Traditionally the pharmacovigilance (PV) function has been responsible for collecting, assessing and reporting adverse events (AEs) and related drug safety information to regulators. Because PV is so process driven, companies have had to invest in safety systems to organize data and optimize efficiency. Yet these closed systems, including home-grown systems, lack real-time integration and the sophistication that’s needed to gain valuable insights that are required for the ever-changing requirements of patients, physicians, providers and regulators.
Today, the PV function is evolving due to a slew of challenges and healthcare trends that include compiling, analyzing and reporting volumes and volumes of data to comply with complex global regulations. Furthermore there’s a market need for the pharmacovigilance function to be more predictive, personalized and proactive. While great strides have been made to overcome these challenges and companies have benefited, they have also had to face significant cost burdens. We are at a point where the benefit to cost ratio in maintaining and upgrading these safety systems along with other operational factors is becoming increasingly disproportional.
How can automation and technology help companies reduce their PV cost burden?
One way is to study the strategic benefits that Clinical and Post Marketing Pharmacovigilance can accrue from the direct and indirect application of machine learning and artificial intelligence (AI) based automation tools.
These tools offer solutions to challenges what existing tools and technology are unable to solve.
Burdens in PV Operations
- The cost and volume of individual case safety reports (ICSRs) increases yearly, including real world sources such as social media1
- It is estimated that more than 90% of AEs go unreported2
- It is reported that ICSR processing contributes to 50% of PV spend3
While the industry works to perfect true PV Artificial Intelligence in the future, there are practical PV automations that can add significant value today, like:
- Case intake tools
- Adverse event tracking tools
- PV query Tools
- Natural language processing tools
- Analytics tools
AI and automation is still in its infancy in the Pharmaceutical industry as compared to other industries, and this is no different even if we take a look at functions such as clinical and post-marketing PV. This is understandable given the highly regulated environment that PV stakeholders operate in. Another challenge, as we’ve touched on earlier, is the pharma companies’ current use of multiple, siloed information systems to process safety data that’s preventing many of them from reaching a desired future state. For example, various internal PV groups examine safety data coming from external sources in different ways and for different purposes; each group may pull and analyze data from as many as a dozen disparate systems and—unsurprisingly—draw multiple versions of the truth.
How do we transform Patient Safety Operations?
To begin, PV organizations should look at their short term goals and long term vision for patient safety. They should then analyze if achieving their vision will require incremental or transformational change. Among questions to consider:
- What are our key strategic PV objectives, short- and long-term, and what needs to be put into motion to achieve them?
- What are the trending technology and business themes that can be catalysts in transforming the industry?
- What are our current capabilities? What are we missing as we prepare for the future?
- Have we achieved peak operational performance using existing solutions and technologies or is there scope for improvement?
- Do we have any low hanging fruit that can be turned into “quick wins” to carve out some breathing room and make a case for investing in future tools and technology?
When we talk of transforming your pharmacovigilance operations, our vision is of a touchless future that applies automation, cognitive technologies and analytics that goes beyond merely detecting, assessing and understanding of safety-related problems to creating a next-generation patient-centric system that uses deep learning to improve a product’s benefit-risk profile, helps patients select an optimal treatment, prevent adverse effects and enhance patient safety.
Moving to a touchless, proactive and patient-centric approach will help facilitate a true, evidence-based center for safety intelligence across the entire product life cycle.
If you are interested in learning how we are helping companies like yours enable their vision of touchless pharmacovigilance and transform their operations, let’s talk.
2 – Mockute, R., Desai, S., Perera, S. et al. Artificial Intelligence Within Pharmacovigilance: A Means to Identify Cognitive Services and the Framework for Their Validation. Pharm Med 33, 109–120 (2019). https://doi.org/10.1007/s40290-019-00269-0