The Yale Department of Medicine COVID-19 Data Explorer and Repository (DOM-CovX): An Innovative Approach to Promoting Collaborative Scholarship During a Pandemic

  1. Tanima Arora
  2. Michael Simonov
  3. Jameel Alausa
  4. Labeebah Subair
  5. Brett Gerber
  6. Andrew Nguyen
  7. Allen Hsiao
  8. Richard Hintz
  9. Yu Yamamoto
  10. Robert Soufer
  11. Gary Desir
  12. Francis Perry Wilson
  13. Merceditas Villanueva

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Abstract

Background

The COVID-19 pandemic has led to an explosion of research publications spanning epidemiology, basic and clinical science. While a digital revolution has allowed for open access to large datasets enabling real-time tracking of the epidemic, detailed, locally-specific clinical data has been less readily accessible to a broad range of academic faculty and their trainees. This perpetuates the separation of the primary missions of clinically-focused and primary research faculty resulting in lost opportunities for improved understanding of the local epidemic; expansion of the scope of scholarship; limitation of the diversity of the research pool; lack of creation of initiatives for growth and dissemination of research skills needed for the training of the next generation of clinicians and faculty.

Objectives

Create a common, easily accessible and up-to-date database that would promote access to local COVID-19 clinical data, thereby increasing efficiency, streamlining and democratizing the research enterprise. By providing a robust dataset, a broad range of researchers (faculty, trainees) and clinicians are encouraged to explore and collaborate on novel clinically relevant research questions.

Methods

We constructed a research platform called the Yale Department of Medicine COVID-19 Explorer and Repository (DOM-CovX), to house cleaned, highly granular, de-identified, continually-updated data from over 7,000 patients hospitalized with COVID-19 (1/2020-present) across the Yale New Haven Health System. This included a front-end user interface for simple data visualization of aggregate data and more detailed clinical datasets for researchers after a review board process. The goal is to promote access to local COVID-19 clinical data, thereby increasing efficiency, streamlining and democratizing the research enterprise.

Expected Outcomes

  • Accelerate generation of new knowledge and increase scholarly productivity with particular local relevance

  • Improve the institutional academic climate by:

  • Broadening research scope

  • Expanding research capability to more diverse group of stakeholders including clinical and research-based faculty and trainees

  • Enhancing interdepartmental collaborations

    • Conclusions

    The DOM-CovX Data Explorer and Repository have great potential to increase academic productivity. By providing an accessible tool for simple data analysis and access to a consistently updated, standardized and large-scale dataset, it overcomes barriers for a wide variety of researchers. Beyond academic productivity, this innovative approach represents an opportunity to improve the institutional climate by fostering collaboration, diversity of scholarly pursuits and expanding medical education. It provides a novel approach that can be expanded to other diseases beyond COVID 19.

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    1. ScreenIT

      SciScore for 10.1101/2021.08.23.21262241: (What is this?)

      Please note, not all rigor criteria are appropriate for all manuscripts.

      Table 1: Rigor

      EthicsIRB: A pre-approved HIC number and an Institutional Review Board (IRB) Protocol is required to be uploaded at the time of data request.
      Sex as a biological variablenot detected.
      Randomizationnot detected.
      Blindingnot detected.
      Power Analysisnot detected.

      Table 2: Resources

      Software and Algorithms
      SentencesResources
      This could include importation to other data collection and analytical systems such as REDCap, a browser based, metadata-driven software to collect and store data for clinical and translational research7,8.
      REDCap
      suggested: (REDCap, RRID:SCR_003445)

      Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

      Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

      Results from TrialIdentifier: No clinical trial numbers were referenced.

      Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

      Results from JetFighter: We did not find any issues relating to colormaps.

      Results from rtransparent:
      • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
      • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
      • No protocol registration statement was detected.

      Results from scite Reference Check: We found no unreliable references.

      About SciScore

      SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

    2. Version published to 10.1101/2021.08.23.21262241v1 on medRxiv