Evidence map›Paper›PMID 32196460›Full record

ArticleJournal of medical Internet research2020

Blockchain-Authenticated Sharing of Genomic and Clinical Outcomes Data of Patients With Cancer: A Prospective Cohort Study.

Benjamin Scott Glicksberg, Shohei Burns, Rob Currie, Ann Griffin, Zhen Jane Wang, David Haussler, Theodore Goldstein, Eric Collisson

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Blockchain in Health Information Systems: A Systematic Review.International journal of environmental research and public health · 2024
    Pooled it
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  15. Is blockchain ready for orthopaedics? A systematic review.Journal of clinical orthopaedics and trauma · 2021
    Review
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  17. Blockchain in Health Care: Hope or Hype?Journal of medical Internet research · 2020
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Benjamin Scott Glicksberg *Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, United States.ORCID 0000-0003-4515-8090
Shohei Burns *Division of Hematology and Oncology, Department of Medicine, University of California, San Francisco, CA, United States.ORCID 0000-0001-7622-4658
Rob CurrieUC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, United States.ORCID 0000-0003-1828-1827
Ann GriffinHelen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, United States.ORCID 0000-0003-0837-6515
Zhen Jane WangDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States.ORCID 0000-0002-2065-5296
David HausslerUC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, United States.ORCID 0000-0003-1533-4575
Theodore GoldsteinBakar Computational Health Sciences Institute, University of California, San Francisco, CA, United States.ORCID 0000-0001-7424-5041
Eric CollissonDivision of Hematology and Oncology, Department of Medicine, University of California, San Francisco, CA, United States.ORCID 0000-0001-8037-9388

Funding

Clinical and Translational Science InstituteUL1TR001872 · NCATS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI COLLARD, HAROLD R, JACOBY, VANESSA · 2016 to 2025
$112.1M
UCSF Project 1U54CA224081 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI CALVIN J KUO · 2017 to 2026
$12.9M
Stanford and Northrop Grumman proposal for the Oncology Models ForumU24CA195858 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI BUTTE, ATUL J · 2015 to 2019
$4.7M
Visualization hub for genomics data exploration and translational discoveryU24CA210974 · NCI · UNIVERSITY OF CALIFORNIA SANTA CRUZ · PI COLLISSON, ERIC, HAUSSLER, DAVID H · 2016 to 2020
$4.2M
Cloud Based Resource for Data Hosting, Visualization and Analysis Using UCSC CancU24CA180951 · NCI · UNIVERSITY OF CALIFORNIA SANTA CRUZ · PI HAUSSLER, DAVID H · 2013 to 2017
$2.9M
NCATS NIH HHS UL1 TR001872NCI NIH HHS U24 CA180951NCI NIH HHS U24 CA195858NCI NIH HHS U24 CA210974NCI NIH HHS U54 CA224081
6 · The paper itself

Abstract

backgroundEfficiently sharing health data produced during standard care could dramatically accelerate progress in cancer treatments, but various barriers make this difficult. Not sharing these data to ensure patient privacy is at the cost of little to no learning from real-world data produced during cancer care. Furthermore, recent research has demonstrated a willingness of patients with cancer to share their treatment experiences to fuel research, despite potential risks to privacy.

objectiveThe objective of this study was to design, pilot, and release a decentralized, scalable, efficient, economical, and secure strategy for the dissemination of deidentified clinical and genomic data with a focus on late-stage cancer.

methodsWe created and piloted a blockchain-authenticated system to enable secure sharing of deidentified patient data derived from standard of care imaging, genomic testing, and electronic health records (EHRs), called the Cancer Gene Trust (CGT). We prospectively consented and collected data for a pilot cohort (N=18), which we uploaded to the CGT. EHR data were extracted from both a hospital cancer registry and a common data model (CDM) format to identify optimal data extraction and dissemination practices. Specifically, we scored and compared the level of completeness between two EHR data extraction formats against the gold standard source documentation for patients with available data (n=17).

resultsAlthough the total completeness scores were greater for the registry reports than those for the CDM, this difference was not statistically significant. We did find that some specific data fields, such as histology site, were better captured using the registry reports, which can be used to improve the continually adapting CDM. In terms of the overall pilot study, we found that CGT enables rapid integration of real-world data of patients with cancer in a more clinically useful time frame. We also developed an open-source Web application to allow users to seamlessly search, browse, explore, and download CGT data.

conclusionsOur pilot demonstrates the willingness of patients with cancer to participate in data sharing and how blockchain-enabled structures can maintain relationships between individual data elements while preserving patient privacy, empowering findings by third-party researchers and clinicians. We demonstrate the feasibility of CGT as a framework to share health data trapped in silos to further cancer research. Further studies to optimize data representation, stream, and integrity are required.

Indexed as

BlockchainCohort StudiesGenomicsHumansNeoplasmsPilot ProjectsProspective StudiesTreatment Outcomeblockchaindata sharingelectronic health recordsgenomicsmedicineneoplasms

Identifiers

PMID32196460
PMCPMC7125440

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.