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The reason? The most valuable insights are often buried in unstructured patient conversations, spread across countless interactions. Without the right tools, safety events can slip through the cracks: potentially impacting patient safety, product efficacy, and regulatory compliance.
The challenges of safety event reporting
Pharmaceutical companies have encountered over a million safety events so far this year. Despite the considerable effort put into reporting, the FDA estimates 90-99% of adverse drug events alone go unreported. This isn’t due to a lack of diligence. Rather, it’s indicative of the sheer volume of data reporting problems these organizations face as they try to manually track, review, report, and audit millions of patient conversations.
AI tools can help remove the burden of recognizing, documenting, and escalating adverse events from the initial agent handling the conversation, and instead direct identified instances directly to pharmacovigilance teams.
Conversations are a particularly challenging data source to analyze manually. The complexity and emotion driving these conversations, coupled with the volume of interactions, make it difficult to scale. As a result, safety events are missed.
Undetected safety events can lead to negative effects on patient health, regulatory penalties for non-compliance, and potential repercussions on FDA standing for pharmaceutical companies. This highlights the urgent need for more effective, automated approaches.
Why AI?
AI can help organizations decrease risk, protect resources, and improve outcomes by streamlining what information is passed to Patient Safety or Pharmacovigilance teams. Traditional reporting methods are overly reliant on consistent and meticulous performance by agents who are simultaneously handling complex conversations daily with patients. AI, however, excels in analyzing vast amounts of data consistently and efficiently. With the help of NLP, generative AI, and most recently LLMs, these innovations allow organizations to process and interpret complex, nuanced language at scale in near-real time. Unlike human reviewers, these various forms of AI can sift through countless conversations in seconds to identify patterns and potential risks that may otherwise go unnoticed. This results in faster detection of safety events, reducing manual effort and delivering more accurate reporting.
AI designed specifically for healthcare brings a nuanced understanding that ensures faster, more reliable results. For example, one healthcare organization introduced automated evaluations to their processes. Within a year, the organization reported they were able to reduce compliance observations by 50% while increasing the volume of calls monitored by 45%. This enhanced oversight allowed leaders to refine their compliance requirements across 45 service lines.
Going beyond safety event detection, AI can highlight areas for operational improvement, uncover reoccurring issues, and provide actionable data that can enhance patient care and maintain regulatory compliance.
The Role of AI in Safety Event Detection
So, what role does AI play in safety event detection?
AI is exceptionally well-suited for monitoring large volumes of patient interactions and identifying safety events that might otherwise slip through the cracks. This type of AI is developed by first identifying what types of conversations typically contain a safety event and then utilizing those examples to build models that can adapt and learn with human validation and ongoing testing. By transforming how safety events are detected and managed, AI offers:
- Reduced Risk: AI enables faster and more accurate detection of potential safety events, minimizing the likelihood of missed incidents and ensuring quicker intervention with scaled efforts to automate and review recorded calls.
- Focused Resources: By automatically identifying and prioritizing high-risk conversations, AI allows healthcare teams to concentrate their efforts on the most critical interactions, saving time and reducing manual workload.
- Enhanced Patient Safety: Healthcare organizations can respond to safety concerns more swiftly, improving overall patient safety outcomes.
Another example, 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.
With AI offering faster and more accurate detection, healthcare organizations can stay ahead of safety risks, ensuring timely responses that protect both patients and regulatory compliance.
The Path Forward
Incorporating AI into safety event reporting is not just an operational step forward—it’s a leap toward transforming patient safety. With the ability to process large volumes of data quickly and accurately, AI helps pharmaceutical and healthcare organizations enhance their reporting efficiency, reduce manual effort, and improve patient outcomes. Embracing these advanced tools is crucial for staying ahead of safety risks and ensuring that patient safety remains a top priority.
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.
Filed Under: machine learning and AI
