Healthcare data plays a key role in improving quality of life as accurate data can help doctors, patients and other stakeholders involved take the right decisions. Artificial Intelligence (AI) has been contributing to the patient data collection more efficiently with improved systems like natural language processing (NLP) applications that can analyse unstructured clinical notes on patients, giving incredible insight into understanding quality, improving methods, and better results for patient. AI also reduces case processing costs and cycle times to improve pharmacovigilance (PV) activities. AI and Machine Learning (ML) and can play a vital role in pharmacovigilance.
During the early stages of COVID-19 pandemic, numerous approved drugs were repurposed and released off-label to treat patients. In this health emergency, it was extremely critical in evaluating the potential risks of the off-label drugs used. The rate at which new drugs are being introduced into the market resulted in increased drug usage. Thus, the need to analyse benefits and potential risks of drugs too rose making PV extremely important. And today, real-world data and real-world evidence play a key role in the risk-benefit analysis of a drug.
What used to be mere data compilation and information provision for regulators is now transforming into a system for enhancing risk-benefit profile of drugs, enabling caregivers to choose the best possible treatment. Advanced technologies such as automation, advanced analytics and cognitive technologies have played a key role in this transformation. Digital pharmacovigilance enables life sciences companies to embed AI/ML for improved data quality and insights from regulatory activities.
PV collects, evaluates and act upon adverse events (AEs) by detecting, assessing, and preventing AEs. The increasing amount of data received from individual case safety reports (ICSRs) needs to be properly managed for further references. With the increased number of ICSRs yearly, it is estimated that more than 90 per cent of AEs go unreported, creating an opportunity to apply AI and Machine Learning techniques to improve drug safety assessment.
At the core, automation helps simplify repetitive, mundane tasks allowing the staff to focus on more value-driven activities. And in PV, automation helps with adverse events processing, a critical activity consuming enormous resource time. Now is time for the industry to harness AI for transforming pharmacovigilance and make it more sustainable with a focus on innovation. This way, life sciences companies can unlock future of AI-powered drug monitoring to influence the global healthcare ecosystem through improved patient safety and optimised care.
However, challenges like establishing a database for an AI-based pharmacovigilance system, lack of human resources, weak AI technology and insufficient government support remain.