Converting Legacy Data to MedDRA: Approach and Implications
The adoption of the Medical Dictionary for Regulatory Activities (MedDRA) as the standardized international medical terminology presents industry with the enormous task of “MedDRA-sizing” legacy data. As a multifunctional, highly granular dictionary, MedDRA has the potential to facilitate the conversion process with minimal loss of specificity, and simultaneously allow for integration and standardization of all clinical, regulatory, and epidemiological information. In order to maximize the value and utility of the MedDRA conversion process, it is critical to involve users who have specific knowledge of MedDRA and understand the possibilities as well as the limitations of MedDRA in the planning and implementation stages so that specificity, consistency, and accuracy may be preserved.
This article will discuss, through the use of actual case studies, the issues and solutions in converting legacy data, whether the source is a home grown/in-house or a standardized dictionary to MedDRA. We conceived, established and continue to operate the Food and Drug Administration’s (FDA’s) Drug Safety Surveillance unit. Based on the insight and understanding attained from having used MedDRA to codify individual case safety reports for the FDA and converting legacy data for the pharmaceutical industry, the following issues have been identified as critical to the success of any legacy conversion project: defining the company’s objectives for data conversion, developing a conversion plan tailored to meet the specific objectives of the individual company, developing a MedDRA coding algorithm, identifying potential areas of conflict within MedDRA and proposing possible solutions, developing and implementing quality assurance processes, and modifying coding guidelines to address specific organization coding philosophies. In addition to sharing the lessons learned from having performed legacy data conversion, this article will also discuss the future impact of the legacy data conversion process on both data management and coding.
Key WordsMedDRA Legacy data Coding/classification conventions Quality assurance; Pharmacovigilance Autoencoders Autoencoding
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