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VA Big Data Science: A Model for Improved National Pandemic Response Present and Future.
Young-Xu, Yinong; Davey, Victoria; Marconi, Vincent C; Cunningham, Francesca E.
Afiliação
  • Young-Xu Y; White River Junction Veterans Affairs Medical Center, Vermont.
  • Davey V; Geisel School of Medicine at Dartmouth, Hanover, New Hampshire.
  • Marconi VC; Office of Research and Development, Department of Veterans Affairs, Washington, DC.
  • Cunningham FE; Atlanta Veterans Affairs Medical Center, Decatur, Georgia.
Fed Pract ; 40(11 Suppl 5): S39-S42, 2023 Nov.
Article em En | MEDLINE | ID: mdl-38577309
ABSTRACT

Background:

The US Department of Veterans Affairs (VA) enterprise approach to research (VA Research) has built a data-sharing framework available to all research teams within VA. Combined with robust analytic systems and tools available for investigators, VA Research has produced actionable results during the COVID-19 pandemic. Big data science techniques applied to VA's health care data demonstrate that medical research can be performed quickly and judiciously during nationwide health care emergencies. Observations We envision a common framework of data collection, management, and surveillance implemented in partnership with other health care agencies that would capture even broader, actionable, and timely observational data on populations, while providing opportunities for enhanced collaborative research across agencies. This model should be continued and expanded through the current COVID-19 and future pandemics.

Conclusions:

Extending the achievements of VA Research in the COVID-19 pandemic to date, we advocate national goals of open science by working toward a synergistic national framework of anonymized, synchronized, shared health data that would provide researchers with potent tools to combat future public health crises.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Fed Pract Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Fed Pract Ano de publicação: 2023 Tipo de documento: Article