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.
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