AI-Powered Literature Surveillance solution for Pharmacovigilance 
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Feb 8th, 2024
Five Reasons to Adopt an AI-Powered Literature Surveillance Solution for Pharmacovigilance 

In the rapidly evolving landscape of pharmacovigilance (PV), ensuring patient safety and health outcomes is non-negotiable. With the surge in post-marketing drug surveillance and safety concerns, literature monitoring has become pivotal. Further, with the AI revolution, the race to transform various aspects of Healthcare Management is at its peak. In the context of PV, let us explore the top 5 reasons to adopt an AI-powered tool for Literature Surveillance. 

1. Efficient Article Search and Ingestion: 

An Intelligent Search Engine powered by AI can make the article search and ingestion process highly efficient. The AI-driven system can utilise Machine Learning (ML) algorithms to fetch relevant articles from multiple open access scientific and medical literature databases, covering thousands of journals worldwide. This would ensure a consolidated view of PV-relevant articles from sources such as PubMed Central, BioMed Central, and Elsevier. With minimal user input, the algorithm can navigate this vast sea of data with precision and speed, providing maximum coverage while drastically reducing the risk of missing critical information. 

2. Automated Deduplication and Priority Listing of Articles: 

A multi-pronged article deduplication technique can automatically identify and remove duplicate articles from multiple databases. By extracting descriptive metadata such as title, abstract, and authors, the algorithm can eliminate the burden of manual deduplication. The outcome would be a streamlined process that ensures only unique articles are indexed. Additionally, the AI-driven tags can prioritize articles based on relevancy, utilizing Named Entity Recognition (NER) and Relationship Extraction (RE) models. Such an automated approach will optimize efficiency and productivity by ensuring that safety vigilance teams focus on the most relevant and crucial safety information. 

3. Intelligent Article Tagging and Ranking: 

ML models such as Named Entity Recognition (NER), Relationship extraction (RE) and Sentiment analysis take the process of literature surveillance to the next level with features like Intelligent Article Tagging and Ranking capabilities. Using NER and RE models, an AI-powered application can thoroughly scrutinize articles’ titles, abstracts, and full texts to identify and classify specific entities, such as suspect drugs, patient information, adverse events, and primary reporters. By understanding the relationships between these entities, the articles can be marked with extracted entities and relevancy of such articles can be determined. It would lead to a prioritized listing where articles providing the most relevant information rise to the top. This intelligent tagging and ranking system can significantly optimize the workflow of PV experts and streamline the review process. 

4. Seamless ICSR Export in Standard Industry Format: 

With the power of AI-driven automation, the identified data can be efficiently mapped and reported to ensure seamless and standardized reporting. The Individual Case Safety Reports (ICSR) can be exported in standard industry formats such as CIOMS form or ICH E2B R2/R3 compliant XML format. A robust process would ensure adequate data mapping while exporting to multiple formats. Further, by achieving easy integration with existing PV systems, the end-to-end functionality can be implemented for a seamless flow of safety information across the relevant regulatory systems.  

5. Timely and Consistent Regulatory Compliance: 

Meeting regulatory timelines is critical in pharmacovigilance. Regulatory bodies, such as the European Medicines Agency (EMA), mandate specific intervals for article searches and a stipulated timeline for reporting adverse events. A smart literature monitoring solution should allow users to schedule listings based on their regulatory mandates and facilitate timely surveillance. It will not only enhance efficiency but ensure that PV experts consistently have access to the most up-to-date information, supporting proactive decision-making within acceptable time limits. 

A smart AI-powered literature monitoring solution needs to be comprehensive, scalable, secure, and compliant. At Datafoundry, we have always been committed to provide the best-in-class products to contribute toward better health outcomes. Our Literature Surveillance Solution, DF Literature Monitor emerges as a game-changer in addressing the challenges faced by PV experts. It empowers the PV teams with an innovative AI-powered application that optimizes the literature surveillance process, saving up to 60% of the time and effort.  

DF Literature Monitor utilizes Natural Language Processing (NLP) techniques, and ensures efficient article search and ingestion by seamlessly integrating with 20+ open-access scientific and medical literature databases, making it the largest aggregation of literature articles in the market. 

The Intelligent Search Engine, driven by a Machine Learning (ML) algorithm, eliminates the need for manual search strategy creation, precisely extracting articles and assigning relevancy-based rankings. The Article Ingestion Frequency Management aligns with regulatory mandates, allowing tailored scheduling for timely updates of new articles. Further, DF Literature Monitor is equipped with automated deduplication algorithm, leveraging a multi-pronged technique to eliminate duplicate entries, and ensuring unique data display. The highlight of the solution is the process of tagging and prioritizing articles with potential adverse events, which employs custom ML algorithms such as Named Entity Recognition (NER) and Relationship Extraction (RE). The models continuously learn and improve, adapting to evolving data distributions. DF Literature Monitor’s commitment to data-driven excellence is transforming the space of literature surveillance, allowing safety vigilance teams to focus on high-value tasks with precision and efficiency, enhancing patient safety and health outcomes. 

Datafoundry‘s commitment of providing latest technologies towards data-driven excellence to enhance the overall efficiency of safety surveillance teams is reflected through this Cloud-based SaaS solution. By choosing DF Literature Monitor, PV experts are redirecting their focus from labour-intensive tasks to higher-value activities, saving costs and improving compliance. 


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