Revolutionizing Adverse Event Detection in Pharma Contact Centers with AI
AI and natural language processing (NLP) are transforming how adverse events are identified from call center audio. These conversations are a rich source of patient and provider insights but have traditionally been challenging to analyze at scale. By integrating advanced speech‑to‑text pipelines with large language models (LLMs) and NLP‑driven workflows, scalable systems can detect, classify, and contextualize adverse events in near real‑time. Overcoming challenges such as capturing nuanced expressions, ensuring data privacy, and maintaining regulatory compliance is crucial to the success of these approaches. The impact of these advancements is significant, improving pharmacovigilance, reducing response times, and enhancing patient safety.
About the speaker
Devdatta Narote
Staff Data Scientist at Genentech
Devdatta is a seasoned expert in bridging the gap between advanced AI solutions and real‑world business outcomes. With a proven track record of guiding cross‑functional teams, he has delivered data‑driven strategies that streamline operations, enhance customer experiences, and promote sustainable growth—all while upholding rigorous data governance and regulatory compliance. A sought‑after speaker, Devdatta has shared insights on responsible AI adoption and the strategic value of analytics at prestigious institutions including Oxford University, the World Bank, and Carnegie Mellon University. His work focuses on fostering a culture of innovation and collaboration, mentoring teams to adopt cutting‑edge methodologies that drive measurable results. Passionate about ethical AI practices, he views artificial intelligence as a transformative catalyst for positive change, ensuring organizations leverage technology to create enduring value for both business and society.
