Accelerating health data sharing: a solution based on the internet of things and distributed ledger technologies
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Abstract
BACKGROUND: Huge 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. OBJECTIVE: This 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. METHODS: The 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. RESULTS: A 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. CONCLUSIONS: DLT 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.
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Extant research has focused on promulgating the major benefits of blockchain within healthcare ecosystems to promote fairness and effective decentralization.
“Extant research has also focused on promulgating the major benefits of blockchain within healthcare ecosystems to promote fairness and effective decentralization (Noh et al., 2017;Zhang et al., 2018;Zheng et al., 2019).”
From Blockchain in healthcare: A systematic literature review, synthesizing framework and future research agenda (2020) · cites this paper as Zheng et al., 2019
The Abstract (Background) describes decentralized ledger technologies, including blockchain, as ways to facilitate health-data sharing, and the Introduction (Distributed Ledger Technologies and Blockchain) describes blockchain’s absence of centralized control. The paper does not establish that these benefits promote fairness; its proposed health-data-sharing system is based specifically on IOTA Tangle rather than conventional blockchain. (LLM, conf=0.84)
Experimental — accuracy not yet measuredReview shows attempts have been made to increase benefits of blockchain in healthcare through AI and ML.
“Third, our review has shown that attempts have been made to increase the benefits of applying blockchain technology in healthcare through other technologies such as artificial intelligence (AI) and machine learning (e.g.Mamoshina et al., 2018;Kuo et al., 2019a,b;Zheng et al., 2019).”
From Blockchain in healthcare: A systematic literature review, synthesizing framework and future research agenda (2020) · cites this paper as Zheng et al., 2019
The Methods > Implementation section (page unavailable) describes a prototype that uses deep learning to evaluate tremor data before publishing the results to IOTA Tangle. This supports the example of combining machine learning with a health-care distributed ledger, but the paper uses Tangle rather than a traditional blockchain and does not establish that machine learning increases blockchain’s benefits. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThe review suggests that management of access control has seen extensive investigation.
“The review suggests that management of access control (Zheng et al., 2019) has especially seen extensive investigation (Badr et al., 2018;Brogan et al., 2018).”
From Blockchain in healthcare: A systematic literature review, synthesizing framework and future research agenda (2020) · cites this paper as Zheng et al., 2019
Previously reported constraints include need for gateway layer for Tangle, limited fog layer storage, semantic interoperability, and incompatibility with legacy systems.
“Some previously reported constraints include the necessity of a gateway layer for Tangle to establish direct communication with sensors (Zheng et al. 2019), the limited storage provided by a fog layer (Silva et al., 2019), as well as semantic interoperability and incompatibility with legacy systems (Zhang et al., 2018).”
From Blockchain in healthcare: A systematic literature review, synthesizing framework and future research agenda (2020) · cites this paper as Zheng et al. 2019
Few studies proposed focusing on improving medical diagnostics and extending applications to other areas like rehabilitation.
“Although algorithms exist for the deployment of blockchain that are geared towards improving healthcare processes, few studies have proposed the need for scholars to focus on the nuances of improving medical diagnostics (Mamoshina et al., 2018) and extending their application to other areas of healthcare, such as rehabilitation (Zheng et al., 2019).”
From Blockchain in healthcare: A systematic literature review, synthesizing framework and future research agenda (2020) · cites this paper as Zheng et al., 2019
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References 19 · Cited by 137
Research works printed but not linked: 21
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- Authors
- Zheng, X., Sun, S., Mukkamala, R. R., Vatrapu, R., & Ordieres-Meré, J.
- Affiliations
- Universidad Politécnica de Madrid (ES)
- Copenhagen Business School (DK)
- Corresponding author
- Ordieres-Meré, Joaquín
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2019-06-06
- Volume
- 21
- Issue
- 6
- Pages
- e13583
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC6592507
- OpenAlex
- W2941036880
- Cited by
- 137
- References
- 19
- Citations by year
- 2026: 6 · 2025: 13 · 2024: 20 · 2023: 28 · 2022: 22 · 2021: 21 · 2020: 25 · 2019: 2
- FWCI
- 21.77
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- CBS Research Portal (Copenhagen Business School) (submittedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- health data sharing, Internet of Things, distributed ledger technology, IOTA Tangle, directed acyclic graphs, blockchain, wearable devices, air quality sensors, Merkle trees, data integrity, data privacy, access control, tremor diagnosis, edge computing
- MeSH terms
- Wearable Electronic Devices, Internet of Things, Confidentiality, Humans, Risk Management, Computer Security, Information Dissemination, Electronic Health Records
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