This practical book demonstrates proven methods for anonymizing health data to help your organization share meaningful datasets, without exposing patient identity. While clinical data is valuable for research and other analytics, anonymizing it without compromising data quality is tricky. This book will show you how you can maximize data value, while minimizing the possibility of identifying data subjects.
What You’ll Learn
- Different methods for working with cross-sectional and longitudinal datasets
- How to reduce the size and complexity of datasets without losing key information
- Methods to anonymize unstructured, free-form text data
Who should read this book?
Everyone working with health data, and anyone interested in privacy in general, will benefit by reading this book. Especially:
- Executive management, looking to create new revenue streams from data assets
- IT professionals, hesitant to implement data anonymization from a technical standpoint
- Data managers and analysts, unsure about the safety of their current methods
- Privacy and compliance professionals, tasked with implementing defensible solutions
Advising healthcare enterprises topping the Fortune 500, Luk has co-authored books, scholarly journal articles and patents on re-identification risk and de-identification. He has more than a decade of experience in the field of anonymization.