encryption methods secure data at rest but introduce vulnerability at computation and are therefore not suitable in sensitive healthcare applications. This paper suggests a homomorphic encryption (HE)-based scheme through which medical records may be shared and analyzed safely on encrypted information. The system combines HE with blockchain to provide an end-to-end privacy, immutability, and auditability of patient records. BFV is used in structured features to work with the exact arithmetic, approximate real-number analytics with CKKS, and lightweight sums with Paillier. Functional correctness is proven with low error margins through experimental validation and manageable overhead is proven by performance evaluation in terms of encryption latency, query time, and storage. Compared to AES-at-rest and trusted execution environments, the benchmarking reveals that it does a better job of protecting privacy at a higher computational cost. Scalability analysis also further confirms applicability to large scale healthcare data. The framework is consistent with GDPR, HIPAA, and India DPDP Act, which provides a regulation-friendly framework to achieve secure sharing of EHRs.