Over the past decade, the life sciences industry has witnessed, and been at, the forefront of an unprecedented technological revolution in pharmacovigilance (PV). With a spurt in innovations, advanced scientific discoveries, personalized medicines, and global portfolios, the life sciences industry relied on digital technologies that could handle the innate complexities. Technology organizations responded by conceptualizing and developing disruptive artificial intelligence (AI) and machine learning (ML) platforms and bringing them into the markets that gradually replaced the traditional point automation PV tools.
Strategic early adopters and visionary industry leaders in the life sciences space have already realized the benefit of leveraging digital and advanced technological approach including AI/ML to solve complex challenges and introduce intelligent systems into the PV process. However, merely recognizing this aspect is half the battle won; it is essential to build applications by partnering with the right cognitive automation provider. Collaborating early on offers multiple benefits including risk mitigation, innovative strategies, and a faster speed-to-market.
Secondly, it is imperative for businesses in the life science industry that wish to be successful in embracing the PV digital transformation to be in alignment with key stakeholders within their organization on the following elements:
Only after the alignment with key decision makers on all the above elements, can any conversation on next-gen technology be pursued. Next up, selecting an experienced AI/ML technology provider can directly impact the outcome of the entire PV system, reporting, and risk management. Businesses need to evaluate the following considerations while selecting a digital technology provider who excels in PV:
Technology experience: Provide robust, reliable, and successful implementations with an impeccable track record in an integrated area of services and technology, specializing in PV operational and scientific excellence coupled with deep digital and cognitive automation development experience.
Product implementation experience: Demonstrate evidence of the AI platform in production as well as offer proof of concept projects and be able to show metrics and experienced results. The provider should be able to guide the organization toward full adoption of cognitive automation-based PV model, function by function, beginning with a pilot project.
Value-based Roadmap: Provide an industry roadmap reflecting the technology provider’s understanding of how the next-gen PV is evolving, illustrate value-driven developments, and define the key capabilities provided by technology for addressing current and future PV challenges and needs.
Safety case processing coverage: Enable safety case processing automation spanning the entire spectrum of activities including case intake, triage, data entry, processing, medical coding, causality assessment, analysis, narrative writing and reporting. Powered by niche technologies such as AI, ML, and natural language processing (NLP), the technology provider should be able to handle high safety case volume with quality, accuracy, and consistency.
Scalable: Provide scalable, cloud-hosted platforms that enable the quick handling of large safety case volumes and spikes, can address unpredicted safety situations (such as COVID-19 safety-related issues), product launches, product acquisitions, and/or changes in regulatory requirements.
Minimal disruption to existing processes: The ability to implement the PV technology with minimal or no impact on the existing business processes as well as with limited dependency on customer’s resources.
Explainable technology: Explanatory decision trees and audit logs for ‘anytime inspection readiness’ is an essential prerequisite in pharmacovigilance. The technology provider should empower users with access to complete audit trail, including full traceability of actions and decisions taken by the AI system in a human-readable log.
Learning management: The PV system should have the ability to learn and apply the new learning with every case processed, and the repository of learnings should continue to grow with variety and the volume of cases processed. The technology should also enable human governance on machines and human-identified learnings through defined change management, thereby facilitating machines to learn from their own and reviewer’s actions, draw inferences and solicit approvals from humans to inculcate the learnings gained.
Tangible efficiencies: Implementation of an innovative automated PV model should augment efficiencies in PV delivery in view of current and future changes and challenges.
Every organization is different with varying business processes, organizational structures, and technical requirements. However, there are some common objectives where all of them converge, and the need to implement PV automation with open platforms being one among them. There are different ways to implement a cognitive automation-based PV platform: build your own platform, assemble, and integrate disparate tools from other solutions, or deploy a ready-to-use AI solution. Depending on an organization's business goals and technological capabilities, any one of these options works better than the rest. This article aims to provide a guiding philosophy for organizations that are about to embark their next-generation technology roadmap in pharmacovigilance with a best-fit technology partner.