
Big Data in Otolaryngology
- 1st Edition - July 17, 2024
- Editor: Jennifer A. Villwock
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 0 5 2 0 - 3
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 0 5 2 1 - 0
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… Read more

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Covers the clinical relevance of big data in otolaryngology, lessons and limitations of large administrative datasets, biologic big data, and much more.
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Discusses artificial intelligence (AI) in otolaryngology and its clinical application.
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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.
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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.
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Consolidates today's available information on this timely topic into a single, convenient resource.
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Chapter 1. Big data—Science fiction or clinically relevant?
- Introduction
- Limitations of big data
- Opportunities in big data
- Looking ahead
- Chapter 2. Large administrative datasets: Lessons and limitations
- Introduction
- Overview of large databases
- Strengths of large databases
- Limitations of large databases
- National cancer database
- Surveillance, epidemiology, and end results program
- SEER-medicare
- National ambulatory medical care survey/National hospital ambulatory medical care survey
- Pediatric health information system
- Reg-ent clinical data registry
- Conclusion
- Chapter 3. Sources of high-dimensional data—The electronic health record, health systems, and insurance and payor data
- Insurance and payment databases
- Electronic health records
- Surveys
- National Ambulatory Medical Care Survey and National Hospital Ambulatory Medical Care Survey: General ambulatory databases
- Registry data
- Reg-ent: An otolaryngology-specific registry
- Primary uses: Advancing research, improving patient outcomes, and quality improvement
- Logistics and data access
- General methods for classifying patients and cases
- Summary overview
- Chapter 4. Best practices when interpreting big data studies: Considerations and red flags
- Introduction
- Rapid expansion in big data research and areas where big data is most useful
- Common pitfalls in big data studies
- Steps to incorporating big data research into your clinical practice
- Conclusion
- Chapter 5. Current big data approaches to clinical questions in otolaryngology
- Introduction
- Chapter 6. Bias in big data: Historically underrepresented groups and implications
- Ethical principles and big data
- Biases and missing data concerns in big data studies
- Addressing the problem
- Chapter 7. Artificial intelligence in otolaryngology
- Introduction
- Fundamentals of artificial intelligence
- Interpreting artificial intelligence models
- AI in medicine: General applications and commonly used terms
- Applications of AI in otolaryngology
- Regulatory and ethical considerations
- Challenges and future directions
- Conclusion
- Chapter 8. The patient perspective on big data and its use in clinical care
- Privacy
- Demographics and bias within big data
- Trust
- Patient vulnerability
- Liability and accountability
- Data ownership/epistemic inequities
- Shift in paradigm: Community engagement
- Index
- No. of pages: 250
- Language: English
- Edition: 1
- Published: July 17, 2024
- Imprint: Elsevier
- Paperback ISBN: 9780443105203
- eBook ISBN: 9780443105210
JV