Data sharing

Grand Rounds October 4, 2024: Health Trends Across Communities – A Novel Health System-Public Health Data Partnership (Tyler Winkelman, MD, MSc; David Johnson, MPH)

Posted on by Sophia Ramirez

Speakers

Tyler Winkelman, MD, MSc
Division Director, General Internal Medicine
Hennepin Healthcare
Co-Director, Health, Homelessness, and Criminal Justice Lab
HHRI

David Johnson, MPH
Health Informatics and Epidemiology
Program Manager
Hennepin County

Keywords

Electronic Health Record; Data Sharing; Public Health; Health Systems; Partnerships

Key Points

  • Collaboration across public health and health care is essential to developing actionable data for both sectors. Electronic Health Record (EHR) data can be used to fill the gaps in public health data and foster collaboration.
  • During the COVID-19 pandemic, it became clear that data infrastructure in the U.S. was underdeveloped. This made addressing COVID-19 challenging, is currently making addressing the overdose crisis challenging, and puts the country at risk for any future epidemics.
  • The Minnesota EHR Consortium (MNEHRC), formed in March 2020, facilitated collaboration between health systems in order to address gaps in COVID-19 data sharing and communication. They were able to develop the technical infrastructure to aggregate and share EHR data for real-time public health needs. Over time, the prioritization of data sharing for developing broader community health indicators became possible.
  • MNEHRC’s mission is to improve health by informing policy and practice through data-driven collaboration among members of Minnesota’s health care community. Dashboards are publicly available at www.mnehrconsortium.org.
  • Dr. Winkelman described how they built out a common data model at each of the MNEHRC health systems using Observational Medical Outcomes Partnership (OMOP), a common language for EHR data. OMOP was chosen because it’s open-source; it has a robust international online community; and some sites in the state had experience with OMOP, which helped with capacity building.
  • MNEHRC and Hennepin County’s Center for Community Health partnered to build Health Trends Across Communities (HTAC-MN), a unique data collaboration of health systems and public health agencies. They seek to develop comprehensive community health data infrastructure in Minnesota, ultimately strengthening community capacity to build healthy communities and promoting health equity.
  • Next steps for HTAC include developing and implementing processes to identify and prioritize new conditions; evaluating HTAC; and developing a plan for long-term sustainability.

Discussion Themes

Developing a central data model facilitated the collaboration.

Onboarding Federally Qualified Health Centers (FQHCs) to the consortium takes longer because of their internal capacity restraints. The team has had to be creative with figuring out how to onboard them; they are adding FQHCs in Hennepin County through EPIC affiliate agreements with Hennepin Healthcare and other sites through Minnesota’s quality measurement agency.

This is a new tool with a lot of potential, especially for the field of public health; researchers could use it to measure the impact of large-scale public health interventions. The HTAC team hopes that they’ll be able to further define the value that the data source can offer over the next few years.

January 31, 2023: New Article Suggests Moving From Idealism to Realism With Data Sharing

Posted on by Karen Staman
Headshots of Keith Marsolo, Kevin Weinfurt, Karen Staman, and Brad Hammill
From left to right: Keith Marsolo, Kevin Weinfurt, Karen Staman, and Bradley Hammill

In an article published today in the Annals of Internal Medicine, leaders of the NIH Pragmatic Trials Collaboratory suggest that data sharing is not rising to its potential, and that more guidance is needed to prevent data sharing from becoming a “box-checking exercise.”

“NIH should consider guidance to help investigators and NIH institutional centers understand what needs to be shared, for what reason, and at what cost.”

The authors suggest that for data to be useful for new generative science or for reproducibility, the NIH should provide guidance regarding 5 important questions:

  1. What data should be shared and for what purpose?
  2. What do we mean by reproducibility?
  3. What metadata should be shared?
  4. What is needed for generative science?
  5. What are the immediate and ongoing resources needed to support data sharing?

