Efficient Clinical Data Sharing Framework Based on Blockchain Technology

Karamo Kanagi, Cheng-Yuan Ku, Li-Kai Lin, Wen-Huai Hsieh

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Background: While electronic health records have been collected for many years in Taiwan, their interoperability across different health care providers has not been entirely achieved yet. The exchange of clinical data is still inefficient and time consuming.

    Objectives: This study proposes an efficient patient-centric framework based on the blockchain technology that makes clinical data accessible to patients and enable transparent, traceable, secure, and effective data sharing between physicians and other health care providers.

    Methods: Health care experts were interviewed for the study, and medical data were collected in collaboration with Ministry of Health and Welfare (MOHW) Chang-Hua hospital. The proposed framework was designed based on the detailed analysis of this information. The framework includes smart contracts in an Ethereum-based permissioned blockchain to secure and facilitate clinical data exchange among different parties such as hospitals, clinics, patients, and other stakeholders. In addition, the framework employs the Logical Observation Identifiers Names and Codes (LOINC) standard to ensure the interoperability and reuse of clinical data.

    Results: The prototype of the proposed framework was deployed in Chang-Hua hospital to demonstrate the sharing of health examination reports with many other clinics in suburban areas. The framework was found to reduce the average access time to patient health reports from the existing next-day service to a few seconds.

    Conclusion: The proposed framework can be adopted to achieve health record sharing among health care providers with higher efficiency and protected privacy compared to the system currently used in Taiwan based on the client-server architecture.
    Original languageAmerican English
    Pages (from-to)193-204
    JournalMethods of Information in Medicine
    Volume59
    Issue number6
    DOIs
    StatePublished - Dec 2020

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