Pharmacovigilance—the practice of monitoring the safety of pharmaceutical products and identifying adverse events—is a critical function in healthcare as the need for increased patient safety and security is at an all-time high. At Authenticx, we help facilitate robust pharmacovigilance through our platform features, AI models, and workflows that are specifically designed to identify and escalate safety events efficiently.
With pharmacovigilance services built into our AI and platform features, organizations can now have eyes into how to monitor, improve, and automate safety for their patients—creating trust and credibility all while maintaining the bottom line by avoiding costly mistakes, such as missing an adverse event (AE) or reporting false incidences.
Below, there are 10 key aspects to pharmacovigilance services and how to have an AI that understands pharmacovigilance and the impact it can have.
On Detecting Adverse Events
Any gaps in reporting safety events (an umbrella term for adverse events, product quality complaints, other special situations impacting efficacy) generally can be traced back to one thing: volume. There are so many conversations flowing through the patient service touchpoints of pharmaceutical and medical devices manufacturers, and manually trying to capture, review, and report every potential event is not scalable. The FDA estimates 90-99% of adverse drug events go unreported.
Additionally, agents may be over-reporting events out of an abundance of caution: diverting resources to reviewing and following up on an event that ultimately does not need to be reported. AI can quickly ingest unstructured conversation data, apply a consistent set of evaluation standards to those conversations, and quickly surface the conversations with potential risk or reportable events.
On Reporting Rates
Typically, when adverse events are reviewed to audit compliance, a small sample (1-3%) of customer interactions are audited to determine if the Adverse Event reported was accurately conveyed. Missing a statistically significant number of Adverse Events can result in costly fines, additional source data validation projects, and more. One client had to analyze a significantly large number of calls after failing an audit, costing their business hundreds of thousands of dollars.
Authenticx has developed a sophisticated operation that combines data labeling, AI model development using deep learning, and “human in the loop” validation to ensure the AI works well from day one onward. We measure the effectiveness of the AI by using a confusion matrix, which ultimately yields an agreement score. Agreement score tells us what percentage of the time our humans labeling and auditing conversations agree with the AI. We’ve proven out this model to successfully deploy and iterate on various technical aspects of the AI to improve agreement scores continually.
On the Reduction of Manual Processes
Our solution can flag potential safety event occurrences in your conversations. This eliminates the need for the agent to document that the event has occurred, and self-report the conversation for review. Once identified, the AI can automatically escalate that conversation to a patient safety or pharmacovigilance services team.
All in all, AI helps eliminate unnecessary, initial manual notetaking by an agent in between calls by a factor of 10-15 minutes per interaction, decreases the number of “false alarms” escalated for review, and expedites the final review before any required reporting by clearly highlighting the relevant content in the conversation.
On Compliance, Governance, and Standards
This entire AI-driven solution was designed with patient safety at the center. It serves no one, least of all the patients, when safety events impacting the efficacy of their treatments and ultimately their day-to-day lives go unreported or unresolved. Adapting an AI governance framework that adheres to industry standards and best practice guidelines is one important way to provide safeguards and protection measures. In addition, we strategically deploy a “human in the loop” strategy that monitors and validates AI performance and validity.
On Integrating into Existing Systems
One of the biggest challenges is risk appetite. AI is still a “new” and often misunderstood technology. The idea of taking something that impacts patient experience and safety so intimately, and leaving it to AI is not a decision to be taken lightly. We offer clients a very intentional, strategic path towards slowly injecting more AI into their process as they see the results, understand the capabilities, and work with us to understand the scope and best places to plug this solution in. Overcoming any concerns or challenges starts with a good partner who understands the industry challenges and has a solution that is intentional in solving those problems.
On the ‘Dark Data’ of It All
“Dark data” is a term we use in reference to all the information buried in everyday conversations that goes completely unused. Too often, organizations are sitting on data that is simply not in a format that can be easily digested or distributed, etc. This data source is valuable because oftentimes it contains all sorts of first-person experience data that speaks to an organization’s processes, brand awareness and loyalty, the customer experience, and more.
