Rising opioid misuse continues to strain health systems already challenged by manual screening processes, fragmented data, and delayed risk identification. Traditional opioid use disorder (OUD) assessments are reactive, time-intensive, and often rely on subjective clinical judgment, leading to missed opportunities for early intervention and inconsistent care outcomes.
By combining machine learning, a clinical rules engine, and real-time EHR connectivity, this AI-driven solution delivers proactive, evidence-based OUD prevention through advanced risk prediction and seamless clinical integration. It enables providers, health systems, and payers to identify at-risk patients earlier, reduce workflow burden, and support safer, more equitable, and data-informed pain management.