Patient safety is the most critical aspect in the drug lifecycle. Early detection of any reaction and its causality assessment is therefore one of the most important activities in the pharmaceutical industry. Digitalization has brought speed, but significant human work is still required. Medical causality necessitates understanding medical and clinical vocabulary, having medical knowledge, and being able to derive relationships and correlations. Each of these requires human cognitive capabilities.
The challenge increases with new diseases and an avalanche of new drug discoveries. Monitoring and processing the huge number of drug reactions across the globe requires a large number of skilled medical professionals. However, even with the assumed availability of experts, the process is slow. Fortunately, with current advancements in Artificial Intelligence (AI), digital clones of medical professionals and pharmacists, AI digital PV case processors, can solve these problems and scale operations.
AI digital clones can mimic some pharmacovigilance functions
The AI digital PV case processor can add value in the pharmacovigilance automation function, reducing the workload for the human processing. The capabilities of a digital PV case processor include:
- Capture Individual Case Study Report (ICSR) inputs from multiple source types – voice calls, emails, chat, websites, social, Alexa, etc.
- Automate case processing based on cognitive decision making and medical knowledge
- Integrate with multiple dictionaries and references like PubMed, WhoDD, UMLS
- Provide enterprise best practices including SOPs and herd knowledge
At the core of the digital PV case processor is an AI and Machine Learning (ML) based inference engine. The engine can understand the pharmacovigilance context of an entity in essay or paragraph format, infer its meaning, and determine its relationship with other entities.
The workflow engine orchestrates information and data flow to ensure streamlined case management and monitoring. The intelligent modules are loosely coupled and designed for microservices architecture and can be hosted on cloud, on-premise, or in hybrid forms. The engine can also integrate with external systems in multiple ways and understand E2B(R3).
Inside the brain of the digital case processor lies a multi-module AI pipeline that works to accurately identify the entities expected in a human case processor.