HELIOT: LLM-Based CDSS for adverse drug reaction management

Medication errors significantly threaten patient safety, leading to adverse drug events and substantial economic burdens on healthcare systems. Clinical Decision Support Systems (CDSSs) aimed at mitigating these errors often face limitations when processing unstructured clinical data, including reliance on static databases and rule-based algorithms, frequently generating excessive alerts that lead to alert fatigue among healthcare providers. This paper introduces HELIOT, an innovative CDSS for adverse drug reaction management that processes free-text clinical information using Large Language Models (LLMs) integrated with a comprehensive pharmaceutical data repository. HELIOT leverages advanced natural language processing capabilities to interpret medical narratives, extract relevant drug reaction information from unstructured clinical notes, and learn from past patient-specific medication tolerances to reduce false alerts, enabling more nuanced and contextual adverse drug event warnings across primary care, specialist consultations, and hospital settings. Evaluation using three state-of-the-art LLMs on synthetic and real-world datasets demonstrates classification accuracy ranging from 98.77 % to 99.80 % with zero false negatives for life-threatening reactions. This high accuracy enabled HELIOT to achieve a 50–53 % reduction in interruptive alerts compared to traditional CDSSs while maintaining perfect safety profiles. To support clinical deployment, the system incorporates a confidence-based risk stratification framework that enables automated decisions for high-certainty cases while ensuring appropriate clinical oversight for uncertain classifications. Clinical usability evaluation with healthcare professionals validated these achievements, revealing strong acceptance and unanimous preference for HELIOT's contextual approach over traditional systems. These findings show promise; however, broader clinical trials remain essential to confirm effectiveness across diverse healthcare environments.