Evolving your adverse event monitoring strategy for the post-COVID data influx

Managing and reporting the growing volumes of adverse events (AEs) is progressively more challenging for pharmaceutical companies as the number of data sources grows. The anticipated number of AEs is expected to increase dramatically as more products are brought to market and pharmacovigilance teams are doing their best to adopt processes that will enable them to keep pace. Most recently, we saw this challenge arise as the initially predicted volumes of AEs for COVID-19 vaccines were quickly outpaced. With more vaccines and treatments rolling out for COVID-19 and various other diseases, we can equally expect to see an influx of AEs paralleling the data these treatments will generate.

Marketing Authorisation Holders (MAHs) are challenged to identify all potential AEs and proactively incorporate a strategy to address them down the line with each new product that comes to market. As data amasses, this becomes harder and harder for people to do manually. Consequently, leaders are seeing the need to become early adopters of new technologies in the interest of digitally transforming their pharmacovigilance teams. They are beginning to equip teams with new tools that are up to the challenge of managing massive amounts of data, regardless of the source, and aiding in comprehensively capturing AEs as they appear.

More adverse event data demands more comprehensive monitoring

Over the past year, the pharmaceutical industry has been challenged to ramp up the rollout of new vaccines and treatments specific to COVID-19, while also keeping patient safety at the forefront. In parallel, the outcomes of the pandemic drove a massive increase in telehealth adoption, which has given pharmacovigilance teams a new pool of unstructured data on patients to monitor for potential AEs. This data can come in many forms, from a doctor’s notes in electronic health records (EHRs), through call centre agents and out of office hours messaging services or social media posts, as well as in different languages, whether they be in written or audio formats. This data can contain safety events or risks. However, it comes with serious challenges around how to make sense of the information and identify where pharmacovigilance professionals should invest their attention quickly and efficiently.

As the channels by which AEs can be reported expand, proactively adopting technologies for pharmacovigilance such as artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) will be critical in keeping pace to avoid under-reporting. Most critically, NLP can take the new unstructured data from any source and convert it to structured formats which AI and ML can then be applied to. These capabilities are expanding even further with the rise of audio transcription capabilities, which can identify AEs discussed on calls and mitigate the need for costly and time-consuming manual transcriptions. As an added layer on top of identification, AI-based auto-prioritisation can filter out the noise and draw the attention of the company to serious or unexpected events captured via text and audio. In turn, the confluence of these capabilities will enable timely identification of potential adverse events and address possible threat to patient safety before it becomes widespread.

Implementing technology is empowering pharmacovigilance teams to streamline their process from AE identification to processing, which has historically been very siloed. Leveraging AI for automated detection of AEs allows pharma companies to process them more efficiently once they are identified. These strategies ultimately promote better patient safety by mitigating the lag time between becoming aware of sources of potential AEs and identifying AEs within these for case processing and regulatory reporting.

Reaping the business benefits of technology driven strategy

For too long, companies have had to rely on expanding their staff to address data processing challenges – a strategy that is both extremely costly and always a temporary fix. By revitalising their strategy through digital transformation, teams can mitigate the need to hire more support by up to 90 percent and eliminate the repetitive and costly manual processes associated with pharmacovigilance. Additionally, less focus is needed on manual reporting tasks such as zero-touch processing, allowing pharmaceutical organisations to focus on risk management and strategy instead.

Moreover, a technology driven strategy can help address operational budget challenges. There has been a 20 percent increase in AE volumes companies must process. This influx has forced over 70 percent of their operational budgets to remain dedicated to meeting routine regulatory requirements and only 20 to 30 percent available for value-driving operational activities and meaningful safety improvements. Integrating automated technologies with existing IT infrastructure empowers leaders to utilise their legacy technology investments while making traction on further innovation that will keep them ahead of the curve and ready for new challenges.

Looking forward

The pandemic underscored the extent to which pharmacovigilance teams are overextended to maintain patient safety and regulatory compliance – and the cadence of new drugs coming to market is not slowing down. As we continue to see our way through the pandemic and simultaneously prepare for the future of medicine, being an early adopter of technology and prioritising digital transformation will pay dividends to companies. Technology vendors also have a role to play in making their technologies available on a trial basis, or providing opportunities for proof of concepts and pilots, so that leaders can test run their value without a long-term commitment. These acts of partnership and trust will be invaluable to foster confidence in these investments, which will equip companies for faster adoption when these tools inevitably evolve from an “added bonus” to a “must-have” for business success.