Abstract
The Secure Health Framework is a progressive temporal blockchain approach that addresses the aforementioned problems (SeFra). Specifically, SeFra’s foundational components, the Context-based Merkle Tree (CBMT) and Enhancement of access control, confidentiality, and integrity checking procedures is the goal of context-based access control (CBAC). Additionally, the framework allows for Interoperability and handles scalability issues with the aid of temporal properties, HL7 specifications, and Interplanetary File System for data management (IPFS). The results of the study demonstrate that the proposed framework SeFra’s performance in securely managing the Personalised Micro Ledger (PML) is affected by the temporal shadow concepts and context-based properties. Additionally, it helps record health records as text with temporal properties and share them with colleagues according to the rules set out in a context-aware smart contract. Importantly, the framework may be able to solve the problems of data silos and supply unchangeable, secure transactions in the healthcare sector. Electronic health records (EHR) have been linked to medical inconsistencies and worries about privacy and security. Although it is true that lack of standardisation is not the only difficulty in electronic health records (EHR), it is arguably the most significant. To solve the problems of interoperability and privacy, blockchain-based decentralised online ledgers have already been proposed and implemented. Nevertheless, there are still a number of other obstacles to overcome. Main technical obstacles include scalability, usability, and accessibility. The first technical difficulty is the need to keep medical records in their various formats off-chain while providing secure access control to them on-chain. The second difficulty is providing an adequate description of who owns what data and how that data is shared during times of medical emergency. Accessibility remains a problem, however, as formal verification of smart contracts takes a long time relative to processing large batches of transactions.
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