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.

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

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

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

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

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.

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-identification Describes how to measure re-identification risk Explains how to reduce the risk of re-identification Includes proofs and supporting reference material Focuses only on transformations proven to work on health information—rather than covering all possible approaches, whether they work in practice or not

Rated 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.

Due to the digitization of medical records, more and more health data is readily available. This dynamic has created many opportunities to unlock this information and use it to improve medical practice, and through research and surveillance understand the effectiveness and side effects of drugs and medical devices to ultimately improve the public's health. This data can also be used for commercial purposes such as sales and marketing. However, this newfound utility raises some profound questions about how this data ought to be used and how it will impact personal privacy. Unless we are able to address these privacy issues in a convincing and defensible way, there will be increased breaches of personal privacy. This will provoke regulators to impose new rules limiting the use and disclosure of health data for secondary purposes, patients increasingly to adopt privacy protective behaviours because they no longer trust how their health information is being managed, or healthcare providers to be reluctant to share their patients' data.By adopting responsible data sharing practices, researchers, companies and the general public can gain the benefits and the promise of big data analytics without sacrificing personal privacy or infringing upon law or regulation. Risky Business Sharing Health Data While Protecting Privacy illustrates how this goal can be achieved. Bringing articles from a diverse collection of health data experts to inform the reader on contemporary policy, legal and technical issues surrounding health information privacy and data sharing. It is a uniquely practical work to inform the reader on how best and how not to share health data in the US and Canada.

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-identification Describes how to measure re-identification risk Explains how to reduce the risk of re-identification Includes proofs and supporting reference material Focuses only on transformations proven to work on health information-rather than covering all possible approaches, whether they work in practice or not Rated 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 provides useful and meaningful analytics? With this practical book, data architects and engineers will learn how to establish and integrate secure, repeatable anonymization processes into their data flows and analytics in a sustainable manner.

Luk Arbuckle and Khaled El Emam from Privacy Analytics explore end-to-end solutions for anonymizing device and IoT data, based on collection models and use cases that address real business needs. These examples come from some of the most demanding data environments, such as healthcare, using approaches that have withstood the test of time.

Create anonymization solutions diverse enough to cover a spectrum of use casesMatch your solutions to the data you use, the people you share it with, and your analysis goalsBuild anonymization pipelines around various data collection models to cover different business needsGenerate an anonymized version of original data or use an analytics platform to generate anonymized outputsExamine the ethical issues around the use of anonymized data

How can you use data in a way that protects individual privacy but still provides useful and meaningful analytics? With this practical book, data architects and engineers will learn how to establish and integrate secure, repeatable anonymization processes into their data flows and analytics in a sustainable manner.

Luk Arbuckle and Khaled El Emam from Privacy Analytics explore end-to-end solutions for anonymizing device and IoT data, based on collection models and use cases that address real business needs. These examples come from some of the most demanding data environments, such as healthcare, using approaches that have withstood the test of time.

Create anonymization solutions diverse enough to cover a spectrum of use casesMatch your solutions to the data you use, the people you share it with, and your analysis goalsBuild anonymization pipelines around various data collection models to cover different business needsGenerate an anonymized version of original data or use an analytics platform to generate anonymized outputsExamine the ethical issues around the use of anonymized data

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.

Building and testing machine learning models requires access to large and diverse data. But where can you find usable datasets without running into privacy issues? This practical book introduces techniques for generating synthetic data—fake data generated from real data—so you can perform secondary analysis to do research, understand customer behaviors, develop new products, or generate new revenue.

Data scientists will learn how synthetic data generation provides a way to make such data broadly available for secondary purposes while addressing many privacy concerns. Analysts will learn the principles and steps for generating synthetic data from real datasets. And business leaders will see how synthetic data can help accelerate time to a product or solution.

This book describes:

Steps for generating synthetic data using multivariate normal distributionsMethods for distribution fitting covering different goodness-of-fit metricsHow to replicate the simple structure of original dataAn approach for modeling data structure to consider complex relationshipsMultiple approaches and metrics you can use to assess data utilityHow analysis performed on real data can be replicated with synthetic dataPrivacy implications of synthetic data and methods to assess identity disclosure

Building and testing machine learning models requires access to large and diverse data. But where can you find usable datasets without running into privacy issues? This practical book introduces techniques for generating synthetic data—fake data generated from real data—so you can perform secondary analysis to do research, understand customer behaviors, develop new products, or generate new revenue.

Data scientists will learn how synthetic data generation provides a way to make such data broadly available for secondary purposes while addressing many privacy concerns. Analysts will learn the principles and steps for generating synthetic data from real datasets. And business leaders will see how synthetic data can help accelerate time to a product or solution.

This book describes:

Steps for generating synthetic data using multivariate normal distributionsMethods for distribution fitting covering different goodness-of-fit metricsHow to replicate the simple structure of original dataAn approach for modeling data structure to consider complex relationshipsMultiple approaches and metrics you can use to assess data utilityHow analysis performed on real data can be replicated with synthetic dataPrivacy implications of synthetic data and methods to assess identity disclosure