Permissioned blockchain network for proactive access control to electronic health records
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| Publicado en: | BMC Medical Informatics and Decision Making vol. 24 (2024), p. 1 |
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| Autor Principal: | |
| Outros autores: | , , , |
| Publicado: |
Springer Nature B.V.
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| Materias: | |
| Acceso en liña: | Citation/Abstract Full Text Full Text - PDF |
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| 001 | 3126412645 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1472-6947 | ||
| 024 | 7 | |a 10.1186/s12911-024-02708-8 |2 doi | |
| 035 | |a 3126412645 | ||
| 045 | 2 | |b d20240101 |b d20241231 | |
| 084 | |a 58451 |2 nlm | ||
| 100 | 1 | |a Psarra, Evgenia | |
| 245 | 1 | |a Permissioned blockchain network for proactive access control to electronic health records | |
| 260 | |b Springer Nature B.V. |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a BackgroundAs digital healthcare services handle increasingly more sensitive health data, robust access control methods are required. Especially in emergency conditions, where the patient’s health situation is in peril, different healthcare providers associated with critical cases may need to be granted permission to acquire access to Electronic Health Records (EHRs) of patients. The research objective of this work is to develop a proactive access control method that can grant emergency clinicians access to sensitive health data, guaranteeing the integrity and security of the data, and generating trust without the need for a trusted third party.MethodsA contextual and blockchain-based mechanism is proposed that allows access to sensitive EHRs by applying prognostic procedures where information based on context, is utilized to identify critical situations and grant access to medical data. Specifically, to enable proactivity, Long Short Term Memory (LSTM) Neural Networks (NNs) are applied that utilize patient’s recent health history to prognose the next two-hour health metrics values. Fuzzy logic is used to evaluate the severity of the patient’s health state. These techniques are incorporated in a private and permissioned Hyperledger-Fabric blockchain network, capable of securing patient’s sensitive information in the blockchain network.ResultsThe developed access control method provides secure access for emergency clinicians to sensitive information and simultaneously safeguards the patient’s well-being. Integrating this predictive mechanism within the blockchain network proved to be a robust tool to enhance the performance of the access control mechanism. Furthermore, the blockchain network of this work can record the history of who and when had access to a specific patient’s sensitive EHRs, guaranteeing the integrity and security of the data, as well as recording the latency of this mechanism, where three different access control cases are evaluated. This access control mechanism is to be enforced in a real-life scenario in hospitals.ConclusionsThe proposed mechanism informs proactively the emergency team of professional clinicians about patients’ critical situations by combining fuzzy and predictive machine learning techniques incorporated in the private and permissioned blockchain network, and it exploits the distributed data of the blockchain architecture, guaranteeing the integrity and security of the data, and thus, enhancing the users’ trust to the access control mechanism. | |
| 653 | |a Emergency medical services | ||
| 653 | |a Security | ||
| 653 | |a Fuzzy logic | ||
| 653 | |a Health care | ||
| 653 | |a Patients | ||
| 653 | |a Blockchain | ||
| 653 | |a Predictive control | ||
| 653 | |a Digital currencies | ||
| 653 | |a Fuzzy control | ||
| 653 | |a Personal health | ||
| 653 | |a Health services | ||
| 653 | |a Privacy | ||
| 653 | |a Long short-term memory | ||
| 653 | |a Machine learning | ||
| 653 | |a Access control | ||
| 653 | |a Electronic medical records | ||
| 653 | |a Electronic health records | ||
| 653 | |a Data integrity | ||
| 653 | |a Robust control | ||
| 653 | |a Neural networks | ||
| 653 | |a Distributed ledger | ||
| 653 | |a Sensitivity analysis | ||
| 653 | |a Network latency | ||
| 653 | |a Trust | ||
| 653 | |a Information processing | ||
| 653 | |a Trusted third parties | ||
| 653 | |a Latency | ||
| 653 | |a Control methods | ||
| 653 | |a Integrity | ||
| 653 | |a Emergency procedures | ||
| 653 | |a Medical prognosis | ||
| 653 | |a Well being | ||
| 700 | 1 | |a Apostolou, Dimitris | |
| 700 | 1 | |a Verginadis, Yiannis | |
| 700 | 1 | |a Patiniotakis, Ioannis | |
| 700 | 1 | |a Gregoris Mentzas | |
| 773 | 0 | |t BMC Medical Informatics and Decision Making |g vol. 24 (2024), p. 1 | |
| 786 | 0 | |d ProQuest |t Healthcare Administration Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3126412645/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3126412645/fulltext/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3126412645/fulltextPDF/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |