data privacy

September 28, 2018: Assessing and Reducing Risk of Re-identification When Sharing Sensitive Research Datasets (Greg Simon, MD, MPH, Deven McGraw, JD, MPH, Khaled El Emam, PhD)

Posted on by Gina Uhlenbrauck

Speakers

Gregory Simon MD, MPH
Investigator, Kaiser Permanente Washington Health Research Institute

Deven McGraw, JD, MPH, LLM
General Counsel & Chief Regulatory Officer, Ciitizen

Khaled El Emam, PhD
Department of Pediatrics, University of Ottawa
Children’s Hospital of Eastern Ontario Research Institute

Topic

Assessing and Reducing Risk of Re-identification When Sharing Sensitive Research Datasets

Keywords

Clinical trials; Research ethics; Data security; Data sharing; Sensitive research data; De-identified data

Key Points

  • The cycle of risk de-identification involves setting a risk threshold, measuring the risk, evaluating the risk, and applying transformations to reduce the risk.
  • The Safe Harbor method of de-identification (removal of 18 categories of data) is a legal minimum standard that does not take context into account, and may not be sufficient when sharing sensitive data publicly.
  • A higher standard for de-identification is the “Expert Determination” method, whereby an expert with contextual knowledge of the broader data ecosystem can determine whether the risk is “not greater than very small.”
  • With increasing concern about the risks of sensitive data sharing, it is important to be transparent with data participants and continue to build trust for data uses.

Discussion Themes

When is a dataset safe for sharing? What is the risk of re-identification, and how can we reduce the risk? Consider who you are releasing the data to and what other kinds of data might they have access to that could potentially lead to re-identification.

For more information on the de-identification of protected health information, visit the U.S. Department of Health and Human Services’s Guidance Regarding Methods for De-identification of Protected Health Information in Accordance with the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule.

The Health Information Trust Alliance de-identification framework identifies 12 criteria for a successful de-identification program and methodology.

Tags

#pctGR, #PragmaticTrials, #HealthData, @HealthPrivacy @Collaboratory1, @PCTGrandRounds

September 14, 2018: Advancing the Use of Mobile Technologies for Data Capture & Improved Clinical Trials (John Hubbard, PhD, Barry Peterson, PhD, Cheryl Grandinetti, PharmD)

Posted on by Gina Uhlenbrauck

Speakers

John Hubbard, PhD
Healthcare Strategic Advisory Board
Genstar Capital

Barry Peterson, PhD
Independent Consultant

Cheryl Grandinetti, PharmD
Office of Compliance, Office of Scientific Investigations, Division of Clinical Compliance Evaluation
Center for Drug Evaluation and Research
Food and Drug Administration

Topic

Advancing the Use of Mobile Technologies for Data Capture & Improved Clinical Trials

Keywords

Clinical trials; Mobile health technologies; Clinical Trials Transformation Initiative; CTTI; FDA; Data integrity

Key Points

  • The goal of CTTI’s Mobile Clinical Trials program is to develop evidence-based recommendations that affect the widespread adoption and use of mobile technology in clinical trials for regulatory submission.
  • Potential benefits of using mobile technology include higher quality, patient-centric endpoints and fewer barriers to participation in clinical trials.
  • Data access issues to consider before selecting a mobile technology include:
    • How will the data generated by the mobile technology be accessed and used by the manufacturer?
    • What data will be provided by the manufacturer to the sponsor?
  • The mobile era creates new data security demands.

Discussion Themes

CTTI’s recommendations aim to help sponsors determine the right device to use, how to write the protocol for remote data capture, and how to protect and analyze the data.

Know what you want to measure before selecting the mobile technology. The appropriateness of the selection should be justified through verification and validation processes.

Ensure the authenticity, integrity, and confidentiality of data over its entire lifecycle.

To reduce risk in large trials, conduct feasibility studies before full implementation.

Visit CTTI for more recommendations and resources for mobile clinical trials.

 

Tags

@CTTI_Trials, @PCTGrandRounds, #MobileTech, #pctGR

August 13, 2018: JAMA Commentary Highlights the Value of Data Enclaves and Distributed Data Networks

Posted on by Gina Uhlenbrauck

In a JAMA Viewpoint published online last week, NIH Collaboratory investigator Dr. Richard Platt and colleague Dr. Tracy Lieu discuss the value of “data enclaves” to facilitate information sharing in support of research, quality improvement, and public health reporting.

Creating data enclaves allows health systems to share useful information from their clinical data without releasing the actual data. Data enclaves can be linked with each other in distributed data networks to create powerful resources for researchers and other analysts. The authors note that efforts to realize this vision must address concerns about protecting patients’ personal information, the costs and work required to make the data usable for analysis, and incentives for health systems to participate.

Dr. Platt is a cochair of the NIH Collaboratory’s Distributed Research Network, which uses a common data model that enables investigators to collaborate with each other in the use of electronic health data while safeguarding protected health information and proprietary data.

