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Table of Contents
1. Big data - Science fiction or clinically relevant2. Large administrative datasets: Lessons and limitations
3. Sources of high-dimensional data - The electronic health record, health systems, and insurance and payor data
4. Best practices when interpreting big data studies: Considerations and red flags
5. Current big data approaches to clinical questions in otolaryngology
6. Bias in big data: Historically underrepresented groups and implications
7. Artificial intelligence in otolaryngology
8. The patient perspective on big data and its use in clinical care

