The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Healthcare Blockchain System Using Smart Contracts for Secure Automated Remote Patient Monitoring
Abstract
As Internet of Things (IoT) devices and other remote patient monitoring systems increase in popularity, security concerns about the transfer and logging of data transactions arise. In order to handle the protected health information (PHI) generated by these devices, we propose utilizing blockchain-based smart contracts to facilitate secure analysis and management of medical sensors. Using a private blockchain based on the Ethereum protocol, we created a system where the sensors communicate with a smart device that calls smart contracts and writes records of all events on the blockchain. This smart contract system would support real-time patient monitoring and medical interventions by sending notifications to patients and medical professionals, while also maintaining a secure record of who has initiated these activities. This would resolve many security vulnerabilities associated with remote patient monitoring and automate the delivery of notifications to all involved parties in a HIPAA compliant manner.
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In the citing paper
“Recent studies have begun to incorporate analogous and peripheral technologies into blockchain-based system architectures that include IoT (Dwivedi et al., 2019), sensors (Casado-Vara & Corchado, 2019), wireless body area networks (Griggs et al., 2018), (Mamoshina et al., 2018), big data (Dhagarra et al., 2019), edge computing (Zheng et al., 2019), and cloud technology (Kaur et al., 2018).”
From Blockchain in healthcare: A systematic literature review, synthesizing framework and future research agenda (2020) · cites this paper as Griggs et al., 2018
In the citing paper
“For example, blockchain adoption has been posited to have positive connotations in clinical trial management (Wong et al., 2019), DNA data transmission (S.J. Lee et al., 2018), remote patient monitoring (Griggs et al., 2018;Dwivedi et al., 2019) as well as drug discovery, biomarker development, and preventive healthcare (Mamoshina et al., 2018).”
From Blockchain in healthcare: A systematic literature review, synthesizing framework and future research agenda (2020) · cites this paper as Griggs et al., 2018
In the citing paper
“Time-related issues have also been discussed as a constraint, including the time spent searching for the global smart contracts (Dagher et al., 2018), increased time consumption (Islam et al., 2019), transmission timing (Griggs et al., 2018), time required for seeking the required data in shared storage by the data recipient (Rahmadika & Rhee, 2019), and higher total execution time (Kuo et al., 20”
From Blockchain in healthcare: A systematic literature review, synthesizing framework and future research agenda (2020) · cites this paper as Griggs et al., 2018
In the citing paper
“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 Griggs et al., 2018
In the citing paper
“Five of the publications addressed the m-health domain and patientcontrolled collection, storage and sharing of sensor data [23,27,35,39,32].”
From Blockchain in healthcare and health sciences—A scoping review (2020) · cites this paper as [39]
In the citing paper
“the Ethereum consensus consumes high power at MEC.”
From Blockchain leveraged decentralized IoT ehealth framework (2020) · cites this paper as [33]
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Metadata
- Authors
- Griggs, K. N., Ossipova, O., Kohlios, C. P., Baccarini, A. N., Howson, E. A., & Hayajneh, T.
- Affiliations
- Fordham University (US)
- Corresponding author
- Hayajneh, Thaier
- Venue
- Journal of Medical Systems
- Published
- 2018-06-06
- Volume
- 42
- Issue
- 7
- Pages
- 130-130
- ISSN
- 0148-5598 (ISSN-L)
- 1573-689X
- OpenAlex
- W2805383889
- Cited by
- 926
- References
- 8
- Citations by year
- 2026: 43 · 2025: 75 · 2024: 97 · 2023: 160 · 2022: 179 · 2021: 174 · 2020: 116 · 2019: 73 · 2018: 9
- FWCI
- 150.08
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Journal of Medical Systems (publishedVersion)
- PubMed (publishedVersion)
- 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 › Information Systems › Blockchain Technology in Education and Learning
- Keywords
- blockchain, smart contracts, remote patient monitoring, Internet of Things, biomedical sensors, protected health information, data security, Ethereum, HIPAA compliance, real-time patient monitoring, notification delivery, medical data integrity
- MeSH terms
- Automation, Delivery of Health Care, Humans, Information Systems, Monitoring, Physiologic, Software, Computer Security, Internet
About these checks
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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