Healthcare Data Gateways: Found Healthcare Intelligence on Blockchain with Novel Privacy Risk Control
Abstract
Healthcare data are a valuable source of healthcare intelligence. Sharing of healthcare data is one essential step to make healthcare system smarter and improve the quality of healthcare service. Healthcare data, one personal asset of patient, should be owned and controlled by patient, instead of being scattered in different healthcare systems, which prevents data sharing and puts patient privacy at risks. Blockchain is demonstrated in the financial field that trusted, auditable computing is possible using a decentralized network of peers accompanied by a public ledger. In this paper, we proposed an App (called Healthcare Data Gateway (HGD)) architecture based on blockchain to enable patient to own, control and share their own data easily and securely without violating privacy, which provides a new potential way to improve the intelligence of healthcare systems while keeping patient data private. Our proposed purpose-centric access model ensures patient own and control their healthcare data; simple unified Indicator-Centric Schema (ICS) makes it possible to organize all kinds of personal healthcare data practically and easily. We also point out that MPC (Secure Multi-Party Computing) is one promising solution to enable untrusted third-party to conduct computation over patient data without violating privacy.
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There has been no study to evaluate the use of blockchain technology in an mHealth system.
“However, there has been no study to evaluate the use of blockchain technology in an mHealth system.”
From Tamper-Resistant Mobile Health Using Blockchain Technology. (2017) · cites this paper as [12]
Blockchain may assist in creating a viable information ecosystem for managing personal health records by incorporating heterogeneous forms of data, including medical big data.
“Further, blockchain may assist in creating a viable information ecosystem for managing such PHRs (Mamoshina et al., 2018) by incorporating heterogeneous forms of data (Yue et al., 2016;Shen et al., 2019;Silva et al., 2019), including medical big data (Kaur et al., 2018).”
From Blockchain in healthcare: A systematic literature review, synthesizing framework and future research agenda (2020) · cites this paper as Yue et al., 2016
Few reviewed studies acknowledged the need for regulatory compliance or standards and goals for compliance requirements.
“Our review suggests that few studies have acknowledged the need for regulatory compliance (Yue et al., 2016) or even the standards and goals for compliance requirements (Zhang & Lin, 2018;Al Omar et al., 2019;Li et al., 2019).”
From Blockchain in healthcare: A systematic literature review, synthesizing framework and future research agenda (2020) · cites this paper as Yue et al., 2016
These concerns relate to technical limitations including node security, cryptographic security level, and data privacy during computations.
“These concerns are mainly related to the technical limitations of blockchain technology, such as the security of individual nodes (Griggs et al., 2018), the level of security enabled by the cryptographic elements incorporated in framework (Tian et al., 2019), and the maintenance of data privacy while requesters complete their computations (Yue et al., 2016).”
From Blockchain in healthcare: A systematic literature review, synthesizing framework and future research agenda (2020) · cites this paper as Yue et al., 2016
If the accessibility of knowledge or data becomes seamless through expanded software, applications, and mobile devices, patients gain access to their medical history and details regarding care.
“If the accessibility of knowledge or data becomes seamless through expanded Software and Applications and mobile devices, patients gain access to their medical history and details regarding care (Yue et al. 2016).”
From Blockchain and Artificial Intelligence Technology in E-Health (2021) · cites this paper as Yue et al. 2016
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- Authors
- Yue, X., Wang, H., Jin, D., Li, M., & Jiang, W.
- Affiliations
- Zhongnan University of Economics and Law (CN)
- Corresponding author
- Wang, Huiju
- Venue
- Journal of Medical Systems
- Published
- 2016-08-26
- Volume
- 40
- Issue
- 10
- Pages
- 218-218
- ISSN
- 0148-5598 (ISSN-L)
- 1573-689X
- OpenAlex
- W2514994441
- Cited by
- 1,240
- References
- 20
- Citations by year
- 2026: 38 · 2025: 95 · 2024: 106 · 2023: 139 · 2022: 177 · 2021: 196 · 2020: 168 · 2019: 188 · 2018: 101 · 2017: 25 · 2016: 7
- FWCI
- 239.45
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Medical Systems (publishedVersion)
- PubMed (publishedVersion)
- 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 › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- health data sharing, blockchain, patient data ownership, data privacy, secure multi-party computation, decentralized data management
- MeSH terms
- Confidentiality, Humans, Risk Management, Computer Security, Information Dissemination, Electronic Health Records
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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.
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