Matching Patients to Treatments: Refining Top-of-Funnel Approaches

The increasing complexity of treatment options across therapeutic areas has made patient-treatment matching one of healthcare's most critical yet challenging processes. Traditional approaches to identifying appropriate patients for specific treatments often rely on broad demographic criteria and clinical history, missing opportunities for more precise matching that could improve outcomes while reducing unnecessary costs and treatment delays.

As precision medicine advances and digital health tools enable more sophisticated patient profiling, organizations are exploring innovative approaches to refine their top-of-funnel patient identification and treatment matching processes. The challenge lies in leveraging available data sources, predictive analytics, and clinical decision support tools to create more targeted and effective patient-treatment alignment while maintaining practical implementation feasibility.

Join us to discuss:

• What innovative approaches and technologies are proving most effective for improving patient-treatment matching accuracy and efficiency in the early stages of care decisions?
• How are healthcare organizations leveraging data analytics, AI, and clinical decision support tools to refine their patient identification and treatment selection processes?
• What operational and workflow changes are required to successfully implement more sophisticated patient-treatment matching approaches while maintaining provider adoption and clinical workflow integration?