A quantitative and metrics driven framework for maximizing data utility while balancing re-identification risk. Implement a structured, high efficiency and proven approach to incorporate quantitative risk and data utility methodologies.
Traditional redaction approaches do not support data utility. Health Canada and EMA are expecting a quantitative risk modeling methodology for new submissions. Maximize data utility while minimizing re-identification risk using RLS Protect data scenario analysis.
Balancing the need to protect the patient’s privacy with a need to maintain data utility is a difficult task without technology. Encourage innovation by supporting transparency and data sharing initiatives with the confidence that patient data is protected and data usefulness is retained. Enable secondary research bodies to take things to the next level while protecting patient data and identity
RLS Protect delivers repeatable automated workflows to batch process, model and anonymize datasets and documents saving you large blocks of time. Avoid highly manual processes that are difficult to quality control. Minor adjustments to business rules allow you to systematically re-process datasets for regulatory submission and voluntary disclosure –for example, adhering to different risk thresholds based on how the data will be consumed and by whom.
Use MD-DUO to take subjectivity out of the equation and provide a definitive set of guidelines from which to base disclosures. Provide regulators with objective data they can rely upon to make clear decisions. Inform Health Canada and EMA ahead of time to accelerate your regulatory disclosure timeline.
Health Canada and the European Medicine Association require Sponsors to disclose clinical trial results. This includes providing an explanation of how personal information was anonymized so the identity of trial participants remains confidential while the trial results are made public.
Historically, Sponsors simply redacted information pertaining to study results. While redaction keeps personally identifiable information confidential, redaction renders otherwise useful attributes about that data unusable -- for example, a study participant's age, weight or other commorbitities which are relevant to trial results. A quantitative modeling approach uses advanced statistical methods to de-identify the information while maintaining the utility of the data. This allows the information to be useful for the reader of the study results and meaningful for purposes of secondary research by academic institutions and other researchers.
Regulators and Sponsors will accept a measured amount of re-identification risk in order to maintain data utility. Quantitiative risk modeling provides a framework to let you know which data variables must be transformed, which can stay in their original form and which must be redacted to meet the required risk threshholds while balancing protection of personal information.
RLS Protect and RLS Managed Services support the following teams: Clinical Operations, Clinical Development, Biostats, Medical Writing, Regulatory & Trial Disclosure Teams.
RLS has extensive experience with disclosure of large and small clinical trials and those with large and very small patient populations. With rare diseases, a more conservative approach/risk threshold is considered (i.e. larger equivalence class sizes). Grouping our subjects in larger equivalence classes reduces the uniqueness of subject, ensuring we mitigate the risk of re-identification in rare studies.
RLS Protect is the industry leading "fit for purpose" solution to support data and document disclosure and anonymization requirements. RLS supports large and small Sponsors and CROs globally. Our leading edge system capabilities combined with regulatory knowledgeable Managed Services team has completed dozens of EMA and Health Canada submission projects and countless dataset processing and voluntary disclosure initiatives. To date we have processed over one million pages for Pharma organizations.
RLS Protect consists of two interoperable applications that are used to anonymize patient data. The Risk application generates a quantitative risk report, providing the sponsor with the risk metrics and most optimal transformation options to anonymize your data with the confidence that the required risk thresholds are being met while maintaining clinical utility . This report can then be loaded into the Docs application, automating all of the markings into their respective anonymized terms and values, thereby eliminating manual processes and increasing efficiency, accuracy and quality.