Anonymizing Health Data

With this practical book, you will learn proven methods for anonymizing health data to help your organization share meaningful datasets, without exposing patient identity. Leading experts Khaled El Emam and Luk Arbuckle walk you through a risk-based methodology, using case studies from their efforts to de-identify hundreds of datasets.

Clinical data is valuable for research and other types of analytics, but making it anonymous without compromising data quality is tricky. This book demonstrates techniques for handling different data types, based on the authors’ experiences with 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 datasets
  • Assess the risk of adversaries who attempt to re-identify patients in anonymized datasets
  • Reduce the size and complexity of massive datasets 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

Table of Contents
Chapter 1. Introduction
Chapter 2. A Risk-Based De-Identification Methodology
Chapter 3. Cross-Sectional Data: Research Registries
Chapter 4. Longitudinal Discharge Abstract Data: State Inpatient Databases
Chapter 5. Dates, Long Tails, and Correlation: Insurance Claims Data
Chapter 6. Longitudinal Events Data: A Disaster Registry
Chapter 7. Data Reduction: Research Registry Revisited
Chapter 8. Free-Form Text: Electronic Medical Records
Chapter 9. Geospatial Aggregation: Dissemination Areas and ZIP Codes
Chapter 10. Medical Codes: A Hackathon
Chapter 11. Masking: Oncology Databases
Chapter 12. Secure Linking
Chapter 13. De-Identification and Data Quality

Book Details

  • Paperback: 212 pages
  • Publisher: O’Reilly Media (December 2013)
  • Language: English
  • ISBN-10: 1449363075
  • ISBN-13: 978-1449363079
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