Literature Monitoring in Pharmacovigilance - Datafoundry
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Nov 18th, 2021
Understanding Literature Monitoring in Pharmacovigilance – The importance, challenges and role AI can play 

Introduction: What is the difference between monitoring, assessment and review? Why is Literature Monitoring Important for Pharmacovigilance?

Literature assessment is the process of evaluating the quality and relevance of literature to an assignment or research topic. This may be accomplished by reading reviews, summaries, or abstracts of the literature. 

Literature review can be defined as a systematic examination of different studies about a given subject to come up with an overview of the entire topic.  

The term ‘literature monitoring’ refers to the process by which adverse events or new drug efficacy data are detected in published sources. Literature monitoring is important to ensure that the medical professionals are aware of the latest research findings. Pharmacovigilance specialists monitor medical literature for adverse events, side effects, and new drug effectiveness

Role of the Pharmacovigilance Specialist in Literature Monitoring Process

Pharmacovigilance specialists are experts in the literature monitoring process. They are responsible for tracking scientific announcements, research studies, drug alerts, and reports of adverse events that might relate to a company’s products.

The specialist should have knowledge of the therapeutic area in which he or she is monitoring, experience with literature-monitoring databases, and an ability to read medical journals. They should also be familiar with the regulatory environment for drugs.

Problems Pharmacovigilance Specialist face in Literature Monitoring Process

The role of a pharmacovigilance specialist is to monitor for potential side effects of drugs. In the literature monitoring process, pharmacovigilance specialists are required to read through hundreds of publications per day and identify whether they are relevant to the safety concerns they are interested in. 

This process is very time consuming and laborious, which is one of the major challenges that pharmacovigilance specialists face. For example, it takes up to eight hours for one person to read through all of the articles published in previous two weeks. 

This is because there are many papers that need to be read through, which can be challenging to go through if they were not written in English. This also makes it difficult to come up with keywords that can be used for filtering, as well as identifying what would be considered “relevant” for their search query.  

How AI can help to solve problems in Medical Literature Review

We understand that medical literature monitoring is a time-consuming and tedious task that requires a lot of expertise. Therefore, it is not surprising that many research institutions and pharmaceutical companies rely on the help of artificial intelligence (AI) to monitor medical literature.  

AI can be used to automatically extract data from published sources, such as journal articles, conference proceedings, or news articles. With the use of machine learning, algorithms can be created that can analyze papers and identify those which are relevant for relevant research question. By automating tedious tasks like scanning PubMed records or cross-referencing journal articles with bibliographies, researchers and PV specialist will be able to focus their attention on more important matters instead of spending hours sifting through irrelevant data. 

The algorithms can also be trained to be able to identify new drugs or adverse events that were not previously detected by humans. It would also allow health officials to track global progress on key issues related to healthcare technology.  

If implemented correctly, AI could play an important role in reducing the complexity and cost associated with monitoring medical literature. 

How can we help?

DF mLiterature AI from Datafoundry is an AI-powered literature review solution for multiple use cases spanning SLRs, Meta Analysis, PV Literature Monitoring and Clinical Evaluation Reports.  

DF mLiterature AI enables searching of content published in databases such as PUBMED and EMBASE. It can be customised to include internal literature. DF mLiterature AI uses deep learning models to find relationships within literature at a more efficient rate than any solution has been able to do before.  

Talk to our experts for getting any help in automating your literature monitoring process.

AI-assisted Literature Monitoring and Review

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