June 7, 2018: NIH Releases First Strategic Plan for Data Science

Posted on by Damon Seils

On June 4, the National Institutes of Health (NIH) released its first Strategic Plan for Data Science. The plan outlines steps the agency will take to modernize research data infrastructure and resources and to maximize the value of data generated by NIH-supported research.

Data science challenges for NIH have evolved and grown rapidly since the launch of the Big Data to Knowledge (BD2K) program in 2014. The most pressing challenges include the growing costs of data management, limited interconnectivity and interoperability among data resources, and a lack of generalizable tools to transform, analyze, and otherwise support the usability of data for researchers, institutions, industry, and the public.

The goals of the NIH Strategic Plan for Data Science are to:

  • support an efficient, effective data infrastructure by optimizing data storage, security, and interoperability;
  • modernize data resources by improving data repositories, supporting storage and sharing of individual data sets, and integrating clinical and observational data;
  • develop and disseminate both generalizable and specialized tools for data management, analytics, and visualization;
  • enhance workforce development for data science by expanding NIH’s internal data science workforce and supporting expansion of the national research workforce, and by engaging a broader community of experts and the general public in developing best practices; and
  • enact policies that promote stewardship and sustainability of data science resources.

As part of the implementation of the strategic plan, the NIH will hire a chief data strategist.

In Nature: The Precision Medicine Initiative & DNA Data Sharing

Posted on by Gina Uhlenbrauck

A recent article in Nature highlights the Precision Medicine Initiative, launched in January 2015 and spearheaded by the National Institutes of Health. Precision medicine is an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person. This initiative will involve collection of data on genomes, electronic health records, and physiological measurements from 1 million participants. A main objective is for participants to be active partners in research.

But a major decision faced by the initiative’s working group is how much information to share with participants about disease risk, particularly genetic data. Though there is much debate in the field, the article suggests that public opinion on data sharing may be shifting toward openness.

The Precision Medicine Initiative working group will be releasing a plan soon. For details on the goals of the Precision Medicine Initiative, read the perspective by NIH Director Dr. Francis Collins in the New England Journal of Medicine.

 

In the News: Increase in Use of Personal Health Data

Posted on by Gina Uhlenbrauck

An explosion in the collection of personal data is fostering concerns about the extent to which health information is accessed—and about the privacy and confidentiality of this information. Two recent National Public Radio stories highlight a few of the burgeoning uses of these abundant data.

In the first, an insurer uses personal data to predict who will get sick so it can identify patients at highest risk for hospital admission, or readmission, and then provide them with personal health coaches. The coordinated care given to patients by the coaches (for example, arranging a visiting nurse or streamlining appointments) has been shown to improve hospitalization rates. The insurer says it follows federal health privacy guidelines for anonymity and uses the information to better serve its members.

The second story explains that results of online health searches aren’t always confidential, and data brokers are tracking information and selling it to interested parties. The author notes that data gathered on the Web are, for the most part, unregulated. Both stories raise questions about privacy and confidentiality of health information and how to best protect it.

Pragmatic clinical trials also seek to use personal health data to answer important questions on the risks, benefits, and burdens of therapeutic interventions. In a blog post in Health Affairs, Joe Selby, executive director of the Patient-Centered Outcomes Research Institute (PCORI), underscores the need for trust, support, and active engagement of patients when involving them in health data research, even with privacy protections in place. PCORI has launched the National Patient-Centered Clinical Research Network (PCORnet) as a means of harnessing large clinical data sets to study the comparative effectiveness of treatments, and a central tenet of the network is that patients, clinicians, and healthcare systems should be actively involved in the governance of the use of health information.

Read the full articles

From NPR: Insurer Uses Personal Data To Predict Who Will Get Sick
From NPR: Online Health Searches Aren't Always Confidential
From Health Affairs: Advancing the Use of Health Data in Research With PCORnet

 

Latest Truven Health Analytics–NPR Health Poll on Medical Data Privacy

Posted on by Gina Uhlenbrauck

How concerned are people about the privacy of their medical information? Not very—according to the November 2014 Truven Health Analytics–NPR Health Poll (opens as PDF). The poll asked how respondents feel about sharing their electronic health information and other data with researchers, employers, health plans, and their doctors. The majority expressed a willingness to share their anonymized health information with researchers; less than a quarter expressed willingness to share non-healthcare data with their healthcare providers.

Each month, the Truven Health Analytics–NPR Health Poll surveys approximately 3,000 Americans to gauge attitudes and opinions on a wide range of healthcare issues. Poll results are reported by NPR on the health blog Shots. Among the results of this survey:

  • 74% of respondents indicated that their physician uses an electronic medical record system.
  • 68% of respondents would share their health information anonymously with researchers.
  • 44% of respondents have looked through their health information kept by their physician.

The survey analyses were stratified by age, education, generation, and income. Poll questions were posed by cell phone, land line, and online during the first half of August 2014. The margin of error was plus or minus 1.8 percentage points. An executive summary of the survey, including questions and survey data, is here.