data sharing

May 10, 2019: Treating Data as an Asset: Data Entrepreneurship in the Service of Patients (Eric Perakslis, PhD)

Posted on by Gina Uhlenbrauck

Speaker

Eric D. Perakslis MS, PhD
Rubenstein Fellow, Duke University
Lecturer, Department of Biomedical Informatics
Harvard Medical School

Topic

Treating Data as an Asset: Data Entrepreneurship in the Service of Patients

Keywords

Digital health; Health data; General Data Protection Regulation (GDPR); Data sharing

Key Points

  • The only 100% common element of digital transformation across all industries is data.
  • With data and digital transformation, patients are changing: They are active, connected, informed, and savvy.
  • Security, compliance, and privacy are different things.

Discussion Themes

Is there any hope of data sharing policies helping to bridge the micro and macro silos of healthcare data?

As data starts to flows through institutions, it ends up in multiple places. Part of sharing data is protecting a single source of truth.

If something is relevant to the bedside, it’s worth doing.

Read Dr. Perakslis’s commentary in The Lancet (May 2019).

Tags

#healthdata, #pctGR, @Collaboratory1

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

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.

March 21, 2018: Dr. Rob Califf to Speak on Data Science at March 23 Grand Rounds

Posted on by Gina Uhlenbrauck

Robert Califf, MD, former FDA Commissioner and current Vice Chancellor for Health Data Science at Duke University School of Medicine, will present at NIH Collaboratory Grand Rounds on Friday, March 23 at 1 pm ET. The webinar will be broadcast live and is open to the public. Following the presentation, Dr. Califf will answer questions from the Grand Rounds audience.

As Director of Duke Forge, Duke’s interdisciplinary center for actionable health data science, Dr. Califf is currently working on initiatives designed to harness biostatics, machine learning, and sophisticated informatics approaches to improve health and healthcare. Dr. Califf is also an adjunct professor of medicine at Stanford University and is employed by Verily Life Sciences as a scientific advisor. Verily, part of the Alphabet (Google) family of companies, is aimed at transforming the growth of health-related data into practical applications.

Dr. Califf has been a pioneer in the fields of clinical, translational, and outcomes research, and the NIH Collaboratory looks forward to hearing his thoughts on the pragmatic applications of data that will advance health and health care strategies and practice.

Topic: Data Science in the Era of Data Ubiquity

Date: Friday, March 23, 2018, 1:00-2:00 p.m. ET

Meeting Info: To check whether you have the appropriate players installed for UCF (Universal Communications Format) rich media files, go to https://dukemed.webex.com/dukemed/systemdiagnosis.php.

To join the online meeting:
Go to https://dukemed.webex.com/dukemed/j.php?MTID=m1a4a0665a615ae0382440edecedbdd33

November 17, 2017: New Video in Living Textbook Explores Data Sharing and Embedded Research

Posted on by Karen Staman

As part of an article published in Annals of Internal Medicine, Dr. Greg Simon created a short video in which he describes concerns related to data sharing and embedded research, as well as potential solutions for those concerns. We recently added this video to the Living Textbook chapter on Data Sharing and Embedded Research. In the chapter, the authors expand on the ideas presented in the Annals article and fame them using lessons learned from the NIH Collaboratory Trials. Data collected as part of research embedded in a health system comes from a fundamentally different context than stand-alone explanatory trials. When they are taken out of context or used for comparisons, they have the potential to do harm—something that can potentially discourage health systems from volunteering to participate in embedded research. The authors suggest that data sharing plans for embedded research be developed in partnership with health system leaders in ways that maximize the amount of data that can be shared while protecting patient privacy and healthcare system interests.

