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-ba

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-ba

Data sharing can accelerate new discoveries by avoiding duplicative trials, stimulating new ideas for research, and enabling the maximal scientific knowledge and benefits to be gained from the efforts of clinical trial participants and investigators. At the same time, sharing clinical trial data presents risks, burdens, and challenges. These include the need to protect the privacy and honor the consent of clinical trial participants; safeguard the legitimate economic interests of sponsors; and guard against invalid secondary analyses, which could undermine trust in clinical trials or otherwise harm public health. Sharing Clinical Trial Data presents activities and strategies for the responsible sharing of clinical trial data. With the goal of increasing scientific knowledge to lead to better therapies for patients, this book identifies guiding principles and makes recommendations to maximize the benefits and minimize risks. This report offers guidance on the types of clinical trial data available at different points in the process, the points in the process at which each type of data should be shared, methods for sharing data, what groups should have access to data, and future knowledge and infrastructure needs. Responsible sharing of clinical trial data will allow other investigators to replicate published findings and carry out additional analyses, strengthen the evidence base for regulatory and clinical decisions, and increase the scientific knowledge gained from investments by the funders of clinical trials. The recommendations of Sharing Clinical Trial Data will be useful both now and well into the future as improved sharing of data leads to a stronger evidence base for treatment. This book will be of interest to stakeholders across the spectrum of research-from funders, to researchers, to journals, to physicians, and ultimately, to patients.

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

This User’s Guide is intended to support the design, implementation, analysis, interpretation, and quality evaluation of registries created to increase understanding of patient outcomes. For the purposes of this guide, a patient registry is an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, and that serves one or more predetermined scientific, clinical, or policy purposes. A registry database is a file (or files) derived from the registry. Although registries can serve many purposes, this guide focuses on registries created for one or more of the following purposes: to describe the natural history of disease, to determine clinical effectiveness or cost-effectiveness of health care products and services, to measure or monitor safety and harm, and/or to measure quality of care. Registries are classified according to how their populations are defined. For example, product registries include patients who have been exposed to biopharmaceutical products or medical devices. Health services registries consist of patients who have had a common procedure, clinical encounter, or hospitalization. Disease or condition registries are defined by patients having the same diagnosis, such as cystic fibrosis or heart failure. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews.

The escalation of security breaches involving personally identifiable information (PII) has contributed to the loss of millions of records over the past few years. Breaches involving PII are hazardous to both individuals and org. Individual harms may include identity theft, embarrassment, or blackmail. Organ. harms may include a loss of public trust, legal liability, or remediation costs. To protect the confidentiality of PII, org. should use a risk-based approach. This report provides guidelines for a risk-based approach to protecting the confidentiality of PII. The recommend. here are intended primarily for U.S. Fed. gov¿t. agencies and those who conduct business on behalf of the agencies, but other org. may find portions of the publication useful.

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.

This report identifies eight key data governance mechanisms to maximise benefits to patients and to societies from the collection, linkage and analysis of health data, and to minimise risks to both patient privacy and the security of health data.

NISTIR 8053 October 2015 De-identification removes identifying information from a dataset so that individual data cannot be linked with specific individuals. De-identification can reduce the privacy risk associated with collecting, processing, archiving, distributing or publishing information. De-identification thus attempts to balance the contradictory goals of using and sharing personal information while protecting privacy. Several U.S laws, regulations and policies specify that data should be de-identified prior to sharing. In recent years researchers have shown that some de-identified data can sometimes be re-identified. Many different kinds of information can be de-identified, including structured information, free format text, multimedia, and medical imagery. This document summarizes roughly two decades of de-identification research, discusses current practices, and presents opportunities for future research. Why buy a book you can download for free? First you gotta find it and make sure it's the latest version (not always easy). Then you gotta print it using a network printer you share with 100 other people - and its outta paper - and the toner is low (take out the toner cartridge, shake it, then put it back). If it's just 10 pages, no problem, but if it's a 250-page book, you will need to punch 3 holes in all those pages and put it in a 3-ring binder. Takes at least an hour. An engineer that's paid $75 an hour has to do this himself (who has assistant's anymore?). If you are paid more than $10 an hour and use an ink jet printer, buying this book will save you money. It's much more cost-effective to just order the latest version from Amazon.com This book is published by 4th Watch Books and includes copyright material. We publish compact, tightly-bound, full-size books (8 � by 11 inches), with glossy covers. 4th Watch Books is a Service Disabled Veteran-Owned Small Business (SDVOSB), and is not affiliated with the National Institute of Standards and Technology. For more titles published by 4th Watch Books, please visit: cybah.webplus.net NIST SP 500-299 NIST Cloud Computing Security Reference Architecture NIST SP 500-291 NIST Cloud Computing Standards Roadmap Version 2 NIST SP 500-293 US Government Cloud Computing Technology Roadmap Volume 1 & 2 NIST SP 500-293 US Government Cloud Computing Technology Roadmap Volume 3 DRAFT NIST SP 1800-8 Securing Wireless Infusion Pumps NISTIR 7497 Security Architecture Design Process for Health Information Exchanges (HIEs) NIST SP 800-66 Implementing the Health Insurance Portability and Accountability Act (HIPAA) Security Rule NIST SP 1800-1 Securing Electronic Health Records on Mobile Devices NIST SP 800-177 Trustworthy Email NIST SP 800-184 Guide for Cybersecurity Event Recovery NIST SP 800-190 Application Container Security Guide NIST SP 800-193 Platform Firmware Resiliency Guidelines NIST SP 1800-1 Securing Electronic Health Records on Mobile Devices NIST SP 1800-2 Identity and Access Management for Electric Utilities NIST SP 1800-5 IT Asset Management: Financial Services NIST SP 1800-6 Domain Name Systems-Based Electronic Mail Security NIST SP 1800-7 Situational Awareness for Electric Utilities

The Model Rules of Professional Conduct provides an up-to-date resource for information on legal ethics. Federal, state and local courts in all jurisdictions look to the Rules for guidance in solving lawyer malpractice cases, disciplinary actions, disqualification issues, sanctions questions and much more. In this volume, black-letter Rules of Professional Conduct are followed by numbered Comments that explain each Rule's purpose and provide suggestions for its practical application. The Rules will help you identify proper conduct in a variety of given situations, review those instances where discretionary action is possible, and define the nature of the relationship between you and your clients, colleagues and the courts.