AI and ML are Game-Changers in Pharmacovigilance

Aug 20, 2021
Life Sciences | 2 min READ
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Artificial Intelligence (AI) and Machine Learning (ML) are game changers in Pharmacovigilance (PV). Technology implementation not only helps decrease the cost of case processing but improves safety data quality progressively.
Dr. Sharada Rao
Dr. Sharada Rao

Former Vertical Head - Delivery

Life Sciences


Patient safety has taken on an urgency and this focus has forced massive technological advancements in pharmacovigilance. It is crucial and imperative to modernize the clinical trial process, introduce federated machine learning systems, employ artificial intelligence in safety systems, and ensure real-time PV reporting.
With technology adoption, processing time for case intake in pharmacovigilance is exponentially faster. Automating testing cycles brings forth an opportunity to formulate faster hypotheses and expedite adverse events. It improves the drug's Risk-Benefit Profile that further enhances safety standards. It also reduces repetitive activities like duplicate searches of cases and reporting.
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Covid-19 pandemic has led to build a robust Drug Safety Reporting system. This is where Online Cloud-based reporting solutions are an integral part of PV post-pandemic.
Responsive intelligence using AI ML with real-time regulatory intelligence is also vital for a sponsor to avoid disrupting clinical trial, including fulfilling legal responsibilities for patient safety.
Then there is robotic process automation solutions for PV to enable rapid implementation of processes. Connected PV agencies, sponsors, and CROs use the cloud to understand one another's requirements, address operational challenges and implement standard solutions.
Responsive intelligence using AI ML
The spread of Covid-19 challenged traditional clinical trial models. There was a shift towards patient-centered clinical trial designs, rewriting PV protocols to allow remote patient monitoring and in-home delivery. Further, implementing other digital capabilities, concepts like telemedicine kept clinical research viable.
Edge Computing is now an established option and this along with an increased usage of smartphones and sensors in a real-world setting provides a gateway to the creation of digital endpoints. As more digital technologies enter the market and become increasingly integrated into clinical research, we will begin to witness a shift from digital endpoints used as supplementary to primary endpoints, she pointed out.
Since this pandemic is new to everyone, healthcare providers too need to rely on observational data. Swift analysis of this data enhances decision-making to prioritize patient safety. Cloud-First platforms in PV space have become a business imperative in the hospital management systems.
Even the Extreme Safety model introduced for vaccine development comprises AI-based clinical trials that reside on the cloud, that scale in real-time, that ensure data security and privacy, that are robust and built to industry standards in Clinical Trials Management Systems.
AI-based clinical trials
Social media plays an enormous role in Social Patient Recruitment, where subjects are engaged and retained for clinical trials, Site Investigation, and RPA.
Connected Patient Communities ensure trial adherence, enable sharing of patient experience, and connect families to discuss their health as they progress through the trial.
This information is again mined by the different stakeholders towards drug refinement and improve efficacy. Terabytes of data that churn out the safety insights reside on cyber-secure platforms and AI and ML-operated Safety Data Lakes and Insights-driven safety management automate Safety Reporting.
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