July 26, 2019: Digital in Trials: Improving Participation and Enabling Novel Endpoints (Craig H. Lipset)

Speaker

Craig H. Lipset
Former Head of Clinical Innovation, Pfizer

Topic

Digital in Trials: Improving Participation and Enabling Novel Endpoints

Keywords

Digital tools; Clinical trials; Participant experience; Patient engagement; Clinical Trials Transformation Initiative

Key Points

  • To improve trial participation, start by understanding the user/consumer; ie, the trial participant and his or her trial experience.
  • Digital improvements in clinical trials can involve these incremental steps:
    • Study planning that is data-driven, crowdsourced, and informed by artificial intelligence
    • Patient engagement that implements electronic consent, flexibility in location, digital concierge support, and data ownership
    • Study conduct that integrates remote monitoring, digital biomarkers, and electronically sourced data
    • Analysis and reporting that is automated and includes dissemination to trial participants

Discussion Themes

Will digital tools in medicine development enable improvement, disruption, or displacement?

Digital tools in development focus on breaking down barriers to participation, using digital to improve existing measurement or enable new endpoints, and automating processes and tasks while improving quality.

Tags

#pctGR, @Collaboratory1

July 12, 2019: medRxiv: A Paradigm Shift in Disseminating Clinical and Public Health Research (Harlan Krumholz, MD, SM, Joseph Ross, MD, MHS)

Speakers

Harlan M. Krumholz, MD, SM
Harold H. Hines, Jr. Professor of Medicine and Public Health
Yale University

Joseph S. Ross, MD, MHS
Associate Professor of Medicine and Public Health
Yale University

Topic

medRxiv: A Paradigm Shift in Disseminating Clinical and Public Health Research

Keywords

Open science; Clinical research dissemination; Preprints; medRxiv preprint server

Key Points

  • medRxiv (med archive) is a server for health science preprints. It is a free service to the research community, managed in partnership with BMJ and Yale.
  • Benefits of preprints in medicine include early sharing of new information; enabling less “publishable” studies to be more readily available; and facilitating replication and reproducibility studies.
  • medRxiv submissions require:
    • Following ICMJE guidance, including author names, contact info, affiliation
    • Funding and competing interests statements
    • Statement of IRB or ethics committee approval
    • Study registration (ClinicalTrials.gov or other ICMJE approved registry for trials, PROSPERO for reviews) or link to protocol
    • Data sharing availability statement
    • EQUATOR Network reporting guidelines checklists
  • The medRxiv preprint server urges caution in using and reporting preprints, and includes language explaining that preprints are preliminary reports of work that have not been peer-reviewed, should not be relied on to guide clinical practice or health-related behaviors, and should not be reported in news media as established information.

Discussion Themes

Preprint servers do not replace, but rather complement, peer review.

Preprint has the potential for being a vehicle for high-quality but “negative” results. If we teach students that a negative result is also a good result, providing an avenue for us to walk-the-talk more easily via open communication seems largely positive despite the limitations.

Read more about medRxiv.

Tags

#pctGR, @Collaboratory1, @jsross119, @hmkyale

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

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)

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

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

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

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

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 Collaboratory’s Demonstration Projects. 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

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 Demonstration Projects 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.

Journal Editors Propose New Requirements for Data Sharing

On January 20, 2016, the International Committee of Medical Journal Editors (ICMJE) published an editorial in 14 major medical journals in which they propose that clinical researchers must agree to share the deidentified data set used to generate results (including tables, figures, and appendices or supplementary material) as a condition of publication in one of their member journals no later that six months after publication. By changing the requirements for manuscripts they will consider for publication, they aim to ensure reproducibility (independent confirmation of results), foster data sharing, and enhance transparency. To meet the new requirements, authors will need to include a plan for data sharing as a component of clinical trial registration that includes where the data will be stored and a mechanism for sharing the data.

Evolving Standards for Data Reporting and Sharing

As early as 2003, the National Institutes of Health published a data sharing policy for research funded through the agency, stipulating that “Data should be made as widely and freely available as possible while safeguarding the privacy of participants, and protecting confidential and proprietary data.” Under this policy, federally funded studies receiving over $500,000 per year were required to have a data sharing plan that describes how data will be shared, that shared data be available in a usable form for some extended period of time, and that the least restrictive method for sharing of research data is used.

In 2007, Congress enacted the Food and Drug Administration Amendments Act. Section 801 of the Act requires study sponsors to report certain kinds of clinical trial data within a specified interval to the ClinicalTrials.gov registry, where it is made available to the public. Importantly, this requirement applied to any study classified as an “applicable clinical trial” (typically, an interventional clinical trial), regardless of whether it was conducted with NIH or other federal funding or supported by industry or academic funding. However, recent academic and journalistic investigations have demonstrated that overall compliance with FDAAA requirements is relatively poor.

In 2015, the Institute of Medicine (now the National Academy of Medicine) published a report that advocates for responsible sharing of clinical trial data to strengthen the evidence base, allow for replication of findings, and enable additional analyses. In addition, these efforts are being complemented by ongoing initiatives aimed at widening access to clinical trial data and improving results reporting, including the Yale University Open Data Access project (YODA), the joint Duke Clinical Research Institute/Bristol-Myers Squibb Supporting Open Access to clinical trials data for Researchers initiative (SOAR), and the international AllTrials project.

Responses to the Draft ICMJE Policy

The ICMJE recommendations are appearing in the midst of a growing focus on issues relating to the integrity of clinical research, including reproducibility of results, transparent and timely reporting of trial results, and facilitating widespread data sharing, and the release of the draft policy is amplifying ongoing national and international conversations taking place on social media and in prominent journals. Although many researchers and patient advocates have hailed the policy as timely and needed, others have expressed concerns, including questions about implementation and possible unforeseen consequences.

The ICMJE is welcoming feedback from the public regarding the draft policy at www.icmje.org and will continue to collect comments through April 18, 2016.

Resources

Journal editors publish editorial in 14 major medical journals stipulating that clinical researchers must agree to share a deidentified data set: Sharing clinical trial data: A proposal from the International Committee of Medical Journal Editors (Annals of Internal Medicine version). January 20, 2016.

A New England Journal of Medicine editorial in which deputy editor Dan Longo and editor-in-chief Jeffrey Drazen discuss details of the ICJME proposal: Data sharing. January 21, 2016.

A follow-up editorial in the New England Journal of Medicine by Jeffrey Drazen: Data sharing and the Journal. January 25, 2016.

Editorial in the British Medical Journal: Researchers must share data to ensure publication in top journals. January 22, 2016.

Commentary in Nature from Stephan Lewandowsky and Dorothy Bishop: Research integrity: Don’t let transparency damage science. January 25, 2016.

National Public Radio interview on Morning Edition: Journal editors to researchers: Show everyone your clinical data with Harlan Krumholz. January 27, 2016.

Institute of Medicine (now the National Academy of Medicine) report advocating for responsible sharing of clinical trial data: Sharing clinical trial data: maximizing benefits, minimizing risk. National Academies Press, 2015.

Rethinking Clinical Trials Living Textbook Chapter, Acquiring and using electronic health record data, which describes the use of data collected in clinical practice for research and the complexities involved in sharing data. November 3, 2015.

NIH Health Care Systems Research Collaboratory data sharing policy. June 23, 2014.

List of International Committee of Medical Journal Editors (ICMJE) member journals.