Decentriq Data Clean Rooms for Healthcare
por dq technologies AG
Data clean rooms powered by Confidential Computing for sensitive health data analytics
A new generation of data collaboration to advance research
Decentriq is a Swiss enterprise SaaS platform providing data clean rooms that uniquely combine confidential computing and privacy enhancing technologies to enable multiple parties to leverage sensitive data in a “trust-free” environment.
About the offer
Our data clean rooms (DCRs) offer a data privacy-compliant and secure way for members of the health care ecosystem to collaborate without ever sharing their sensitive data or algorithms. Users can now combine sensitive inputs to uncover novel insights at unprecedented speeds, reducing inefficiencies for both data custodians as well as data analysts.
Up until now, collaboration users had to make tradeoffs between:
- Environments where a trusted party, eg. a Hospital or Life Science company, has control over all the data and the query. This requires the other collaborators to trust them and their promise to keep the inputs private and secure.
- Environments with limited or no matching capabilities, limiting the breadth or depth of insights that can be generated.
- Environments with complex or resource intense methods to protect data sensitivity - resulting in little analytical flexibility.
However, the risk of unauthorized access to patient medical records, data breaches, lack of proper data encryption and inadequate security measures leading to sensitive patient information being misused, mishandled and becoming public is still too great.
Who is this offer for
The Decentriq Data Clean Rooms for Healthcare is a solution for Hospitals, Researchers, Real World Evidence teams, Insurers, and others, who cannot afford to compromise security and privacy for analytical flexibility and ability to easily scale. Our low-code/no-code SaaS environment is designed for both data scientists and data custodians alike, with effortless set up in just 5-minutes. It enables advancements in clinical trial optimization, Real World Evidence programs, and quality of care evaluations.