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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.
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.
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 distributions Methods for distribution fitting covering
different goodness-of-fit metrics How to replicate the simple
structure of original data An approach for modeling data structure
to consider complex relationships Multiple approaches and metrics
you can use to assess data utility How analysis performed on real
data can be replicated with synthetic data Privacy implications of
synthetic data and methods to assess identity disclosure
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.
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
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
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.
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.
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.
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