Anonymizing Health Data
The experts’ answer to getting started with anonymization.
With Anonymizing Health Data: Case Studies and Methods to Get You Started, you will learn proven methods for anonymizing health data to help your organization share meaningful, de-identified health data, without exposing patient identity. Leading experts, Khaled El Emam and Luk Arbuckle, walk you through a risk-based methodology, using case studies from their experience de-identifying hundreds of thousands of datasets.
Protected health information is valuable for research and other types of analytics, but making it safe for secondary purposes without compromising data quality is tricky. This book demonstrates techniques for handling different data types, based on the authors’ experiences with a various datasets. Case studies include a maternal-child registry, inpatient discharge abstracts, health insurance claims, electronic medical record databases, and the World Trade Center disaster registry, among others.
- Understand different methods for working with cross-sectional and longitudinal data sets
- Assess the risk of adversaries who attempt to re-identify patients in anonymized data sets
- Reduce the size and complexity of massive data sets without losing key information or jeopardizing privacy
- Use methods to anonymize unstructured free-form text data
- Minimize the risks inherent in geospatial data, without omitting critical location-based health information
- Look at ways to anonymize coding information in health data
- Learn the challenge of anonymously linking related datasets