In this episode of the Digital Health Transformers podcast, Meghna Misra, Head of Product at ClaritasRx, discusses how AI is transforming patient visibility across specialty, rare disease, oncology, and CAR T therapies. She explains how predictive analytics enable care teams to identify risks such as prior authorization denials, refill delays, and therapy drop-offs before they occur. Meghna emphasizes that the real value of AI lies not only in prediction but in turning insights into clear actions embedded within existing workflows.

The conversation explores the importance of transparency, explainability, and trust in high-stakes healthcare use cases. Meghna shares real-world outcomes from ClaritasRx, including measurable improvements in fill and refill rates driven by AI-powered risk models. She also discusses the role of healthcare leaders and policymakers in creating frameworks that support innovation while ensuring equity, data quality, and patient privacy. The episode concludes with practical advice for organizations adopting AI, focusing on problem-first design, explainable models, and keeping humans in the loop.

Key Moments

Introduction and AI Focus in Healthcare

• Meghna Misra introduced as Head of Product at ClaritasRx
• Discussion centers on how AI is reshaping patient visibility and healthcare delivery
• Emphasis on impact-driven AI rather than technology-driven adoption

Solving the Patient Visibility Problem

• Fragmented healthcare data limits understanding of the patient journey
• AI connects data across pharmacies, providers, hubs, and access programs
• Shift from reactive analysis to proactive, predictive visibility

Predictive Analytics for Early Risk Detection

• Identification of risks such as prior authorization denials, refill delays, and therapy drop-offs
• Use of foresight to predict when and why risks will occur
• Integration of social determinants of health to improve accuracy

Turning Insights Into Action

• Predictive insights embedded directly into existing workflows
• Next best action models guide care teams on what to do next
• Focus on reducing administrative burden and enabling timely intervention

Measurable Outcomes and Real World Impact

• AI-driven models deliver approximately 20 percent improvement in fill rates
• Refill rates increase by more than 17 percent across brands
• Improved care coordination helps patients start and remain on therapy

Trust, Transparency, and the Future of AI in Care

• Explainable AI is essential in high-stakes healthcare decisions
• Healthcare leaders and policymakers play a role in ensuring equity and data quality
• AI evolving into a decision partner that supports proactive, patient-centered care