The healthcare industry is one of the areas most vulnerable to identity theft, with many cases recorded over time and space regarding the magnitude and frequency of data infringement. Data breaches in hospitals involve records theft, which is more than 70 percent of the cases, the stolen information is often used for identity theft or fraud and may contain items such as Social Security numbers, credit card information, and birth dates among others.
According to the year 2022, breaches of healthcare organization data hit 49. If one of the attacks were to occur today, delaying its effects would be impossible: cyberattacks affect 6 million Americans and rank as one of the most threatened industries. Thus, healthcare sectors demand the best face recognition system that excludes identity theft and patient mismatching.
The impact of identity theft within the theme of this article aims to capture the effects of patient misidentification in healthcare facilities.
What is Face Recognition Technology?
Facial recognition is a practice in identification that has over the years brought changes to the identification process in various fields. It can verify a person’s identity by matching his face from an image or a frame of a video against a face database. As compared to the fingerprint scanners, it is believed to be less accurate mainly because it doesn’t come into physical contact with people and can be scanned many feet away from the sensors. It is employed in banks, insurance and health departments, and other organizations for which recognition of certain regulations is essential.
In addition, face recognition is combined with AI and ML frameworks of convolutional neural networks. This mechanism is generally involved in the process of feature extraction and comparing them with the ones in the database.
The Efficient Functionality of Face Verification
The type of technology used can either be artificial intelligence-based or biometric face recognition. The process with both approaches takes place as follows: The process with both approaches takes place as follows:
Face Detection: It mainly incorporates detection algorithms to identify facial parts from a picture or typical portable video picture.
Face Alignment: They make sure that all faces are of the right size and are oriented in the right direction which eases the process.
Feature Extraction: The framework involves extraction of the facial images and feeding each of them into a CNN model to come up with a feature model.
Database Storage: It compares the feature vectors to future input calls and it also stores the feature vectors in a database.
Face Matching: It retrieves the similarities between the feature vectors extracted and the ones that are stored in the database.
Verification: It confirms a person or reacts to their face by matching it to the saved data and also gives the resultant verdict by either allowing access or not.
Online Face Recognition To Dissuade Identity Theft
There is also a growing problem of identity theft in the SOC of the healthcare system. To do this, the people acquire other people’s identities or, more often, use the genuine arrangement to pay for insurance reimbursement and other promotion offers in the hospital which are for the insured only. Even though it is cheaper when you have health coverage, otherwise it is very expensive to even be treated, get tested, or even get a scan for even the simplest of complications.
Concerning some of the identified risks at the hospital, online face recognition is common in discouraging fraud and other related risks. In addition, it assists in reducing the cases of fraud insurance claims, and this makes hospitals offer their services to deserving individuals only. It also enhances the belief between insurance firms, hospitals, and individuals or patients.
Biometric Face Verification for Patient Misidentification
Face recognition technology benefits the healthcare system’s operations by maintaining its reliability through preventing patient identity confusion, which may affect treatment delivery. The problem of patient identification can be described as critical since it entails the killing of needy individuals. These problems are common in most hospitals, and the organizations end up damaging their image and incurring hefty fines. Hence, they hugely depend on biometric face recognition to identify the patients on the spot on admission, as well as before administering any treatment.
The Role and Advantages of Facial Liveness Detection
Its role in the healthcare system is to determine live faces online. This is an additional measure that prevents swindlers from standing before the scanners and using identities to obtain benefits and enrich themselves. This technology is combined with face identification to check that there is no spoof and still image to recognize. Below are some advantages that can be attributed to the face recognition scanner:
Enhanced Security: Effectively controls access rights of the patient, staff, and other visitors to eliminate unauthorized access.
Accurate Patient Identification: Eliminates mistakes that may be made in a patient’s records as well as in the treatment that he/she is being administered.
Efficient Check-ins: Reduces the amount of time taken to check in patients.
Contactless Access: Reduces physical touch, which helps in the prevention of the spreading of infection.
Fraud Prevention: Helps to stop cases of identity theft, especially in public hospitals, and cases of insurance fraud.
Access Control: Restricts the access of only those employees who by their roles, need to access such areas.
Personalized Care: Enables the healthcare givers to make quick consultations hence offering personalized treatments based on the records.
Regulatory Compliance: Facilitates the compliance of data privacy and security standard requirements.
Wrapping it Up
The mechanisms of face recognition in the healthcare industry provide a revolutionary approach to the issue of identity theft and patient identification errors. This would mean that hospitals can greatly improve the organization’s security, make reception quicker and more effective, and offer assurance in the biometric ID of a patient through the implementation of AI and biometric systems. This helps to avoid such financial repercussions as can be as a result of misidentification, and encourage patient, insurance company, and healthcare provider trust. The application of strong face recognition systems is relevant to safeguard healthcare data and the credibility of provided services due to constant cyber threats.
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