How To Tackle the Data Challenges of Pharmacovigilance?

How To Tackle the Data Challenges of Pharmacovigilance?

By Pharma Tech Outlook | Monday, November 11, 2019

Cognitive computing can transform the practice of pharmacovigilance, from a tedious, resource-intensive process to a dynamic and efficient method focusing on risk management.

FREMONT, CA: As pharmacovigilance deals with the activities relating to the detection, understanding, assessment, and prevention of adverse effects of pharmaceutical products, it has to navigate through a large volume of complex data. It cannot be avoided for its complex nature because pharmacovigilance audit accesses the compliance of pharma companies with worldwide laws, regulations, and FDA guidance. There arises a demand for handling enormous data by remaining compliant with the changing regulations globally while maintaining and improving the information contained in the individual case safety reports. The cost of handling pharmacovigilance is increasing with the exponential growth of cases received by pharmaceutical companies.

The technical advancement like cloud-based solutions, mobile health devices, artificial intelligence, blockchain, and machine learning can improve the effectiveness of PV and the efficacy of drugs. They are the gateway for new methods of processing and collecting data. Cognitive computing has been influencing all the industries, and medical sciences is not an exception. It comprises of various technologies like machine learning, natural language processing (NLP), speech recognition, and image recognition.

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Cognitive computing can automate several activities like manual and routine tasks associated with case management in pharmacovigilance. Their algorithms utilize the vast database containing data related to marketing, observational, and epidemiological studies for training themselves and perform various pharmacovigilance tasks. It enables successful integration to overcome technical, medical, and regulatory limitations. This resolves the ethical concerns regarding the safety and integrity of data.

Cognitive computing plays a significant role in personalized medicine, where the sheer knowledge of data is required for streamlining patients allowing them to make decisions on the optimization of their personal health in light of their lifestyle choices. Their impact on pharmacovigilance operations is vast, including automating manual and repetitive tasks, detecting complex patterns, generating information, and making predictions and recommendations. Employing these tools can greatly assist pharmacovigilance staff, reduce the volume of manual and repetitive tasks, and allow them to concentrate on more intelligent tasks. The pharmaceutical industry should readily adopt technology and stay abreast with the evolving needs of the sector.

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