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Biometric
in Healthcare
Biometric Security in Healthcare Management System
Medical data theft is a serious problem in the healthcare sector. Millions of Americans are victims of medical data theft, according to a study conducted by the Ponemon Institute. To provide security for critical medical records in healthcare management systems, secure data access is required. The biometric authentication mechanism provided by EKEMP is suitable for this situation, it overcomes the limitations of id theft and forgotten password in the id-password mechanism used to provide security.
EKEMP's biometric devices have powerful functions including signature, fingerprint, iris scan and more. The medical data management system ensures the security of electronic medical data access based on the authentication of behavioral biometric signatures.
Secure Biometric Architecture
Face
Recognition
Authenticating
Signature
Upload Biometric Authentication Data
Importing Data into Healthcare Data Central
Health
Data Store
Biometric Devices for Healthcare Management
EKEMP biometric tablets help improve safety, efficiency and security measures within healthcare facilities, playing a vital role in healthcare management systems. Examples include fingerprint scanning, iris scanning and facial recognition. These technologies can securely and accurately identify individuals within healthcare settings. Biometric authentication provides a reliable method of ensuring that only authorized personnel have access to sensitive medical data.
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Aims to Provide Safe Healthcare Management System
EKEMP Biometric Authentication
The system uses biometric signatures for authentication. The dynamic characteristics of the signature were captured using a biometric tablet (such as VERSAT) that recorded features such as x, y coordinates, pen speed, total time to sign, angle of the pen at the time of signing, number of strokes and strokes, and acceleration. When a signature is captured, its important features are extracted, then preprocessed and stored as a template. Templates refer to dynamic feature datasets extracted from signature samples.
After this stage, this feature dataset is preprocessed and used to train an artificial neural network based model, and these trained networks are then stored in a medical database. During the verification phase, the user's signature is checked against the stored model to verify whether the user is real or fake. Also, another added advantage of this approach is that processing (training and validation) is also done on this biometric machine (VERSAT). This saves storage and costs while reducing the carbon footprint, i.e. it is an energy efficient method as it utilizes mobile and handheld biometric devices such as tablets and phones for access.
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