Beyond Redaction: Protect the privacy of study participants

Clinical trials produce data and reports that sponsors must share with the public. With d-wise Clinical Transparency solutions share clinical trial data and study reports with regulatory agencies and academia while protecting study participants privacy

Anonymization of Data & Documents at Quality & Scale

d-wise Clinical Data Transparency solutions provide either turnkey software solutions or a full-range of de-identification services to outsources data sharing and document anonymization needs.

d-wise clinical trial transparency partners have led the delivery of over 5,500 redacted documents and supported development and completion of more than 30 EMA Policy 0070 submissions. Our team includes members of the EMA Technical Anonymization Group, the Health Canada Stakeholder Reference Group on Public Release of Clinical Information, and the lead of the Phuse Working Group on Data Transparency.

De-ID as a Service:
Outsource Anonymization of Data & Documents

Completely outsource the processes, validation and risks involved with stricter and consistently changing regulatory policies for bringing medicine to market. New policies vary globally and are much more complex than previous requirements. Partner with d-wise to simplify the CSR and data anonymization process, while having experienced subject matter experts support your overall strategy.

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de-ID Service

Blur Software: Anonymizes Trial Data and Documents Efficiently and Compliantly

Blur is the industry-leading software solution for anonymizing clinical trial data sets and clinical study documents on-site in partnership with multiple globally pharmaceutical companies. Blur removes many manual processes so Life Science companies spend less time and money writing code or manually reviewing thousands of CSR pages. Developed by clinical trial experts at d-wise with deep knowledge of transparency and regulations, Blur provides the unique approach of simulating all the ways data can be anonymized and allow risk to drive the anonymization. This always putting patent privacy at the forefront while illustrating the underlying data utility of each simulation allowing you to choose the highest data utility simulation within reasonable risk thresholds.