Implementing Artificial Intelligence (AI) to Advance Real-World Evidence (RWE) Research
This course introduces how large language models (LLMs) can enhance real-world evidence (RWE) research, highlighting practical applications and best practices. Designed for professionals involved in data science, clinical research, healthcare innovation, and regulatory strategy, it offers a structured overview of how LLMs can streamline key research activities such as literature reviews, clinical documentation analysis, patient phenotyping, and data interpretation through real-world examples.
Technical topics include:
Practical applications of LLMs for synthesizing literature, interpreting clinical documentation, and identifying patient cohorts.
Workflow enhancements for efficient review and interpretation of complex healthcare data.
Key considerations for data quality, privacy, and responsible use of AI technologies in RWE.
Ethical and regulatory frameworks for transparency, bias mitigation, and compliance in AI-assisted research.
Interactive, hands-on activities using LLM-assisted tools for data analysis and reporting.
Future-facing topics including multimodal AI integration and shifting regulatory landscapes.
This course includes tools and concepts that can be immediately applied, including:
Hands-on demonstrations of LLM-powered RWE workflows.
Interactive exercises in Excel for synthesizing and analyzing healthcare data (laptops with Microsoft Excel for Windows required).
Case-based scenarios to explore real-world barriers and solutions in deploying AI for RWE.
Discussions of emerging trends and best practices for ethical and effective AI integration in healthcare research.
Participants who wish to gain hands-on experience must bring their laptops with Microsoft Excel for Windows installed.
PREREQUISITE: This course assumes that participants are familiar with the standing challenges and opportunities for RWE in research.