International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 06 | June 2022
p-ISSN: 2395-0072
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A Review on Biometric-based Systems for Patient Health Record Authentication Arya J Nair1, Sandhya S2, Sreeja Kumari S3 1,2,3 Lecturer, Dept. of
Computer Engineering, NSS Polytechnic College, Kerala, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract –Health records kept on paper have largely been
medical data duplication, and identifies patients. Despite being a potential use case, patient matching among healthcare providers is still not extensively used [4].
replaced by electronic versions in the worldwide health sector. Now, patients, physicians, and healthcare providers have easier access to information about their health. Having access to personal health records raises privacy, confidentiality, and security concerns. This emphasizes the need for reliable and powerful identification. Using biometrics to identify patients can prevent duplication of medical records and detect fraud while increasing patient safety. The field of biometrics deals with finding ways to evaluate and use a person's physical and behavioral characteristics for identification and verification. A review of biometrics-based methods for patient identification and data security is provided in this paper.
A biometric-based system facilitates the maintenance of health records [5]. Patients are not required to bring any documents, prescriptions, or other items. The management of patient records is also made simpler for the practitioner. Doctors can read, edit, and remove patient medical records because of these technologies. The doctor can lessen the likelihood of records becoming mixed up by entering prescriptions and checking past data. Without the consent of both the patient and the doctor, a doctor is not permitted to make changes to a patient's records.
Key Words: Intelligence Quotient (IQ), Magnetic Resonance Image (MRI), Electroencephalogram (EEG), Convolutional Neural Network, Support Vector Regression (SVR), Small Visual Geometry Group (SVGG), Visual Geometry Group (VGG), Residual Network (ResNet), Artificial Neural Network (ANN)
This article examines multiple biometric-based patient identification systems. In Chapter 3, the different methods of identification are discussed. The comparison of the various approaches is shown in Chapter 4. The study's conclusion is presented in Chapter 5.
1. INTRODUCTION
2. LITERATURE REVIEW
Over the past few years, the deployment of computer technology (CT) to improve health care services has proved to be highly beneficial [1]. Having electronic health records has helped hospitals reduce paperwork and alleviate the shortage of healthcare workers. It is essential to employ trusted technology for storing and retrieving records that allow users to quickly authenticate themselves. With biometric authentication technologies, patient healthcare data can be accessed more easily and transmitted securely. A biometric identification system uses physical and behavioral features to identify someone, whereas a biometric authentication system verifies the authenticity of an individual. Physical identification methods [2] utilize invariable physiological characteristics, such as face shape, fingerprint, retina, iris, DNA, etc. Behavioral identification methods consider the characteristics inherent in each individual as they repeat behavior which includes signature, gait, etc.
The fingerprint scanner is used by the fingerprint-based patient authentication system to access the database and collect patient information [6]. The fingerprint is thought to be the most accurate and efficient biometric identification approach is thought to be a fingerprint. Everybody has a different fingerprint, and they do not alter over time. Users of this system will always have access to healthcare-related information. The face-based system in [7] consists of a Raspberry Pi 3 processor and a Webcam for acquiring the face image of the patient. The facial attributes are extracted using the Local Binary Pattern (LBP) and Haar Cascade Algorithm. The classifier gets the extracted attributes, compares them to the ones it has learned, and then displays the data and reports that were saved in the database. An iris-based cancelable biometric cryptosystem has been proposed in [8] to securely store patient medical information on smart cards. The information is then encrypted with symmetric-key cryptography and the encrypted data is then stored on the smart card. A fuzzy commitment technique is used to link the patient's revocable iris template and the secret encryption key. By utilizing the iris pattern of the smart card holder, this suggested system offers user
Medical biometrics refers to the use of biometrics in clinics, hospitals, or for patient monitoring. This might involve managing the workforce, controlling access, identifying people, or storing medical records [3]. Most hospitals use biometrics to identify their patients and personnel. It improves the workflow of the healthcare system, reduces
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