“Ultimately, it’s a practical question: if we want healthcare providers and healthcare systems to participate in research, we shouldn’t expect them to bear extra risk. In an ideal world, all information about the quality of health care and healthcare outcomes across the country would be completely open to everyone, but we don’t live in that world now. So if we are asking healthcare providers and healthcare systems to open up and be more transparent by participating in research, we certainly would not want to punish those who volunteer.” — Simon et al. in video for Ann Intern Med

 

Simon G, Coronado G, DeBar L, et al. Data Sharing and Embedded Research: Introduction. In: Rethinking Clinical Trials: A Living Textbook of Pragmatic Clinical Trials. Bethesda, MD: NIH Health Care Systems Research Collaboratory. Available at: https://rethinkingclinicaltrials.org/data-share-top/data-sharing-and-embedded-research-introduction/. Updated November 13, 2017.

October 3, 2017: New Collaboratory Article Explores Data Sharing and Embedded Research

Posted on by Karen Staman

In an article published in Annals of Internal Medicine, authors from the NIH Collaboratory describe concerns and solutions regarding data sharing and embedded research. Pragmatic research embedded in health systems uses data from the electronic health record and comes from a fundamentally different context than explanatory trials, which collect research-specific data. Data from embedded research have the potential to do harm if taken out of context or used for comparisons. Therefore, while the authors enthusiastically support data sharing, they also recognize that mandating data sharing may discourage health systems from volunteering to participate in embedded research.

“In an ideal world of transparency regarding healthcare processes and outcomes, health systems would have no expectation of or need for privacy regarding quality of health care delivery.  But the current world is not perfect, and unintentional disclosures from participation in embedded research could be far greater than that required for public quality measures. Health systems volunteering to participate in research to improve public health may not be willing to bear the additional risk of misuse of sensitive information.” — Simon et al. Ann Intern Med

The authors use examples from the NIH Collaboratory Trials to illustrate potential solutions, and emphasize that data sharing plans for embedded research should be developed in partnership with health system leaders in ways that maximize the amount of data that can be shared while protecting patient privacy and healthcare system interests.

New Living Textbook Chapter on Acquiring and Using Electronic Health Record Data for Research

Posted on by Karen Staman

Topic ChaptersMeredith Nahm Zozus and colleagues from the NIH Collaboratory’s Phenotypes, Data Standards, and Data Quality Core (now the Electronic Health Records Core) have published a new Living Textbook chapter about key considerations for secondary use of electronic health record (EHR) data for clinical research.

In contrast to traditional randomized controlled clinical trials where data are prospectively collected, many pragmatic clinical trials use data that were primarily collected for clinical purposes and are secondarily used for research. The chapter describes the steps a prospective researcher will take to acquire and use EHR data:

  • Gain permission to use the data. When a prospective researcher wishes to use data, a data use agreement (DUA) is usually required that describes the purpose of the research and the proposed use of the data. This section also describes use of de-identified data and limited data sets.
  • Understand fundamental differences in context. Data collected in routine care settings reflect standard procedures at an individual’s healthcare facility, and are not collected in a standard, structured manner.
  • Assess the availability of health record data. Few assumptions can be made about what is available from an organization’s healthcare records; up-front, detailed discussions about data element collection over time at each facility is required.
  • Understand the available data. A secondary data user must understand both the data meaning and the data quality; both can vary greatly across organizations and affect a study’s ability to support research conclusions.
  • Identify populations and outcomes of interest. Because healthcare facilities are obligated to provide only the minimum necessary data to answer a research question, investigators must identify the needed patients and data elements with specificity and sensitivity to answer the research question given the available data.
  • Consider record linkage. Studies using data from multiple records and sources will require matching data to ensure they refer to the correct patient.
  • Manage the data. The investigator is responsible for receiving, managing, and processing data and must demonstrate that the data are reproducible and support research conclusions.
  • Archive and share the data after the study. Data may be archived and shared to ensure reproducibility, enable auditing for quality assurance and regulatory compliance, or to answer other questions about the research.

