Blockchain-based Secure Data Sharing Framework for Healthcare Industry: A Case Study of U.S. Healthcare

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Research ID G33X1

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Abstract

This paper focuses on proposing and assessing a blockchain application solution to deal with the security and privacy issues in the US healthcare system. This study uses an exploratory qualitative case study research design to examine a) the presence of blockchain technology in the management of health informatics data and b) its effects. The proposed structure is to improve the protection of information, to protect it from unauthorized access, as well as to comply with patients’ records’ authenticity due to decentralised bases and cryptocurrencies technologies. Furthermore, it solves the problem of incompatible systems by providing for the systemization of data sharing to various EHRs.

These is an affirmation that blockchain framework provides a more secure platform since it reduces the risks of data loss, intend, and deliberate forgery by enhancing privacy. Cryptographic hashing and smart contracts have the ability to protect important data from being shared while at the same time being in line with privacy laws. In addition, the use of the framework increases compatibility because data is shared from one platform to another; hence improving communication between different EHR systems. In a way, this study has highlighted the capacity of blockchain for modern healthcare data management, which is quite beneficial to resolve current problems. Nonetheless, more studies should be conducted on the workability and compatibility of blockchain over a long term and alongside other upcoming technologies. These findings are useful for the future research of the blockchain as the tool that optimizes the healthcare sphere and makes it more protected.

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Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

Not applicable

Data Availability

The datasets used in this study are openly available at [repository link] and the source code is available on GitHub at [GitHub link].

Funding

This work did not receive any external funding.

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  • Classification

    NLM Code: W 26.55.C7, WX 173.1

  • Version of record

    v1.0

  • Issue date

    25 November 2024

  • Language

    English

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