OneMedNet, a curator of regulatory-grade real world data (RWD), has integrated AI technology into de-identification processes to increase efficiency.

As a result of the integration, the company has streamlined the de-identification of patient data and reduced operational costs.

The technological enhancement allows OneMedNet to process large volumes of clinical data in minutes, a task that previously took hours.

With the introduction of AI, the company not only optimises accuracy but accelerates the delivery of regulatory-grade, de-identified datasets to Life Science organisations.

This has resulted in up to a fivefold increase in speed for the end-to-end data curation process, significantly improving service delivery times.

OneMedNet CEO and president Aaron Green said: “The integration of AI into our de-identification processes not only enhances our data privacy but also significantly boosts our operational efficiency, allowing us to quickly provide high-quality, de-identified datasets to our customers.

“Furthermore, AI has enabled us to respond to sales inquiries much faster, improving our customer service and overall business performance.”

The AI-driven de-identification process is a key feature of the company’s iRWD platform, which is designed to provide secure, comprehensive management of clinical data types, including laboratory results, electronic health records, and medical imaging.

This platform is intended to securely de-identify, search, and curate the clinical data to its medical device, drug and imaging/diagnostic AI development customers.

It is said to meet the clinical requirements across various domains, including oncology and rare diseases.