Big Data in Otolaryngology, 1st Edition

Big data plays an increasingly important role in today’s practice of otolaryngology and in all of healthcare. In Big Data in Otolaryngology, Dr. Jennifer Villwock leads a team of expert authors who provide a comprehensive view of many key impacts of big data in otolaryngology—including understanding what big data is and what we can and cannot learn from it; best practices regarding analysis; translating findings to clinical care and associated cautions; ethical issues; and future directions.

Key Features

• Covers the clinical relevance of big data in otolaryngology, lessons and limitations of large administrative datasets, biologic big data, and much more.
• Discusses artificial intelligence (AI) in otolaryngology and its clinical application.
• Presents a patient perspective on big data in otolaryngology and its use in clinical care, as well as a glimpse into the future of big data.
• Compiles the knowledge and expertise of leading experts in the field who have assembled the most up-to-date recommendations for managing big data in otolaryngology.
• Consolidates today’s available information on this timely topic into a single, convenient resource.

Author Information

Edited by Jennifer A. Villwock, MD, Associate Professor, Otolaryngology-Head and Neck Surgery, The University of Kansas Medical Center, Kansas City, Kansas

CONTENTS

1. Introduction: Big Data – Science Fiction or Clinically Relevant?

2. Large Administrative Datasets: Lessons and Limitations

3. Biologic Big Data: Introduction to Genomics, Proteomics, and Metabolomics

4. Sources of High-Dimensional Data – The Electronic Health Record, Health Systems, and Insurance and Payor Data

5. Best Practices When Interpreting Big Data Studies: Considerations and Red Flags

6. Current Big Data Approaches to Clinical Questions in Otolaryngology

7. Translating Big Data to Patient Care

8. Bias in Big Data: Historically Underrepresented Groups and Implications

9. Artificial Intelligence in Otolaryngology

10. Clinical Applications of Artificial Intelligence: Clinical Decision Aids, Imaging Analysis, and Disease Prediction

11. The Patient Perspective on Big Data and Its Use in Clinical Care