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Healthcare Data Gateways: Found Healthcare Intelligence on Blockchain with Novel Privacy Risk Control

Overview of attention for article published in Journal of Medical Systems, August 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#34 of 1,182)
  • High Attention Score compared to outputs of the same age (90th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

policy
1 policy source
twitter
4 X users
patent
5 patents
wikipedia
1 Wikipedia page

Citations

dimensions_citation
943 Dimensions

Readers on

mendeley
1107 Mendeley
citeulike
1 CiteULike
Title
Healthcare Data Gateways: Found Healthcare Intelligence on Blockchain with Novel Privacy Risk Control
Published in
Journal of Medical Systems, August 2016
DOI 10.1007/s10916-016-0574-6
Pubmed ID
Authors

Xiao Yue, Huiju Wang, Dawei Jin, Mingqiang Li, Wei Jiang

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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1,107 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 <1%
Tanzania, United Republic of 1 <1%
Netherlands 1 <1%
Unknown 1103 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 207 19%
Student > Ph. D. Student 148 13%
Student > Bachelor 100 9%
Researcher 76 7%
Student > Doctoral Student 44 4%
Other 167 15%
Unknown 365 33%
Readers by discipline Count As %
Computer Science 336 30%
Engineering 102 9%
Business, Management and Accounting 93 8%
Medicine and Dentistry 36 3%
Economics, Econometrics and Finance 24 2%
Other 113 10%
Unknown 403 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 18. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 March 2024.
All research outputs
#1,772,725
of 23,524,722 outputs
Outputs from Journal of Medical Systems
#34
of 1,182 outputs
Outputs of similar age
#32,654
of 340,710 outputs
Outputs of similar age from Journal of Medical Systems
#1
of 23 outputs
Altmetric has tracked 23,524,722 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,182 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done particularly well, scoring higher than 97% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 340,710 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 23 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.