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Blockchain for healthcare data management: opportunities, challenges, and future recommendations

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Abstract

Today's healthcare data management systems are facing key challenges in terms of data transparency, traceability, immutability, audit, data provenance, flexible access, trust, privacy, and security. Also, a large portion of existing healthcare systems leveraged for managing data are centralized that pose potential risks of single point of failures in case of natural disasters. Blockchain is an emerging and disruptive decentralized technology that has the potential to significantly revolutionize, reshape, and transform the way data are being handled in healthcare industries. In this paper, we discuss how leveraging blockchain for healthcare data management systems can lead to stimulate innovations and bring major improvements. We present the key blockchain features and characteristics. We discuss the premier advantages of adopting blockchain technology along with opportunities for healthcare industries. We present recent on-going projects and case studies to show the practicality of blockchain technology for various healthcare applications. We identify and discuss important open research challenges hindering the successful adoption of blockchain in the healthcare sector. Finally, we outline several future research directions.

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Acknowledgements

This publication is based upon work supported by the Khalifa University of Science and Technology under Award No. CIRA-2019-001.

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Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, 127788, Abu Dhabi, United Arab Emirates

    Ibrar Yaqoob, Khaled Salah & Yousof Al-Hammadi

  2. Department of Industrial and Systems Engineering, Khalifa University of Science and Technology, 127788, Abu Dhabi, United Arab Emirates

    Raja Jayaraman

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  1. Ibrar Yaqoob
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Yaqoob, I., Salah, K., Jayaraman, R. et al. Blockchain for healthcare data management: opportunities, challenges, and future recommendations. Neural Comput & Applic 34, 11475–11490 (2022). https://doi.org/10.1007/s00521-020-05519-w

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