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Healthcare system resilience and adaptability to pandemic disruptions in the United States.
Zhong, Lu; Lopez, Dimitri; Pei, Sen; Gao, Jianxi.
Afiliação
  • Zhong L; Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, USA.
  • Lopez D; Network Science and Technology Center, Rensselaer Polytechnic Institute, Troy, NY, USA.
  • Pei S; Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, USA.
  • Gao J; Department of Environmental Health Sciences, Columbia University, New York City, NY, USA.
Nat Med ; 2024 Jul 02.
Article em En | MEDLINE | ID: mdl-38956198
ABSTRACT
Understanding healthcare system resilience has become paramount, particularly in the wake of the COVID-19 pandemic, which imposed unprecedented burdens on healthcare services and severely impacted public health. Resilience is defined as the system's ability to absorb, recover from and adapt to disruptions; however, despite extensive studies on this subject, we still lack empirical evidence and mathematical tools to quantify its adaptability (the ability of the system to adjust to and learn from disruptions). By analyzing millions of patients' electronic medical records across US states, we find that the COVID-19 pandemic caused two successive waves of disruptions within the healthcare systems, enabling natural experiment analysis of the adaptive capacity of each system to adapt to past disruptions. We generalized the quantification framework and found that the US healthcare systems exhibit substantial adaptability (ρ = 0.58) but only a moderate level of resilience (r = 0.70). When considering system responses across racial groups, Black and Hispanic groups were more severely impacted by pandemic disruptions than white and Asian groups. Physician abundance was the key characteristic for determining healthcare system resilience. Our results offer vital guidance in designing resilient and sustainable healthcare systems to prepare for future waves of disruptions akin to COVID-19 pandemics.

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article