TO COMPLY WITH THE REGULATORY STANDARDS SET FORTH BY EUROPEAN AND US AUTHORITIES, ALL OUR DATA IS PROPERLY ANONYMIZED

Data anonymization, data storage and data transfer are regulated by GDPR in the EU and HIPAA in the US. A good example of this approach is the Safe Harbor standard in the HIPAA Privacy Rule. It specifies 18 data elements that need to be removed or encrypted. If this is done properly, the data is considered anonymized in accordance with HIPAA.

This list includes:

Names of patients, nurses, doctors; Geographic locations; All elements of dates (except the year) that are related to an individual; Telephone, cellphone, and/or fax numbers; Email addresses; IP addresses; Social Security Numbers; Medical record number; Health plan beneficiary numbers; Device identifiers and serial numbers; Certificate/license numbers; Account numbers; Vehicle identifiers and serial numbers, including license plates; Website URLs; Full-face photos; Biometric identifiers (e.g., fingerprints, voice prints, and retinal images); Any unique identifying numbers, characteristics, or codes.

For image data anonymization, specific de-identification tools are used. After anonymisation, all image data goes through a quality control process to confirm that no personal identifiers are left.

Video data is instrumental in medical research, education, and training. Anonymizing this data ensures that researchers can access valuable information without compromising patient privacy. Medical professionals can use anonymized videos for training purposes, allowing them to hone their skills without exposing patient identities, or for using this data to develop AI-assisted software.

Artificial intelligence and machine learning algorithms are increasingly being employed to automate the anonymization process, making it more efficient and accurate. At medDARE, we’ve created a video anonymization tool designed to automatically identify faces of both patients and personnel, even those wearing masks or lying on a bed. This software goes beyond facial recognition by blurring all screens captured in the video.

Users have the flexibility to choose between blur or blackening for the anonymization process. To ensure thorough anonymization, we’ve integrated a quality control process that verifies every frame, ensuring none are overlooked or left without proper anonymization.