Evidence map›Paper›PMID 31172963›Full record

ArticleJournal of medical Internet research2019

Accelerating Health Data Sharing: A Solution Based on the Internet of Things and Distributed Ledger Technologies.

Xiaochen Zheng, Shengjing Sun, Raghava Rao Mukkamala, Ravi Vatrapu, Joaquín Ordieres-Meré

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 4 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

25 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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  14. Design and implementation of hybrid consensus mechanism for IoT based healthcare system security.International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management · 2022
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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

5 authors.

Xiaochen ZhengEscuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, Madrid, Spain.ORCID 0000-0003-1506-3314
Shengjing SunEscuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, Madrid, Spain.ORCID 0000-0002-8790-7371
Raghava Rao MukkamalaCentre for Business Data Analytics, Department of Digitalization, Copenhagen Business School, Copenhagen, Denmark.ORCID 0000-0001-9814-3883
Ravi VatrapuCentre for Business Data Analytics, Department of Digitalization, Copenhagen Business School, Copenhagen, Denmark.ORCID 0000-0002-9109-5281
Joaquín Ordieres-Meré *Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, Madrid, Spain.ORCID 0000-0002-9677-6764

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHuge amounts of health-related data are generated every moment with the rapid development of Internet of Things (IoT) and wearable technologies. These big health data contain great value and can bring benefit to all stakeholders in the health care ecosystem. Currently, most of these data are siloed and fragmented in different health care systems or public and private databases. It prevents the fulfillment of intelligent health care inspired by these big data. Security and privacy concerns and the lack of ensured authenticity trails of data bring even more obstacles to health data sharing. With a decentralized and consensus-driven nature, distributed ledger technologies (DLTs) provide reliable solutions such as blockchain, Ethereum, and IOTA Tangle to facilitate the health care data sharing.

objectiveThis study aimed to develop a health-related data sharing system by integrating IoT and DLT to enable secure, fee-less, tamper-resistant, highly-scalable, and granularly-controllable health data exchange, as well as build a prototype and conduct experiments to verify the feasibility of the proposed solution.

methodsThe health-related data are generated by 2 types of IoT devices: wearable devices and stationary air quality sensors. The data sharing mechanism is enabled by IOTA's distributed ledger, the Tangle, which is a directed acyclic graph. Masked Authenticated Messaging (MAM) is adopted to facilitate data communications among different parties. Merkle Hash Tree is used for data encryption and verification.

resultsA prototype system was built according to the proposed solution. It uses a smartwatch and multiple air sensors as the sensing layer; a smartphone and a single-board computer (Raspberry Pi) as the gateway; and a local server for data publishing. The prototype was applied to the remote diagnosis of tremor disease. The results proved that the solution could enable costless data integrity and flexible access management during data sharing.

conclusionsDLT integrated with IoT technologies could greatly improve the health-related data sharing. The proposed solution based on IOTA Tangle and MAM could overcome many challenges faced by other traditional blockchain-based solutions in terms of cost, efficiency, scalability, and flexibility in data access management. This study also showed the possibility of fully decentralized health data sharing by replacing the local server with edge computing devices.

Indexed as

Computer SecurityElectronic Health RecordsInformation DisseminationInternet of ThingsWearable Electronic DevicesConfidentialityHumansRisk Managementblockchaindata sharingdistributed ledger technologieshealth information interoperabilityintelligent healthcareInternet of ThingsIOTA Tanglemasked authenticated messaging

Identifiers

PMID31172963
PMCPMC6592507

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.