Projecting hospital resource utilization during a surge using parametric bootstrapping

  1. Jeffrey N. Chiang
  2. Ulzee An
  3. Misagh Kordi
  4. Brandon Jew
  5. Clifford Kravit
  6. William J. Dunne
  7. Ronald Perez
  8. Neil R. Parikh
  9. Drew Weil
  10. Richard F. Azar
  11. Robert Cherry
  12. Karen A. Grimley
  13. Samuel A. Skootsky
  14. Christopher E. Saigal
  15. Vladimir Manuel
  16. Eleazar Eskin
  17. Eran Halperin

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Abstract

During the initial wave of the COVID-19 pandemic in the United States, hospitals took drastic action to ensure sufficient capacity, including canceling or postponing elective procedures, expanding the number of available intensive care beds and ventilators, and creating regional overflow hospital capacity. However, in most locations the actual number of patients did not reach the projected surge leaving available, unused hospital capacity. As a result, patients may have delayed needed care and hospitals lost substantial revenue.

These initial recommendations were made based on observations and worst-case epidemiological projections, which generally assume a fixed proportion of COVID-19 patients will require hospitalization and advanced resources. This assumption has led to an overestimate of resource demand as clinical protocols improve and testing becomes more widely available throughout the course of the pandemic.

Here, we present a parametric bootstrap model for forecasting the resource demands of incoming patients in the near term, and apply it to the current pandemic. We validate our approach using observed cases at UCLA Health and simulate the effect of elective procedure cancellation against worst-case pandemic scenarios. Using our approach, we show that it is unnecessary to cancel elective procedures unless the actual capacity of COVID-19 patients approaches the hospital maximum capacity. Instead, we propose a strategy of balancing the resource demands of elective procedures against projected patients by revisiting the projections regularly to maintain operating efficiency. This strategy has been in place at UCLA Health since mid-April.

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    SciScore for 10.1101/2020.07.30.20164475: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  2. Version published to 10.1101/2020.07.30.20164475v1 on medRxiv