Connected Blockchain Federations for Sharing Electronic Health Records

With the growing utility of blockchain technology, the desire for reciprocal interactions among different blockchains is growing. However, most operational blockchain networks currently operate in a standalone setting. This fragmentation in the form of isolated blockchains creates interoperability difficulties, inhibiting the adoption of blockchains in various ecosystems. Interoperability is a key factor in the healthcare domain for sharing EHRs of patients registered in independent blockchain networks. Each blockchain network could have its own rules and regulations, obstructing the exchange of EHRs for improving diagnosis and treatments. Examples include patients being treated by healthcare providers in different countries or regions, or within one country but with a different set of rules per state or emirate. By contrast, a federation of blockchain networks can provide better communication and service to stakeholders in healthcare. Thus, solutions for facilitating inter-blockchain communication in such a blockchain federation are needed. However, this possibility has not been fully explored, and further investigations are still being conducted. Hence, the present study proposes a transaction-based smart contract triggering system for inter-blockchain communication, enabling EHR sharing among independent blockchains. We use local and global smart contracts that will be executed once a transaction is created in the blockchain. Local smart contracts are used for EHR sharing within the blockchain, whereas global smart contracts are used for EHR sharing among independent blockchains. The experimental setup is conducted using the Hyperledger Fabric blockchain platform. Inter-blockchain communication between two independent fabric networks is conducted through a global smart contract using Hyperledger Cactus for EHR sharing in a health federation setup. To the best of our knowledge, our study is the first to implement an inter-blockchain communication model in the healthcare domain.