For major pharma brands, for which pharmacovigilance (PV) has up to now primarily involved monitoring long-established drugs for potential side effects over time, adverse event (AE) case management has been seen first and foremost as a compliance activity. Often the value added beyond satisfying regulatory demands and managing risk has been overlooked in the face of rising workloads, a challenging economy and static budgets. The priority has been to optimise performance; make it possible to deliver more for less. Outsourcing arrangements, and use of technology, have been geared largely to enabling those improved efficiencies.
As brands diversify and add more novel and ambitious therapy areas to their portfolios, meanwhile, another driver for honed PV practices presents itself. A whole range of important new therapies and drug applications are entering the market now, supported by regulators who are working hard to shorten their paths to market without compromising patient safety.
These include the popular GLP-1 receptor agonist/weight loss injections (WHO plans to officially support their use to treat obesity in adults (1)), for instance, as well as the use of messenger ribonucleic acid (mRNA) technology in approaches to cancer. Meanwhile, pioneering COVID-19 vaccines – developed at speed five years ago, subject to accelerated approvals, and given to huge swathes of the global population (2), still need to be carefully tracked for new side-effects.
In the context of new modalities and novel therapies with less predictable long-term impacts, there is a heightened need to detect issues and emerging patterns swiftly. This in turn is placing new emphasis on technology-enabled PV transformation as a means to hone accuracy, precision and speed, in addition to operational efficiency.
The case for AI
It is in the context of these converging demands that AI is starting to make its mark as a mature and viable solution to AE case processing.
AI solutions specifically designed for PV are entering the market in earnest now, and top-tier pharma organisations are testing them with encouraging results. Current solutions have been shown to reliably handle large volumes of data, extract key information from various sources, and even detect subtle patterns that might be missed by human reviewers. (Implementing AI in PV has improved the detection of potential drug risks by over 25% since its introduction, according to the US FDA (3)).
The need for pharma companies to diversify as a means of new brand differentiation and long-term growth, added to their already soaring AE case volumes, leaves them with no real choice about embracing next-generation, AI-driven process automation.
It is a fact that most PV scientists today are highly overstretched. They are also difficult to recruit or replace, while their skills are needed (yet are not available to be deployed) at a more strategic level.
At the same time, intelligent automation technology is advancing at speed. AI’s acceptance in life sciences and in a PV context has been established and is growing at pace. The technology’s accuracy and efficiency improves exponentially – and swiftly – with exposure and training, too.
With good reason then, inspired by the potential and the experiences of early adopters, most PV leaders are now actively tracking AI developments in their field at events and forums, and in their general reading and networking.
Introducing an AI capability
Embracing AI in an AE case processing context will require some groundwork, since the right approach will vary according to a number of parameters. Much will depend on a company’s existing PV ecosystem, the volumes of work it underpins, and the existing technology infrastructure that’s in place, for example. PV technology vendors might be selling the latest functionality, and promising much in terms of the efficiencies it represents, but internal teams (and their PV service provider if they have one) must determine how well this will fit with what they have – and how they work today. They also need to understand how this will translate into tangible benefits, including cost reduction and improved productivity.
Large pharma organisations, processing many hundreds of thousands of spontaneous AE cases each year across extensive and diverse portfolios, are likely to gain the most from having access to advanced technology to ease the PV load.
Even here though, it isn’t just the scale of the operation that will determine the best path to AI use. Retiring legacy safety databases can be daunting, so implementing AI may involve sophisticated workarounds and wrap-around software. This could involve giving users a single, simple, intuitive interface or portal, for instance, and creating a layer where the ‘magic’ happens – both ringfencing the core safety database’s integrity, and extending its return on investment.
For organisations with more modest product portfolios, immediate PV pressures are more likely to revolve around limited internal resources, scalability and associated challenges with meeting AE reporting timescales in key markets.
Here, the best route to embracing AI may be to establish a digital-first capability – beginning with the inbound AE case capture process, ensuring that this is digitised across all supported channels. The more that cases can be captured digitally at source, the greater the potential impact of AI in their assessment and processing. As well as creating a substantial efficiency gain in its own right, by doing away with manual data entry, it also sets up the PV operation for all downstream processing to be digital.
Seizing the day now, to be ready tomorrow
Whichever route companies go down in transforming their AE case processing capability, the priority must be to make a move sooner rather than later – and be clear about the end goal. Anticipated cost efficiencies will help shore up the business case, for instance, but it’s important not to overlook the strategic benefits on offer.
As more pharma companies look to novel and advanced products and therapies to drive new value for patients, and as a source of vital new growth, parallel advances in technology will help not only to streamline the pharmacovigilance function, but also to deliver important new insights that will enhance patient safety and inform future product development.
Supported by the right technology, the PV function could become a more strategic partner in drug development and product lifecycle management. That’s as long as there is integration with risk management and technology is leveraged to streamline processes, surface new insights and hone decision-making. Such PV-driven insights could inform new studies to be considered (in England, reports of pancreatic issues linked to weight-loss injections have triggered a new study into side effects of the treatments (4)), for instance; or identify potential new use cases for existing drugs for further exploration, following reports of unexpected side benefits.
Any steps companies take to modernise and upgrade PV now will help them to realise that future.
References and notes
- WHO to back use of weight-loss drugs for adults globally, raises cost issue, Reuters, May 2025: https://www.reuters.com/business/healthcare-pharmaceuticals/who-set-back-use-weight-loss-drugs-adults-globally-raises-cost-issue-2025-05-01/
- World Health Organization COVID dashboard: https://data.who.int/dashboards/covid19/vaccines
- Pharmacovigilance market, Market Research Future, June 2025: https://www.marketresearchfuture.com/reports/pharmacovigilance-market-8451
- Weight loss jabs study begins after reports of pancreas issues, BBC News, June 2025: https://www.bbc.co.uk/news/articles/c4ged0r1n3wo
