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Harnessing Multiteam Systems in the Development of a Multifaceted Data Repository for Emergency Public Health

Brandy L Farlow1, Keriayn N Smith, Aubrie Weyhmiller

  • 1Author Affiliations: Renaissance Computing Institute (RENCI), University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (Farlow, Smith, Weyhmiller, Hubal, Suber, and Krishnamurthy); Booz Allen Hamilton Inc., McLean, Virginia (Keller); and Stanford Center for Biomedical Informatics Research, Stanford University, Palo Alto, California (Musen).

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The NIH RADx Data Hub enabled secure COVID-19 data sharing through collaboration. Structured team science methods accelerated data access, improving public health crisis response.

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Area of Science:

  • Public Health
  • Data Science
  • Health Informatics

Background:

  • The National Institutes of Health (NIH) established the Rapid Acceleration of Diagnostics (RADx) Initiative to improve COVID-19 testing.
  • A centralized data repository, the RADx Data Hub, was created for secure data access and analysis across RADx programs.
  • Effective implementation necessitated significant collaboration among multiple teams.

Purpose of the Study:

  • To describe the nested multiteam system framework used to build the RADx Data Hub.
  • To present the Data Hub as a model for future public health data management during crises.
  • To highlight the role of structured team science in data sharing and crisis response.

Main Methods:

  • Developing a centralized, secure data repository (Data Hub).
  • Implementing a nested multiteam system approach for collaboration and alignment.
  • Curating and de-identifying data from various RADx Initiative programs.

Main Results:

  • The Data Hub successfully enabled secure access and secondary analysis of de-identified data.
  • The nested multiteam system framework facilitated data assembly and distribution.
  • Structured team science methods proved effective in accelerating data sharing.

Conclusions:

  • The RADx Data Hub implementation demonstrates a successful model for managing data during public health emergencies.
  • Structured team science is crucial for enhancing the speed and effectiveness of data sharing.
  • This framework can guide future efforts to accelerate responses to global health challenges.