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Healthcare system resilience and adaptability to pandemic disruptions in the United States

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.

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Fig. 1: Adaptive responses to successive disruptions in healthcare systems.
Fig. 2: Adaptability and resilience assessment of US healthcare systems.
Fig. 3: Adaptability and resilience assessment for essential services.
Fig. 4: Adaptability and resilience assessment for patient groups by race and ethnicity.

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Data availability

The EMR dataset that supports the findings of this study is available from the Healthjump database, provided by the COVID-19 Research Database Consortium (https://covid19researchdatabase.org/). However, restrictions apply to accessing these data, which were used under license for the current study. The EMR dataset is not publicly available. The data on COVID-19 infection cases in each state are collected from the Johns Hopkins Coronavirus Resource Center (https://github.com/CSSEGISandData/COVID-19). The general physician abundance data regarding physician numbers in each state are collected from the 2019 State Physician Workforce Data (https://www.aamc.org/data-reports/workforce/report/state-physician-workforce-data-report). The sociodemographic factors in each state are collected from the CDC/ATSDR SVI (https://www.atsdr.cdc.gov/placeandhealth/svi/index.html). For validation, external summary datasets on patient visits to physicians, emergency departments and the number of hospital discharges during the pandemic are sourced from the National Center for Health Statistics (https://www.cdc.gov/nchs/index.htm) and the US Census Bureau (https://www.census.gov/). For the results dashboard, please see the website ResilienceHealthSys.com.

Code availability

The code used in the study for the quantification framework is available at https://github.com/lucinezhong/healthcare_resilience_quantification_framework.

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Acknowledgements

The data, technology and services used in generating these research findings were generously supplied pro bono by the COVID-19 Research Database partners, who are acknowledged at https://covid19researchdatabase.org/. This work was supported, in whole or in part, by the Bill & Melinda Gates Foundation (No. CORONAVIRUSHUB-D-21-00120). Under the grant conditions of the Foundation, a Creative Commons Attribution 4.0 Generic License has already been assigned to the Author Accepted Manuscript version that might arise from this submission. We also acknowledge the support of Research Accelerator grants funded by the USA National Science Foundation (2047488), the Rensselaer-IBM AI Research Collaboration, the USA National Science Foundation (DMS-2229605) and Centers for Disease Control and Prevention (U01CK000592).

Author information

Authors and Affiliations

Authors

Contributions

L.Z., D.L. and J.G. conceived the project and designed the study. D.L. and L.Z. performed the data analyses. L.Z. and D.L. wrote the first draft of the manuscript. L.Z., S.P. and J.G. contributed to interpreting the results and improving the manuscript. J.G. was the lead writer of the manuscript.

Corresponding author

Correspondence to Jianxi Gao.

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The authors declare no competing interests.

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Nature Medicine thanks Catherine Arsenault and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Ming Yang, in collaboration with the Nature Medicine team.

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Extended data

Extended Data Fig. 1 Comparison of predictive models P(t) using national volume of Non-COVID-19 patient visits in Healthjump dataset.

Comparison of predictive models P(t) using national volume of Non-COVID-19 patient visits of Healthjump dataset. (a) Estimation model incorporating physician EMR technology adoption counts within Healthjump database. (b) Generalized logistic model utilizing AHA (American Hospital Association) EMR adoption rates. (c) Simple exponential smoothing model integrated with AHA EMR adoption rates. (d) Seasonal exponential smoothing model incorporating AHA EMR adoption rates. (e) Constant population who have active visits during the pre-pandemic period. All models identify two disruptions.

Extended Data Fig. 2 Volume of non-COVID-19 patient care in the US from 2019 to 2022 provided by other datasets.

(a) Patient visits to physicians, sourced from the National Center for Health Statistics. (b) Patient visits to emergency departments, also sourced from the National Center for Health Statistics. (c) Number of hospital discharges, provided by the US Census Bureau. Compared against a baseline represented by a blue line, indicative of average patient visits before the pandemic, the observed patient visits (represented by a red line) show two disruptions, marked by dashed vertical lines. The first disruption spans from the first quarter of 2020 to the second/third quarter of 2021, while the second disruption begins in the third/fourth quarter of 2021.

Extended Data Fig. 3 Resilience index of states versus social vulnerability index.

(a) State resilience index. (b) State social vulnerability index. (c) Pearson correlation coefficient. The resilience index is negatively correlated with the social vulnerability index. The correlation significance is determined by a two-sided test and indicated by a P value.

Extended Data Fig. 4 Temporal trend of patient visits for dialysis service.

Similar to conclusions drawn for other essential services, two disruptions are observed, but the period differs, with the first occurring from December 2019 to March 2022 and the second beginning in April 2022 with no observed recovery.

Extended Data Table 1 Successive disruptions on healthcare system from 2020 to 2022

Supplementary information

Supplementary Information

Supplementary Notes 1–4, Supplementary Figs. 1–8 and Supplementary Tables 1–4.

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Zhong, L., Lopez, D., Pei, S. et al. Healthcare system resilience and adaptability to pandemic disruptions in the United States. Nat Med (2024). https://doi.org/10.1038/s41591-024-03103-6

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