Patient-Centric Blockchain-Enabled Framework for Secure Medical Data Sharing in Indian EHR Systems

Electronic health records (EHRs) are integral to modern hospital systems, especially in India's rapidly digitizing healthcare landscape. EHRs connect hospital records through shared networks, enabling quick and accurate data sharing. However, these systems are targeted by attackers as they store large volumes of sensitive patient information. Thus, this research proposed a secure EHR framework named the sequential belief neural network (SeqBNNet). The SeqBNNet integrates deep belief networks (DBN) with sequential convolutional neural networks (SCNN) to encrypt EHRs. The images are split into N-shares, stored in the InterPlanetary File System (IPFS), and indexed using blockchain hashes for secure access. Data retrieval involves blockchain verification and decryption. The proposed model demonstrated strong performance with a value of 52.134 dB peak signal-to-noise ratio, 0.066 MSE, 0.518 s encryption time, 0.436 s decryption time, 0.576 s latency, and 97.754 Mbps throughput. This architecture offers a secure and scalable solution for managing patient data in Indian hospitals.