Khaled El Emam – författare

Offering compelling practical and legal reasons why de-identification should be one of the main approaches to protecting patients’ privacy, the Guide to the De-Identification of Personal Health Information outlines a proven, risk-based methodology for the de-identification of sensitive health information. It situates and contextualizes this risk-based methodology and provides a general overview of its steps.The book supplies a detailed case for why de-identification is important as well as best practices to help you pin point when it is necessary to apply de-identification in the disclosure of personal health information. It also: Outlines practical methods for de-identificationDescribes how to measure re-identification riskExplains how to reduce the risk of re-identificationIncludes proofs and supporting reference materialFocuses only on transformations proven to work on health information—rather than covering all possible approaches, whether they work in practice or notRated the top systems and software engineering scholar worldwide by The Journal of Systems and Software, Dr. El Emam is one of only a handful of individuals worldwide qualified to de-identify personal health information for secondary use under the HIPAA Privacy Rule Statistical Standard. In this book Dr. El Emam explains how we can make health data more accessible—while protecting patients’ privacy and complying with current regulations.

The ROI from Software Quality provides the tools needed for software engineers and project managers to calculate how much they should invest in quality, what benefits the investment will reap, and just how quickly those benefits will be realized. This text provides the quantitative models necessary for making real and reasonable calculations and it shows how to perform ROI analysis before and after implementing a quality program. The book demonstrates how to collect the appropriate data and easily perform the appropriate ROI analysis.Taking an evidence-based approach, this book supports its methodology with large amounts of data and backs up its positioning with numerous case studies and straightforward return-on-investment calculations. By carefully substantiating arguments, this volume separates itself from other works on ROI.

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

Offering compelling practical and legal reasons why de-identification should be one of the main approaches to protecting patients’ privacy, the Guide to the De-Identification of Personal Health Information outlines a proven, risk-based methodology for the de-identification of sensitive health information. It situates and contextualizes this risk-based methodology and provides a general overview of its steps.The book supplies a detailed case for why de-identification is important as well as best practices to help you pin point when it is necessary to apply de-identification in the disclosure of personal health information. It also: Outlines practical methods for de-identificationDescribes how to measure re-identification riskExplains how to reduce the risk of re-identificationIncludes proofs and supporting reference materialFocuses only on transformations proven to work on health information—rather than covering all possible approaches, whether they work in practice or notRated the top systems and software engineering scholar worldwide by The Journal of Systems and Software, Dr. El Emam is one of only a handful of individuals worldwide qualified to de-identify personal health information for secondary use under the HIPAA Privacy Rule Statistical Standard. In this book Dr. El Emam explains how we can make health data more accessible—while protecting patients’ privacy and complying with current regulations.

How can you use data in a way that protects individual privacy, but still ensures that data analytics will be useful and meaningful? With this practical book, data architects and engineers will learn how to implement and deploy anonymization solutions within a data collection pipeline. You'll establish and integrate secure, repeatable anonymization processes into your data flows and analytics in a sustainable manner.Luk Arbuckle and Khaled El Emam from Privacy Analytics explore end-to-end solutions for anonymizing data, based on data collection models and use cases enabled by real business needs. These examples come from some of the most demanding data environments, using approaches that have stood the test of time.