Cited by 160Blockchain Technology Applications and SecurityBlockchain Technology in Education and LearningElectronic Health Records Systems

Prototype of running clinical trials in an untrustworthy environment using blockchain

Open access
Wong, Daniel R., Bhattacharya, Sanchita, Butte, Atul J.·2019·Nature Communications

Abstract

Monitoring and ensuring the integrity of data within the clinical trial process is currently not always feasible with the current research system. We propose a blockchain-based system to make data collected in the clinical trial process immutable, traceable, and potentially more trustworthy. We use raw data from a real completed clinical trial, simulate the trial onto a proof of concept web portal service, and test its resilience to data tampering. We also assess its prospects to provide a traceable and useful audit trail of trial data for regulators, and a flexible service for all members within the clinical trials network. We also improve the way adverse events are currently reported. In conclusion, we advocate that this service could offer an improvement in clinical trial data management, and could bolster trust in the clinical research process and the ease at which regulators can oversee trials.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41467-019-08874-y

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Blockchain adoption has been posited to have positive connotations in clinical trial management, DNA data transmission, remote patient monitoring, drug discovery, biomarker development, and preventive healthcare.

    “For example, blockchain adoption has been posited to have positive connotations in clinical trial management (Wong et al., 2019), DNA data transmission (S.J. Lee et al., 2018), remote patient monitoring (Griggs et al., 2018;Dwivedi et al., 2019) as well as drug discovery, biomarker development, and preventive healthcare (Mamoshina et al., 2018).”
  • Studies have focused on improving processes for timely updates and reporting for adverse events.

    “Additionally, prior studies have focused on improving processes for timely updates (Wang & Song, 2018) and reporting for adverse events (Wong et al., 2019).”
  • Societal constraints include possibility of collusion for fabricating data and inability to control clinical malpractice.

    “For example, there may be a possibility of collusion for fabricating data (Wong et al., 2019) or a healthcare system may be constrained by an inability to control clinical malpractice (Zhang et al., 2018)”
  • Certain studies pointed to socially-oriented concerns about public data sharing and users' trust in governments.

    “However, certain studies have also pointed to more sociallyoriented concerns about public data sharing (Wong et al., 2019) and users' trust in governments (Dhagarra et al., 2019).”
  • Researchers have called for extending technical boundaries of framework development to improve scalability and functionality.

    “Further, in order to improve the scalability (Zhang et al., 2016) and functionality (Wong et al., 2019) of previously proposed frameworks, researchers have called for the need to extend the current technical boundaries of framework development, e.g. by consideration of novel blockchain platforms (Rahmadika & Rhee, 2019).”

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The citation lists haven’t been fetched yet

References 15 · Cited by 160

Research works printed but not linked: 12

  1. [1]Gupta A Fraud and misconduct in clinical research: a concern Perspect. Clin. Res. 2013 4 144 147 10.4103/2229-3485.111800 23833741 PMC3700330 · DOI
  2. [2]George SL Buyse M Data fraud in clinical trials Clin. Investig. 2015 5 161 173 10.4155/cli.14.116 PMC4340084 25729561 · DOI · Paper page
  3. [3]Bhatt A Quality of clinical trials: a moving target Perspect. Clin. Res. 2011 2 124 128 10.4103/2229-3485.86880 22145122 PMC3227329 · DOI
  4. [4]Valkenhoef G Tervonen T Brock B Hillege H Deficiencies in the transfer and viability of clinical trials evidence: a review of existing systems and standards BMC Med. Inform. Decis. Mak. 2012 12 95 10.1186/1472-6947-12-95 22947211 PMC3534489 · DOI
  5. [5]Hume S Sarnikar S Becnel L Bennet D Visualizing and validating metadata traceability within the CDISC standards AMIA Jt Summits Transl. Sci. Proc. 2017 2017 158 165 28815125 PMC5543378
  6. [7]Kuo T Kim HE Ohno-Machado L Blockchain distributed ledger technologies for biomedical and health care applications J. Am. Med. Inform. Assoc. 2017 24 1211 1220 10.1093/jamia/ocx068 29016974 PMC6080687 · DOI · Paper page
  7. [8]Boulos M Wilson T Clauson K Geospatial blockchain: promises, challenges, and scenarios in health and healthcare Int J. Health Geogr. 2018 17 25 10.1186/s12942-018-0144-x 29973196 PMC6033217 · DOI
  8. [9]Nugent T Upton D Cimpoesu M Improving data transparency in clinical trials using blockchain smart contracts F1000 Res. 2016 5 2541 10.12688/f1000research.9756.1 PMC5357027 28357041 · DOI · Paper page
  9. [10]Benchoufi M Ravaud P Blockchain technology for improving clinical research quality Trials 2017 18 335 10.1186/s13063-017-2035-z 28724395 PMC5517794 · DOI
  10. [14]Bhattacharya S ImmPort, toward repurposing of open access immunological assay data for translational and clinical research Sci. Data 2018 5 180015 10.1038/sdata.2018.15 29485622 PMC5827693 · DOI
  11. [15]Feige U Fiat A Shamir A Zero-knowledge proofs of identity J. Cryptol. 1988 1 77 94 10.1007/BF02351717 · DOI
  12. [16]Benet, J. IPFS – Content Addressed, Versioned, P2P File System. Preprint at https://arxiv.org/abs/1407.3561 (2017). · arXiv

Authors
Wong, D. R., Bhattacharya, S., & Butte, A. J.
Affiliations
University of California, San Francisco (US)
Corresponding author
Butte, Atul J.
Venue
Nature Communications
Published
2019-02-22
Volume
10
Issue
1
Pages
917-917
ISSN
2041-1723 (ISSN-L)
OpenAlex
W2917431061
Cited by
160
References
15
Citations by year
2026: 6 · 2025: 11 · 2024: 20 · 2023: 29 · 2022: 27 · 2021: 29 · 2020: 27 · 2019: 11
FWCI
29.24
Type
journal article
Language
en
Open access
Locations
  • Nature Communications (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • eScholarship (California Digital Library) (submittedVersion)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
Funders
  • National Institutes of Health (HHSN316201200036W)
  • National Institute of Allergy and Infectious Diseases (HHSN316201200036W)
Topics
  • Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
  • Physical Sciences › Computer Science › Information Systems › Blockchain Technology in Education and Learning
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
Keywords
blockchain, clinical trials, clinical trial data integrity, data immutability, data traceability, audit trail, adverse event reporting, clinical trial data management, regulatory oversight
MeSH terms
Proof of Concept Study, Clinical Trials as Topic, Data Collection, Delivery of Health Care, Humans, Medical Audit, Quality Control

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review