Machine learning and AI adoption in healthcare is accelerating, yet many organizations still face challenges in translating models into measurable value. This session explores the full lifecycle, from development to insight, operational execution, and value realization, focusing on what's needed beyond algorithms alone.

• Explore how healthcare ML models are developed, including use-case prioritization, data readiness, feature engineering, validation, and clinical/operational governance

• Discuss how to turn model outputs into trusted, consumable insights through interpretability, bias monitoring, workflow integration, decision thresholds, and stakeholder alignment

• Examine how organizations operationalize insights into action via care management, utilization workflows, provider engagement, automation, and human-in-the-loop design

• Share and align on practical approaches to measuring value, linking AI-driven actions to outcomes such as cost avoidance, quality improvement, utilization reduction, clinical impact, and ROI, while accounting for attribution and real-world constraints