Hemanth Dandu

Building Smarter Healthcare Systems with Real-World Insight

Abstract:

Healthcare organizations now have access to unprecedented volumes of data, yet fragmented claims, electronic medical records (EMRs), diagnostic, laboratory, prescription, genomic, and patient-generated data often limit timely and meaningful action. This talk explores how real- world evidence and artificial intelligence can be combined to build smarter healthcare systems that identify risks earlier, support more precise interventions, and continuously learn from patient outcomes. The session presents a four-layer real-world insight framework, beginning with a trusted data foundation that integrates longitudinal patient information across multiple sources. An insight intelligence layer applies risk prediction, cohort classification, care-pathway analysis, pattern discovery, forecasting, and natural language processing to uncover disease progression, treatment gaps, complications, non-adherence, and changing healthcare utilization patterns.

A decision enablement layer then translates these insights into patient segmentation, clinical and operational alerts, therapy optimization, and targeted intervention workflows. An outcome validation layer measures real-world performance, comparative effectiveness, fairness, adoption, and operational impact. The talk also examines the role of predictive models and explainable agentic AI in evidence retrieval, interpretation, and workflow coordination while maintaining appropriate human oversight. A practical adoption roadmap is discussed, beginning with unified data ecosystems and disease-specific use cases and expanding through continuous validation and learning.The central message is that smarter healthcare requires trusted data, explainable AI models, responsible governance, and continuous feedback loops that improve future decisions. Better evidence enables better action.

Brief Profile:

Hemanth Dandu is the Associate Director of Data Science and Advanced Analytics at IQVIA, where he leads high-impact analytics initiatives across oncology, rare diseases, and broader healthcare domains. With more than a decade of experience in data science, machine learning, and healthcare analytics, he has developed and deployed advanced analytical solutions supporting national market sizing, forecasting, physician targeting, and strategic decision-making for leading biopharmaceutical organizations. His work has influenced commercial and clinical decision-making across multiple therapeutic areas, including breast, ovarian, lung, and hematologic cancers. Hemanth specializes in translating complex real-world healthcare data into actionable insights through predictive modeling, statistical analysis, machine learning, and scalable data engineering. Recognized for consistent professional excellence, he combines strong technical expertise with deep healthcare domain knowledge and stakeholder leadership. His ability to connect advanced analytics with practical business and clinical applications has established him as a trusted expert in applied healthcare data science and real-world evidence.