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Blockchain-enabled immutable, distributed, and highly available clinical research activity logging system for federated COVID-19 data analysis from multiple institutions

Authors: Tsung-Ting Kuo; Anh Pham; Maxim E Edelson; Jihoon Kim; Jason Chan; Yash Gupta; Lucila Ohno-Machado; +55 Authors

Blockchain-enabled immutable, distributed, and highly available clinical research activity logging system for federated COVID-19 data analysis from multiple institutions

Abstract

Abstract Objective We aimed to develop a distributed, immutable, and highly available cross-cloud blockchain system to facilitate federated data analysis activities among multiple institutions. Materials and Methods We preprocessed 9166 COVID-19 Structured Query Language (SQL) code, summary statistics, and user activity logs, from the GitHub repository of the Reliable Response Data Discovery for COVID-19 (R2D2) Consortium. The repository collected local summary statistics from participating institutions and aggregated the global result to a COVID-19-related clinical query, previously posted by clinicians on a website. We developed both on-chain and off-chain components to store/query these activity logs and their associated queries/results on a blockchain for immutability, transparency, and high availability of research communication. We measured run-time efficiency of contract deployment, network transactions, and confirmed the accuracy of recorded logs compared to a centralized baseline solution. Results The smart contract deployment took 4.5 s on an average. The time to record an activity log on blockchain was slightly over 2 s, versus 5–9 s for baseline. For querying, each query took on an average less than 0.4 s on blockchain, versus around 2.1 s for baseline. Discussion The low deployment, recording, and querying times confirm the feasibility of our cross-cloud, blockchain-based federated data analysis system. We have yet to evaluate the system on a larger network with multiple nodes per cloud, to consider how to accommodate a surge in activities, and to investigate methods to lower querying time as the blockchain grows. Conclusion Blockchain technology can be used to support federated data analysis among multiple institutions.

Keywords

Distributed Computing and Systems Software, Biomedical and clinical sciences, Coronaviruses, 4606 Distributed Computing and Systems Software (for-2020), 08 Information and Computing Sciences (for), 11 Medical and Health Sciences (for), Research and Applications, Medical and Health Sciences, Emerging Infectious Diseases (rcdc), Biomedical Informatics, Medical Informatics (science-metrix), Machine Learning, 46 Information and Computing Sciences (for-2020), Engineering, Blockchain, 42 Health sciences (for-2020), Information and Computing Sciences, Internal Medicine, Medical Specialties, Medicine and Health Sciences, Electronic Health Records, Humans, 46 Information and computing sciences (for-2020), Blockchain (mesh), Humans (mesh), Research (mesh), 000, COVID-19 (mesh), Research, Health sciences, Reproducibility of Results, Coronaviruses (rcdc), COVID-19, electronic health record, clinical information systems, 004, Hospitalization, Infectious Diseases, Emerging Infectious Diseases, R2D2 Consortium, 32 Biomedical and clinical sciences (for-2020), Infectious Diseases (rcdc), 09 Engineering (for), blockchain distributed ledger technology, decision support systems, Medical Informatics, Algorithms

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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
13
Top 10%
Average
Top 10%
Green
hybrid