Anonymizing Health Data
Anonymizing Health Data by Khaled El Emam, published by O’Reilly Media, Inc. in 2014, is a practical guide that explores effective methods for anonymizing health data to facilitate the sharing of meaningful datasets while protecting patient identities. This edition, comprising 197 pages, addresses the complexities of maintaining data quality during the anonymization process, drawing on the authors’ extensive experience with various datasets, including maternal-child registries and electronic medical records.
Readers will find a comprehensive examination of risk-based methodologies for de-identifying health data, supported by case studies that illustrate the authors’ approaches. The book covers techniques for managing both cross-sectional and longitudinal datasets, assessing risks associated with potential re-identification, and minimizing the complexities of large datasets without compromising privacy. Additionally, it discusses strategies for anonymizing unstructured text data and geospatial information, making it a valuable resource for professionals in the fields of data science, health informatics, and online privacy.
Official synopsis Publisher
Updated as of August 2014, this practical book will demonstrate 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 datasetsAssess the risk of adversaries who attempt to re-identify patients in anonymized datasetsReduce the size and complexity of massive datasets without losing key information or jeopardizing privacyUse methods to anonymize unstructured free-form text dataMinimize the risks inherent in geospatial data, without omitting critical location-based health informationLook at ways to anonymize coding information in health dataLearn the challenge of anonymously linking related datasets
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