Context-Based, Predictive Access Control to Electronic Health Records

Effective access control techniques are in demand, as electronically assisted healthcare services require the patient’s sensitive health records. In emergency situations, where the patient’s well-being is jeopardized, different healthcare actors associated with emergency cases should be granted perm...

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Bibliographic Details
Published in:Electronics (Basel) 2022-10, Vol.11 (19), p.3040
Main Authors: Psarra, Evgenia, Apostolou, Dimitris, Verginadis, Yiannis, Patiniotakis, Ioannis, Mentzas, Gregoris
Format: Article
Language:English
Subjects:
Access control
Artificial intelligence
Blockchain
Context
Control methods
Customization
Data encryption
Decision making
Electronic health records
Emergency medical services
Health care
Health care industry
Health services
Information management
Machine learning
Medical equipment
Medical records
Neural networks
Patients
Personal health
Predictive control
Privacy
Professionals
Technology application
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Summary:Effective access control techniques are in demand, as electronically assisted healthcare services require the patient’s sensitive health records. In emergency situations, where the patient’s well-being is jeopardized, different healthcare actors associated with emergency cases should be granted permission to access Electronic Health Records (EHRs) of patients. The research objective of our study is to develop machine learning techniques based on patients’ time sequential health metrics and integrate them with an Attribute Based Access Control (ABAC) mechanism. We propose an ABAC mechanism that can yield access to sensitive EHRs systems by applying prognostic context handlers where contextual information, is used to identify emergency conditions and permit access to medical records. Specifically, we use patients’ recent health history to predict the health metrics for the next two hours by leveraging Long Short Term Memory (LSTM) Neural Networks (NNs). These predicted health metrics values are evaluated by our personalized fuzzy context handlers, to predict the criticality of patients’ status. The 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 with personalized context handlers proved to be a robust tool to enhance the performance of the access control mechanism to modern EHRs System.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics11193040