The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
A Novel Smart Healthcare Design, Simulation, and Implementation Using Healthcare 4.0 Processes
Open accessAbstract
Blockchain technology is found to have its applicability in almost every domain because of its advantages such as crypto-security, transparency, immutability, decentralized data network. In present times, a smart healthcare system with a blockchain data network and healthcare 4.0 processes provides transparency, easy and faster accessibility, security, efficiency, etc. Healthcare 4.0 trends include industry 4.0 processes such as the internet of things (IoT), industrial IoT (IIoT), cognitive computing, artificial intelligence, cloud computing, fog computing, edge computing, etc. The goal of this work is to design a smart healthcare system and it is found to be possible through integration and interoperability of Blockchain 3.0 and Healthcare 4.0 in consideration with healthcare ground-realities. Here, healthcare 4.0 processes used for data accessibility are targeted to be validated through statistical simulation-optimization methods and algorithms. The blockchain is implemented in the Ethereum network, and with associated programming languages, tools, and techniques such as solidity, web3.js, Athena, etc. Further, this work prepares a comparative and comprehensive survey of state-of-the-art blockchain-based smart healthcare systems. The comprehensive survey includes methodology, applications, requirements, outcomes, future directions, etc. A list of groups, organizations, and enterprises are prepared that are working in electronic health records (EHR), electronic medical records (EMR) or electronic personal records (EPR) mainly, and a comparative analysis is drawn concerning adopting the blockchain technology in their processes. This work has explored optimization algorithms applicable to Healthcare 4.0 trends and improves the performance of blockchain-based decentralized applications for the smart healthcare system. Further, smart contracts and their designs are prepared for the proposed system to expedite the trust-building and payment systems. This work has considered simulation and implementation to validate the proposed approach. Simulation results show that the Gas value required (indicating block size and expenditure) lies within current Etherum network Gas limits. The proposed system is active because block utilization lies above 80%. Automated smart contract execution is below 20 seconds. A good number (average 3 per simulation time) is generated in the network that indicates a health competition. Although there is error observed in simulation and implementation that lies between 0.55% and 4.24%, these errors are not affecting overall system performance because simulated and actual (taken in state-of-the-art) data variations are negligible.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
McCabe, T. J. (1976) · IEEE Transactions on Software Engineering · doi:10.1109/tse.1976.233837 · Cited by 6,059
An Overview of Blockchain Technology: Architecture, Consensus, and Future Trends
Zheng, Z., Xie, S., Dai, H., Chen, X., & Wang, H. (2017) · 2017 IEEE International Congress on Big Data (BigData Congress) · doi:10.1109/bigdatacongress.2017.85 · Cited by 4,515
Yue, X., Wang, H., Jin, D., Li, M., & Jiang, W. (2016) · Journal of Medical Systems · doi:10.1007/s10916-016-0574-6 · Cited by 1,240
A Survey of Blockchain Security Issues and Challenges
Lin, I., & Liao, T.-C. (2017) · International journal of network security · doi:10.6633/ijns.201709.19(5).01 · Cited by 999
Blockchain-based electronic healthcare record system for healthcare 4.0 applications
Tanwar, S., Parekh, K., & Evans, R. (2020) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2019.102407 · Cited by 944
Healthcare blockchain system using smart contracts for secure automated remote patient monitoring
Griggs, K. N., Ossipova, O., Kohlios, C. P., Baccarini, A. N., Howson, E. A., & Hayajneh, T. (2018) · Journal of Medical Systems · doi:10.1007/s10916-018-0982-x · Cited by 926
Blockchain in healthcare applications: Research challenges and opportunities
McGhin, T., Choo, K.-K. R., Liu, C. Z., & He, D. (2019) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2019.02.027 · Cited by 866
Blockchain: A Panacea for Healthcare Cloud-Based Data Security and Privacy?
Esposito, C., De Santis, A., Tortora, G., Chang, H., & Choo, K.-K. R. (2018) · IEEE Cloud Computing · doi:10.1109/mcc.2018.011791712 · Cited by 855
Blockchain Technology for Healthcare: Facilitating the Transition to Patient-Driven Interoperability
Gordon, W. J., & Catalini, C. (2018) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2018.06.003 · Cited by 828
A Systematic Review of the Use of Blockchain in Healthcare
Hölbl, M., Kompara, M., Kamišalić, A., & Nemec Zlatolas, L. (2018) · Symmetry · doi:10.3390/sym10100470 · Cited by 780
Cited by
Medical 4.0 technologies for healthcare: Features, capabilities, and applications
Haleem, A., Javaid, M., Pratap Singh, R., & Suman, R. (2022) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2022.04.001 · Cited by 468
The role of blockchain technology in telehealth and telemedicine
Ahmad, R. W., Salah, K., Jayaraman, R., Yaqoob, I., Ellahham, S., & Omar, M. (2021) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2021.104399 · Cited by 282
Review on Federated Learning for digital transformation in healthcare through big data analytics
Babar, M., Qureshi, B., & Koubaa, A. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.05.046 · Cited by 55
An Approach to Binary Classification of Alzheimer’s Disease Using LSTM
Salehi, W., Baglat, P., Gupta, G., Khan, S. B., Almusharraf, A., Alqahtani, A., & Kumar, A. (2023) · Bioengineering · doi:10.3390/bioengineering10080950 · Cited by 42
Self-citation
Metadata
- Authors
- Kumar, A., Krishnamurthi, R., Nayyar, A., Sharma, K., Grover, V., & Hossain, E.
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 118433-118471
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3037093268
- Cited by
- 293
- References
- 44
- FWCI
- 38.82
- Type
- journal article
- Language
- en
- Open access
- gold
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 ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A Novel Smart Healthcare Design, Simulation, and Implementation Using Healthcare 4.0 Processes. https://app.cochat.ai/papers/10.1109/access.2020.3004790
- The paper
- Kumar, A., Krishnamurthi, R., Nayyar, A., Sharma, K., Grover, V., & Hossain, E. (2020). A Novel Smart Healthcare Design, Simulation, and Implementation Using Healthcare 4.0 Processes. IEEE Access. https://doi.org/10.1109/access.2020.3004790
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.