TripleBlind Data De-Identification Solution Confirmed as HIPAA Compliant by World Renowned Expert, Dr. Bradley Malin, PhD

Company’s Cryptographic Approach and Technology Architecture Satisfies HIPAA’s Expert Determination Method for De-Identification and All State-Level Data Protection Acts

TripleBlind, the leader in automated, real-time data de-identification, announced the completion of an assessment and determination by Dr. Bradley Malin, PhD. This determination confirms that TripleBlind’s automatic real-time de-identification solution is fully compliant with the Health Insurance Portability and Accountability Act (HIPAA) and meets the requirements of all state-level data protection acts.

Dr. Bradley Malin, Accenture Professor of Biomedical Informatics, Biostatistics, and Computer Science, as well as Vice Chair for Research Affairs in the Department of Biomedical Informatics at Vanderbilt University, is one of a handful of independent experts with the authority to review technologies and determine whether or not they meet HIPAA compliance. While a majority of determinations focus on a singular dataset, Dr. Malin’s expert determination method covers the entire process of de-identification.

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“Healthcare organizations have an enormous amount of data but have not had the tools to analyze data to positively impact patient outcomes while maintaining compliance,” said Riddhiman Das, Co-founder and CEO of TripleBlind. “We’ve dedicated countless hours to ensure our solution maintains privacy of all patient data to meet and exceed all standards set out by HIPAA. Dr. Malin’s official determination of HIPAA compliance will make it much easier for our current and prospective healthcare customers to rest assured they are in compliance so they can focus on what really matters – optimizing genetic research, pharmaceutical research and patient care.”

According to the World Economic Forum (WEF), hospitals produce 50 petabytes of data – 1 petabyte is estimated to equal 500 billion pages of standard printed text – per year on average. With so much data being collected by hospitals and other healthcare organizations, there is a major risk for misuse or inadequate protection of that data. HIPAA was designed to solve that challenge and protect patient health data but as a result, much of that data goes unused with existing technologies not able to access the data in a meaningful way for research, trials and other analysis.

With TripleBlind, organizations do not need to de-identify patient health information (PHI) prior to deploying models, ensuring the data is available for analysis without putting the data at risk. De-identified data is available at the speed of installation meaning newly collected data can be instantly made available for collaboration, skipping the laborious manual processes previously required to prepare data for safe use. Automatic de-identification occurs at the time of use and works on structured data or tabular data, but also on unstructured data, like images.

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Dr. Malin is one of just a few experts in the United States with the authority to assess and determine HIPAA compliance. Dr. Malin evaluates organizations focused on the following areas to determine whether or not the technology will comply according to regulations:

• Deployment of models on data that may contain patient health information;
• TripleBlind’s Blind Learning and query solutions to train models and derive insights;
• TripleBlind’s ability to support the above activities without the need for de-identifying data prior to engaging with TripleBlind.

Following the assessment, Dr. Malin confirmed, “The data protection methods for data passing through the TripleBlind solution, meets the de-identification requirements set forth in the HIPAA Privacy Rule and state-level consumer data protection acts, such as CCPA. This is true for the advanced statistical analytics that are performed on the data, as well as the machine learning algorithms that are learned over the data.”

The assessment by Dr. Malin follows the recent evaluation of TripleBlind’s solution by MITRE Engenuity, a not-for-profit organization that works in the public interest across federal, state and
local governments, as well as industry and academia. MITRE’s findings verify that conclusion followed a detailed technical evaluation of the underlying de-identification technology.

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