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In pharmacovigilance, NLP can help distinguish events, outcomes, and risk factors using sources such as labels, EMRs, manufacturer's websites, patient forums, and other online groups, e-mails, scientific literature, and others.
The less visible debate on digital transformation in pharmacovigilance can be explained by two key factors: the field's niche nature and the highly controlled nature of the field. However, with an intensified emphasis on rapid drug approvals and a vast amount of data produced daily, it is not easy to believe that pharmacovigilance will remain on the fringe of digital transformation.
Today, one can identify key technologies that impact the pharmacovigilance strategy, including:
In healthcare, big data refers to the large and increasing amount of computerized medical information available in electronic health records, administrative or health claims data, disease, and drug tracking registries. For years, all kinds of medical knowledge have been collected without understanding its importance and future use. The emergence of new powerful computing tools that can process and analyze vast amounts of information has brought big data into focus—it can now be used for predictive purposes.
When it comes to pharmacovigilance, big data contains sources such as:
• Detection of signals.
• Substantiation and confirmation of safety signals for medicines or vaccines.
• Internet and social media networks.
Natural Language Processing
Natural Language Processing (NLP) is a branch of artificial intelligence that allows computers to understand, perceive, and manipulate human language. Life Sciences companies need to filter high amounts of noise if they want to remain behind all the post-marketing safety signals that appear through the Internet and digital resources. If they struggle to keep up with it, up to 17 percent of the adverse events will be missed.
In pharmacovigilance, NLP can help distinguish events, outcomes, and risk factors using sources such as labels, Electronic Medical Records (EMRs), manufacturer's websites, patient forums, and other online groups, e-mails, scientific literature, and others. Currently, the option of NLP tools to efficiently filter safety signals is very small. There are two critical choices for businesses. The first is the acquisition of a proprietary analytical tool, and the second is the modification of an existing tool, such as a social listening tool.
Cloud is a technology that enables an organization to benefit from the ability to store and analyze vast volumes of data. Among the key drivers for switching to the cloud are:
• Cost and Efficiency: The cloud will allow businesses to deal with a vast volume of data on a case-by-case basis without compromising quality, security, and privacy.
• Scalability: The adverse event case burden for life science companies has gradually increased, with some firms experiencing an annual rise of 50 percent. This increase calls for technology that can rapidly handle the increasing volume of data.
• Simplicity: Cloud use will make it simpler for businesses by eliminating questions about the compatibility of modules and scaling up servers.
T rends, such as artificial intelligence, wearables, and patient centricity, will further increase the amount and range of data to be evaluated to make the most informed decisions on drug benefit-risk profiles. As a result, the cloud in pharmacovigilance is gradually becoming a reality.
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