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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 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
About the author
Dr. Khaled El Emam is an Associate Professor at the University of Ottawa, Faculty of Medicine, a senior investigator at the Children's Hospital of Eastern Ontario Research Institute, and a Canada Research Chair in Electronic Health Information at the University of Ottawa. He is also the Founder and CEO of Privacy Analytics, Inc. His main area of research is developing techniques for health data de-identification/anonymization and secure computation protocols for health research and public health purposes. He has made many contributions to the health privacy area. Luk Arbuckle has been crunching numbers for a decade. He originally plied his trade in the area of image processing and analysis, and then in the area of applied statistics. Since joining the Electronic Health Information Laboratory (EHIL) at the CHEO Research Institute he has worked on methods to de-identify health data, participated in the development and evaluation of secure computation protocols, and provided all manner of statistical support. As a consultant with Privacy Analytics, he has also been heavily involved in conducting risk analyses on the re-identification of patients in health data.
Summary
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 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