The authors state that the moral and scientific arguments for data sharing are clear: it promotes transparency, honors the participation of individuals, and enables new discoveries. They also state that more work is needed to make data sharing a meaningful exercise.

“We hope that ultimately, data sharing will be conducted not simply to meet a requirement by a funder or a journal, but towards a goal of generating meaningful, re-usable data that can help us better understand and protect public health.”

For more, read the full article, and see the Living Textbook chapter on Data Sharing and Embedded Research.

December 17, 2021: Cyberthreat, Cybersecurity and Cyber Compliance in Clinical Research and Healthcare: One Size Fits None (Eric Perakslis, PhD)

Posted on by terren.green@duke.edu

Speaker

Eric Perakslis, PhD
Chief Science & Digital Officer
Duke Clinical Research Institute
Professor
Department of Population Health Sciences
Chief Research Technology Strategist
Duke University School of Medicine

Topic

Cyberthreat, Cybersecurity and Cyber Compliance in Clinical Research and Healthcare: One Size Fits None

Keywords

Cybersecurity; Attack Surface; Cyber-Compliance; FISMA; InfoSec

Key Points

  • Over 40 million medical records are compromised each year.
  • Electronic Health Information is targeted due to its high value with respect to improper medical payments. Medicare estimates over $25 billion in improper payments each year.
  • The focus for cybersecurity should be on the most vulnerable groups. Women, BIPOC, and elderly populations experience cyberattack and identity theft more often than other populations.
  • Security objectives should focus on confidentiality, integrity, and availability.
  • The Cyber Risk Equation: Risk = Threat*Vulnerability*Impact*Likelihood
  • When starting a study, design with cybersecurity in mind, minimize attack surface, know your weakest link, add InfoSec expertise to the design team, and lean-in to innovation.

Discussion Themes

Researchers should take some responsibility for learning how to secure patient information. Training programs to make researchers more aware of cybersecurity concerns would increase researcher comfort in working with electronic health data.

A research network consisting on multiple sites may have differing needs and capacity to secure data. Treating each research site individually could allow greater representation in research, but those sites may be more vulnerable to cyberattack.

 

Read more about cybersecurity by Dr. Perakslis in A cybersecurity primer for translational research.

 

Tags

#pctGR, @Collaboratory1, @eperakslis

November 3, 2020: Disseminating Trial Results: We Can Have Faster and Better

Posted on by Karen Staman

Healthcare cover imageNIH Collaboratory investigators Drs. Greg Simon, Rachel Richesson, and Adrian Hernandez published an opinion piece in Healthcare arguing that clinical trials investigators should align their dissemination processes with industry-sponsored trials to favor speed, and that years-long delays in dissemination reduce the relevance of clinical research.

“Delays reduce the ability for researchers to apply trial findings to new research questions, impede clinicians from having the most up-to-date information, and perhaps most importantly, are a disservice to patients who could benefit from the information.”

The authors use experiences with pragmatic trials supported by the NIH Collaboratory to explore faster dissemination of results, and suggest the following solutions:

  • Real-time access to outcome data
  • Continuous data curation and cleaning
  • Immediate data analysis
  • Rapid reporting of trial results

Much change is needed to reach these goals. The authors suggest that by modeling processes after industry-sponsored trials, researchers may be able to improve the speed and quality of results reporting.

“Cultural incentives are aligned in industry sponsored trials to favor speed: readiness for generalizing topline results is considered valuable to shareholders, and the culture encourages a system where data are liquid, available, and continuously cleaned and curated, such that topline results can be reported within a timespan of two weeks rather than two years.”

As part of the NIH Collaboratory’s commitment to dissemination and sharing, all NIH Collaboratory Trials are expected to share data and resources, and topline results are reported in our weekly Grand Rounds Webinars.

 

May 18, 2020: Two New Sections in the Living Textbook Describe Incentives for Sharing Data Sets and Preparing for Data Sharing

Posted on by Karen Staman

Drs. Adrian Hernandez, Greg Simon, and Rich Platt of the NIH Collaboratory have authored two new sections of the Living Textbook as part of an ongoing commitment to sharing resources and data.