Task Force Releases Recommendations for National Medical Device Evaluation System

Posted on by Gina Uhlenbrauck

A new report (PDF) containing recommendations for the creation of a national registry system for evaluating and monitoring medical devices has been released for public comment today. The report, a joint project of the Medical Device Registry Task Force and cover_19aug2015 the Medical Device Epidemiology Network (MDEpiNet), is available on boh the US Food and Drug Administration (FDA) website and on  the MDEpiNet website.

The report reflects the results of a year-long effort, prompted by the FDA’s Center for Devices and Radiological Health (CDER), that  is focused on fostering a national system for monitoring the use of medical devices in the “real-world” setting of patient care, once the devices have been approved for the market (known as “postmarket surveillance”).

The term “medical devices” encompasses a wide range of technologies, including implantable pacemakers, cardiovascular stents, robotic surgical devices, and artificial joint replacements, among many others. At present, information about the use of these devices in routine care settings, including safety issues reported by doctors and patients, is collected in a variety of registries and health record systems. A  networked national system, such as the one described in the task force report, would be able to unite and build upon both existing and novel data resources, thereby improving safety monitoring and accelerating the development of new devices:

“Task Force recommendations for [Coordinated Registry Network] CRN architecture, and thus for the National System, center on leveraging existing, self sustaining electronic resources, such as device registries, electronic health records, administrative data and even social media and personal mobile device sources.”

The Task Force Report offers recommendation in several key areas, including:

  • Establishing a national dialog about medical device evaluation that includes all stakeholders;
  • Leveraging existing efforts in the arena of device registries and electronic data systems;
  • Describing the desired characteristics of a national Coordinated Registry Network (CRN) for medical devices;
  • Outlining priorities for developing and refining medical devices in multiple therapeutic areas;
  • Identifying and improving methods for analyzing data on medical devices; and
  • Addressing network governance and issues related to patient privacy and informed consent.

Each of these key areas also features suggested pilot projects designed to inform ongoing efforts.

A related perspective article summarizing the National Registry System project has also been published online in the Journal of the American Medical Association.

Related Links

Study Examines Public Attitudes Toward Data-Sharing Networks

Posted on by Gina Uhlenbrauck

A new study examining public attitudes about the sharing of personal medical data through health information exchanges and distributed research networks finds a mixture of receptiveness and concerns about privacy and security. The study, conducted by researchers from the University of California, Davis and University of California, San Diego and published online in the Journal of the American Medical Informatics Association (JAMIA), reports results from a telephone survey of 800 California residents. Participants were asked for their opinions about the importance of sharing personal health data for research purposes and their feelings about related issues of security and privacy, as well as the importance of notification and permission for such sharing.

The authors found that a majority of respondents felt that sharing health data would “greatly improve” the quality of medical care and research. Further, many either somewhat or strongly agreed that the potential benefits of sharing data for research and care improvement outweighed privacy considerations (50.8%) or the right to control the use of their personal information (69.8%), although study participants also indicated that transparency regarding the purpose of any data sharing and controlling access to data remained important considerations.

However, the study’s investigators also found evidence of widespread concern over privacy and security issues, with substantial proportions of respondents reporting a belief that data sharing would have negative effects on the security (42.5%) and privacy (40.3%) of their health data. The study also explored attitudes about the need to obtain permission for sharing health data, as well as whether attitudes toward sharing data differed according to the purpose (e.g., for research vs. care) and the groups or individuals among which the data were being shared.

The authors note that while data-sharing networks are increasingly viewed as a crucial tool for enabling research and improving care on a national scale, they ultimately rely upon trust and acceptance from patients. As such, the long-term success of efforts aimed at building effective data-sharing networks may depend on accurately understanding the views of patients and accommodating their concerns.

Read the full article here: 

Kim KK, Joseph JG, Ohno-Machado L. Comparison of consumers' views on electronic data sharing for healthcare and research. J Am Med Inform Assoc. 2015 Mar 30. pii: ocv014. doi: 10.1093/jamia/ocv014. [Epub ahead of print]