Overcoming Data Gaps in Pharmacovigilance

As the use of machine learning (ML) in pharmacovigilance grows, so does the need for large and comprehensive data sets to ensure reliable and accurate models. However, obtaining and maintaining such data can be a challenge, as data gaps can arise for a variety of reasons. These gaps can have significant implications for the accuracy and effectiveness of ML models, and it is therefore important to explore new strategies for closing these gaps.

Data gaps can pose challenges to the development of reliable ML models in pharmacovigilance. There are various strategies that organizations can use to overcome these challenges, including active surveillance, using diverse and representative data sources, employing data augmentation techniques, and implementing data governance and management practices. By leveraging these strategies, organizations can effectively close data gaps and achieve more accurate and effective ML models in pharmacovigilance. Choosing the right technology partner is important when implementing data-driven technologies in pharmacovigilance and on the need for continuous monitoring, evaluation, and improvement of the models. Overcoming data gaps and achieving reliable ML models in pharmacovigilance can be done through a combination of data management, governance practices, and effective technology partnerships.

Read this article in Pharmaceutical Technology’s Quality and Regulatory Sourcebook eBook.

About the authors

Pankaj Bhardwaj serves as a senior product manager, and Ryanka Chauhan serves as a product manager; both at Datafoundry for DF mSignal AI.

Article details

Pharmaceutical Technology
eBook: Quality and Regulatory Sourcebook
March 2023
Pages: 12–16

Citation

When referring to this article, please cite it as Bhardwaj, P. and Chauhan, R. Overcoming Data Gaps in Pharmacovigilance. Pharmaceutical Technology Quality and Regulatory Sourcebook eBook. March 2023.