The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Blockchain Technology for Healthcare: Facilitating the Transition to Patient-Driven Interoperability
Open accessAbstract
Interoperability in healthcare has traditionally been focused around data exchange between business entities, for example, different hospital systems. However, there has been a recent push towards patient-driven interoperability, in which health data exchange is patient-mediated and patient-driven. Patient-centered interoperability, however, brings with it new challenges and requirements around security and privacy, technology, incentives, and governance that must be addressed for this type of data sharing to succeed at scale. In this paper, we look at how blockchain technology might facilitate this transition through five mechanisms: (1) digital access rules, (2) data aggregation, (3) data liquidity, (4) patient identity, and (5) data immutability. We then look at barriers to blockchain-enabled patient-driven interoperability, specifically clinical data transaction volume, privacy and security, patient engagement, and incentives. We conclude by noting that while patient-driving interoperability is an exciting trend in healthcare, given these challenges, it remains to be seen whether blockchain can facilitate the transition from institution-centric to patient-centric data sharing.
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“First, the designs of conventional access control strategies [14]-[16] require external key authorities with a Public Key Infrastructure (PKI), which can be extremely complex and usually demands expensive resources to achieve secure EHRs sharing [25].”
From Blockchain for Secure EHRs Sharing of Mobile Cloud Based E-Health Systems (2019) · cites this paper as [25]
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“More importantly, conventional access control systems mainly rely on a predefined point of access, i.e. a centralized cloud server, and this can lead to the central point of failure for e-health networks [25].”
From Blockchain for Secure EHRs Sharing of Mobile Cloud Based E-Health Systems (2019) · cites this paper as [25]
Experimental — accuracy not yet measuredIn the citing paper
“Our system is a hyperledger composer [19] based Blockchain application that alleviates these problems with an architecture that allows our system to be both extensible and scalable: a blockchain based EACMS, off chain data storage and a patient-centered mobile and web interface.”
From EACMS: Emergency Access Control Management System for Personal Health Record Based on Blockchain (2019) · cites this paper as [19]
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Metadata
- Authors
- Gordon, W. J., & Catalini, C.
- Affiliations
- Brigham and Women's Hospital (US)
- National Bureau of Economic Research (US)
- Corresponding author
- Gordon, William J.
- Venue
- Computational and Structural Biotechnology Journal
- Published
- 2018-01
- Volume
- 16
- Pages
- 224-230
- ISSN
- 2001-0370 (ISSN-L)
- PMC
- PMC6068317
- OpenAlex
- W2810476200
- Cited by
- 828
- References
- 34
- Citations by year
- 2026: 52 · 2025: 80 · 2024: 94 · 2023: 126 · 2022: 151 · 2021: 135 · 2020: 112 · 2019: 78
- FWCI
- 15.04
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computational and Structural Biotechnology Journal (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology in Education and Learning
- Keywords
- blockchain, health data sharing, data privacy, data security, patient identity, data immutability, data aggregation, patient engagement, incentives, governance, barriers to adoption
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 ›
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