With the growing need for data transparency, it is critical to ensure that patient privacy is maintained without limiting the usability of the data. BDLS’s experienced team understands that the appropriate level of de-identification can vary depending on many factors, such as the planned use of the data, the rarity of the disease under study, and the study design.
1. A “point-and-click” application that requires no programming language to learn or code to write.
2. A suite of SAS macros allowing clients to upload and administer within their own computing environment.
3. The option to use our in-house BDLS data standards team as a resource solution to your organisation.
De-I.D. works with any clinical data, but the greatest efficiencies are available when using standardized CDISC data sets. The BDLS approach uses the principle of 7 de-identification functions which can be applied across the clinical trial data but more principles can be added in consultation with a client. BDLS understands the balance between reviewing historically proprietary clinical trial data and the need to protect patient privacy.
Greater standardisation across the healthcare industry has allowed industry professionals to focus more on important clinical issues – this in turn leads to important, sometimes very subtle, differences between clinical trials.
BDLS has been working with 2 of the world’s top 10 pharma companies and has launched the De-I.D. solution for de-identifying clinical trials.
- provide expert consultancy services to help Life Sciences organizations determine an appropriate level of de-identification
- review the protocol, SAP, CSR, clinical study data and intended use for the de-identified data to determine which variables or records contain information that could potentially identify a patient
- develop a de-identification plan based on the metadata for each individual clinical studies
- fully document the de-identification functions that will be applied to the applicable variables and records
- a metadata-driven approach automates the application of the specified de-identified methods
- generation of an updated define.xml for the de-identified data that includes a description of the function used for each variable or record
- destroy all elements of the seed keys and commendation to ensure complete anonymization of the patent data
With the growing need for data transparency, this service helps our clients de-identify their patient data so that the results can be shared with rese...EMA | data transparency
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