  • Incentive Structure and Citations for Data Sets
    • This section calls for a revision of the appointment, promotion, and tenure (APT) process to incorporate effective data sharing into decision-making and to recognize and credit creators of data sets that gain meaningful use by others.
  • Preparing for Data Sharing
    • This section describes how to prepare for data and resource sharing throughout the embedded PCT (ePCT) lifecycle, including during grant submission, trial registration, conduct, and dissemination.

All NIH Collaboratory Trials are expected to share data and resources, such as protocols, consent documents, public use datasets, computable phenotypes, and analytic code. During the onboarding process, NIH Collaboratory Trials are given a Data and Resource Sharing Informational Document and an Onboarding Data and Resource Sharing Questionnaire to assist clinical investigators in developing data sharing plans. At closeout, NIH Collaboratory Trials are provided a Closeout Data and Resource Sharing Checklist and are expected to utilize this checklist to provide a final data share package.

For more on data sharing, see the Living Textbook chapter, Data Sharing and Embedded Research.

March 16, 2020: New Video Interviews Highlight NIH Collaboratory Data and Resource Sharing

Posted on by Karen Staman

Recently, the NIH Collaboratory Coordinating Center conducted a video interview with Drs. Wendy Weber, Cathy Meyers, and Lesley Curtis to discuss the NIH Collaboratory Data Sharing Policy, including special requirements for the NIH Helping to End Addiction Long-term (HEAL) Initiative projects, and tips for sharing data and resources.

“For all the Demonstration Projects, primary data sets should be made available with the publication of the primary results paper. The Collaboratory policy also recognizes that there are additional considerations that must be incorporated into the policy because data sets are derived from the electronic health records of partnering healthcare systems, and additional precautions might be relevant to making these data sets available.” —Cathy Meyers, MD

The PRISM (Pragmatic and Implementation Studies for the Management of Pain to Reduce Opioid Prescribing) Demonstration Projects are part of the HEAL Initiative to address the national opioid crisis. The special requirements for these pragmatic trials include:

  • All publications must be open access so the public has immediate access to the results of the trials.
  • The underlying primary data must be shared through a repository, which will be set up for all of the projects funded through the HEAL Initiative.

To support sharing, the NIH Collaboratory Data and Resource Sharing Page on the Living Textbook holds links to datasets and data dictionaries, study tools, ethics and regulatory documentation, computable phenotypes and analytic code, data collection forms, study design papers, main outcomes papers, and other information from the Demonstration Projects that might be useful to others.

Demonstration Projects are given a Data and Resource Sharing Informational Document and an Onboarding Data and Resource Sharing Questionnaire during the onboarding process to assist clinical investigators in planning early for the sharing of these resources,. At closeout, Demonstration Projects are provided a Closeout Data and Resource Sharing Checklist, and investigators from the completed projects use this checklist to provide a final data sharing package.

January 15, 2020: NIH Collaboratory Announces Data and Resource Sharing Page

Posted on by Karen Staman

The NIH Collaboratory Coordinating Center has published a new Data and Resource Sharing Page on the Living Textbook, which is designed to share the data and resources generated by the NIH Collaboratory Trials.

“As part of the Collaboratory’s commitment to sharing, all NIH Collaboratory Trials are expected to share data and resources, such as protocols, consent documents, public use datasets, computable phenotypes, and analytic code.”  —Data and Resource Sharing Page

The page holds links to datasets and data dictionaries, study tools, ethics and regulatory documentation, computable phenotypes and analytic code, data collection forms, study design papers, main outcomes papers, and other information that might be useful to others.

To assist clinical investigators in planning for sharing these resources, NIH Collaboratory Trials are given a Data and Resource Sharing Informational Document and an Onboarding Data and Resource Sharing Questionnaire during the onboarding process.