Analyzing this data not only gets you answers to basic business questions you are already investing a lot of time and money trying to answer in other ways, but it also centers the voice of your customer.
On Applying It to the Real World
One major health organization used AI to analyze over 800K+ interactions. They rapidly found that approximately 28K (or over 3%) of those interactions used incorrect agent language, which had risk implications in identifying and documenting safety events. By automating routine monitoring, the company could focus its human analysts on high-risk interactions, significantly reducing the likelihood of future complaints or audit findings.
On Looking Toward Future Trends
Our vision is to eliminate manual notetaking, improve accuracy in identifying these events, and ultimately improve the outcomes for patients impacted through faster reporting. That really is the broader promise of AI: to do things more consistently and in a more efficient way to ensure equally positive outcomes for patients and the healthcare organizations serving them.
On Adaptation and Training
Human oversight is a non-negotiable part of responsible AI development. We deploy a “Human in the Loop” approach for oversight of AI performance, ensuring results that our clients can rely on and expanding the scope of how they can responsibly deploy AI and reap the benefits without increasing risk exposure.
On How to Best Engage Stakeholders
Engagement begins first by listening. We recognize the challenges of innovating an industry that brings in nearly a third of the world’s data — this is why it’s important all stakeholders are intentionally considering what problem they are aiming to solve and the impact they hope to see.
Pharmacovigilance is a Must-have, Not a Nice-to-have
Pharmacovigilance services and AI features with Authenticx empower healthcare organizations to proactively identify, monitor, and escalate adverse events and safety concerns with precision and efficiency. Through AI-driven detection models and real-time reporting, the platform scans every conversation for mentions of adverse events, product quality complaints, and HIPAA compliance issues, triggering automated workflows for immediate escalation.
This seamless process not only accelerates response times but also fortifies patient safety by ensuring critical safety signals are acknowledged and addressed without delay. Authenticx sharpens this focus by enabling targeted extraction of conversations related to safety concerns, allowing teams to detect emerging risks and respond proactively before they escalate.
Moreover, our robust evaluation tools transform raw conversational data into actionable insights, offering healthcare organizations a clear view of adverse event prevalence and agent response effectiveness. Customizable reporting and real-time monitoring streamline compliance with regulatory standards, making audits and documentation seamless and transparent. With Authenticx, healthcare is not only equipped to detect and resolve safety risks efficiently but is also empowered to cultivate a culture of accountability and continuous improvement in patient safety.
This holistic approach to having pharmacovigilance services built into our platform redefines how healthcare organizations safeguard their patients, ensuring that critical information is never missed and always acted upon with the urgency it deserves.
Begin your journey with pharmacovigilance services and tools that are purpose-built for your organization. Schedule time with our team or watch our Compliance demo to learn more.
About Eric Prugh
Eric Prugh is the Chief Product Officer at Authenticx and leads product strategy, design, and product marketing. Eric has spent more than 15+ years building and scaling software companies in go to market, product, and international functions. Prior to Authenticx, Eric was Co-founder and Chief Product Officer at PactSafe, a platform that powered over 1 billion online contracts for companies like Wayfair, DoorDash, Orangetheory Fitness, Dell, Upwork, and more. Eric also was a leader at ExactTarget, a marketing technology giant in Indianapolis that sold to Salesforce in 2013.
About Authenticx
Authenticx was founded to analyze and activate customer interaction data at scale. Why? We wanted to reveal transformational opportunities in healthcare. We are on a mission to help humans understand humans. With a combined 100+ years of leadership experience in pharma, payer, and healthcare organizations, we know first-hand the challenges and opportunities that our clients face because we’ve been in your shoes. In 2023, Authenticx was ranked No. 349 on the Inc. 5000 recognized as one of America and Indiana’s fastest-growing private companies.
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