Evidence map›Paper›PMID 39292723›Full record

ArticlePloS one2024

PatCen: A blockchain-based patient-centric mechanism for the granular access control of infectious disease-related test records.

Bello Musa Yakubu, Syeda Mahera Ali, Majid Iqbal Khan, Pattarasinee Bhattarakosol

Abstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Bello Musa YakubuDepartment of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand.ORCID 0000-0002-2925-0607
Syeda Mahera AliDepartment of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan.
Majid Iqbal KhanDepartment of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan.
Pattarasinee BhattarakosolDepartment of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand.ORCID 0000-0002-9938-028X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The recent global outbreaks of infectious diseases such as COVID-19, yellow fever, and Ebola have highlighted the critical need for robust health data management systems that can rapidly adapt to and mitigate public health emergencies. In contrast to traditional systems, this study introduces an innovative blockchain-based Electronic Health Record (EHR) access control mechanism that effectively safeguards patient data integrity and privacy. The proposed approach uniquely integrates granular data access control mechanism within a blockchain framework, ensuring that patient data is only accessible to explicitly authorized users and thereby enhancing patient consent and privacy. This system addresses key challenges in healthcare data management, including preventing unauthorized access and overcoming the inefficiencies inherent in traditional access mechanisms. Since the latency is a sensitive factor in healthcare data management, the simulations of the proposed model reveal substantial improvements over existing benchmarks in terms of reduced computing overhead, increased throughput, minimized latency, and strengthened overall security. By demonstrating these advantages, the study contributes significantly to the evolution of health data management, offering a scalable, secure solution that prioritizes patient autonomy and privacy in an increasingly digital healthcare landscape.

Indexed as

BlockchainCOVID-19Electronic Health RecordsCommunicable DiseasesComputer SecurityConfidentialityHumansPrivacySARS-CoV-2

Identifiers

PMID39292723
PMCPMC11410234

What OpenQuestion holds

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LicenceCC BY
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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.