These documents contain:

  • Questions designed to help investigators think through the unique concerns when sharing data and resources from embedded pragmatic trials
  • Data sharing requirements for the NIH Collaboratory, NIH, and medical journals
  • Examples from NIH Collaboratory Trials.
  • Descriptions of data sharing mechanisms, such as archives and enclaves
  • Examples of data sharing platforms
  • Examples of data sharing statements

At closeout, NIH Collaboratory Trials are provided a Closeout Data and Resource Sharing Checklist, and investigators from the completed projects use this checklist to provide a final data share package.

For more on data sharing, see the Living Textbook Chapter, Data Sharing and Embedded Research.

January 9, 2020: NIH Collaboratory Investigators Respond to Draft NIH Policy on Data Sharing

Posted on by Karen Staman

NIH Collaboratory leadership and NIH Collaboratory Trial principal investigators, along with their colleagues, responded this week to the recently released Draft NIH Policy for Data Management and Sharing and supplemental draft guidance. The draft policy proposes that applicants for research funding submit a plan describing how scientific data will be managed and shared.

“We applaud the NIH’s policy and commitment to making the results and outputs of the research it funds and conducts available to the public. We enthusiastically support data sharing and agree with the principles of this policy. However, we believe more detail is warranted about the different types of research (ie, embedded pragmatic research), the associated protections, and acceptable mechanisms for sharing data, such as public and private archives and enclaves.” —Response to Draft NIH Policy for Data Management and Sharing

The main topics covered in the response are:

  • Support for the goals of the draft data sharing policy
  • Assessing and mitigating re-identification risk
  • Protecting secondary subjects
  • Use of data enclaves
  • Crediting those who share data

Other signatories include participants in the National Academy of Medicine’s Clinical Effectiveness Research Innovation Collaborative of the Leadership Consortium for Value and Science-Driven Health Care, and leaders of the Health Care Systems Research Network.

The full letter is available for download and includes the list of signatories.

Comments are due no later than January 10, 2020, and may be submitted online.

For more on data sharing, see the Living Textbook chapter, Data Sharing and Embedded Research.

November 22, 2019: NIH Releases Draft Policy for Data Management and Sharing

Posted on by Karen Staman

The NIH recently released a Draft NIH Policy for Data Management and Sharing and supplemental draft guidance for public comment.

In the draft, the NIH reiterates its commitment to making available the results and products of the research it funds, and acknowledges that data sets come from a variety of sources that may have unique data sharing concerns. Therefore, the draft policy proposes that applicants for research funding submit a plan describing how scientific data will be managed and shared.

 “Under this Policy, individuals and entities would be required to provide a Data Management and Sharing Plan (Plan) describing how scientific data will be managed, including when and where the scientific data will be preserved and shared, prior to initiating the research study.” —Draft NIH Policy for Data Management and Sharing

The elements of the Plan are described in detail in the Draft Policy and will require a description of data type and quantity, a rationale for decisions about data sharing, metadata and associated documentation, and plans for protecting confidentiality.

Comments are due no later than January 10, 2020. Comments may be submitted online.

For information on the NIH Collaboratory Data Sharing Policy, see the Data and Resource Sharing informational document, questionnaire, and checklist.

Podcast October 1, 2019: Preparing for Clinical Trial Data Sharing and Re-use: The New Reality for Researchers (Rebecca Li, PhD, Frank Rockhold, PhD)

Posted on by Gina Uhlenbrauck

In this episode of the NIH Collaboratory Grand Rounds podcast, Dr. Adrian Hernandez sits down with Drs. Rebecca Li and Frank Rockhold to discuss clinical trial data sharing and re-use. In the discussion, Li and Rockhold highlight benefits, potential fears, and the future of data sharing and open science as well as Vivli, a global clinical research data sharing platform.

Click on the recording below to listen to the podcast.

Want to hear more? View the full Grand Rounds presentation.

For alerts about new episodes, subscribe free on Apple Podcasts or SoundCloud.

Read the transcript.

Rate this podcast