Promoting Collaborative Scholarship During the COVID-19 Pandemic Through an Innovative COVID-19 Data Explorer and Repository at Yale School of Medicine: Development and Usability Study

Abstract

Background: The COVID-19 pandemic sparked a surge of research publications spanning epidemiology, basic science, and clinical science. Thanks to the digital revolution, large data sets are now accessible, which also enables real-time epidemic tracking. However, despite this, academic faculty and their trainees have been struggling to access comprehensive clinical data. To tackle this issue, we have devised a clinical data repository that streamlines research processes and promotes interdisciplinary collaboration.

Objective: This study aimed to present an easily accessible up-to-date database that promotes access to local COVID-19 clinical data, thereby increasing efficiency, streamlining, and democratizing the research enterprise. By providing a robust database, a broad range of researchers (faculty and trainees) and clinicians from different areas of medicine are encouraged to explore and collaborate on novel clinically relevant research questions.

Methods: A research platform, called the Yale Department of Medicine COVID-19 Explorer and Repository (DOM-CovX), was constructed to house cleaned, highly granular, deidentified, and continually updated data from over 18,000 patients hospitalized with COVID-19 from January 2020 to January 2023, across the Yale New Haven Health System. Data across several key domains were extracted including demographics, past medical history, laboratory values during hospitalization, vital signs, medications, imaging, procedures, and outcomes. Given the time-varying nature of several data domains, summary statistics were constructed to limit the computational size of the database and provide a reasonable data file that the broader research community could use for basic statistical analyses. The initiative also included a front-end user interface, the DOM-CovX Explorer, for simple data visualization of aggregate data. The detailed clinical data sets were made available for researchers after a review board process.

Results: As of January 2023, the DOM-CovX Explorer has received 38 requests from different groups of scientists at Yale and the repository has expanded research capability to a diverse group of stakeholders including clinical and research-based faculty and trainees within 15 different surgical and nonsurgical specialties. A dedicated DOM-CovX team guides access and use of the database, which has enhanced interdepartmental collaborations, resulting in the publication of 16 peer-reviewed papers, 2 projects available in preprint servers, and 8 presentations in scientific conferences. Currently, the DOM-CovX Explorer continues to expand and improve its interface. The repository includes up to 3997 variables across 7 different clinical domains, with continued growth in response to researchers' requests and data availability.

Conclusions: The DOM-CovX Data Explorer and Repository is a user-friendly tool for analyzing data and accessing a consistently updated, standardized, and large-scale database. Its innovative approach fosters collaboration, diversity of scholarly pursuits, and expands medical education. In addition, it can be applied to other diseases beyond COVID-19.

Keywords: COVID-19; clinical data; collaborative scholarship; data access; database; innovation; interdepartmental communication; large-scale database; repository; researchers.

Figure 1

Flowchart of the DOM-CovX data request process. DOM-CovX: Department of Medicine COVID-19 Explorer and Repository; IRB: Institutional Review Board.

Figure 2

Web interface of the DOM-CovX Explorer—sample queries extracted in October 2022, showing different functions of Explorer to find correlations between variables and aid in hypothesis generation. (A) Histogram showing frequency of patients with variable “alkaline phosphatase” available in the data set. (B) Scatter plot showing correlation between the independent variable “median creatinine” (plotted in x-axis) and the dependent variable “maximum potassium” (plotted in y-axis). (C) Box Plot displaying the distribution of the variable “median IL-6” in patients who died (1) and did not die (0). (D) 2x2 Table and bar graph showing the frequency and proportion of patients by sex (female=1, male=0) that died (1) and did not die (0). DOM-CovX: Department of Medicine COVID-19 Explorer and Repository.