The Power of Modeling in Emergency Preparedness for COVID-19: A Moonshot Moment for Hospitals

Safavi, Kyan; Prestipino, Ann L; Langle, Ana Cecilia Zenteno; Copenhaver, Martin S.; Hu, Michael S.; Daily, Bethany; Koehler, Allison; Biddinger, Paul D.; Dunn, Peter F.

doi:10.1017/dmp.2021.51

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…It is not surprising that PTE status was associated with differences in geography and surge timing; it is well documented that the pandemic affected the Northeast (including New York City) early and severely. 36,37 This aligns with our findings that fewer programs in the Northeast (which had early surges, with high volumes of patients with COVID-19 and less time for preparedness planning 38 ) planned to exclude residents from COVID-19 care. Conversely, a higher percentage of programs in the South had plans to exclude residents.…”

Section: Discussionsupporting

confidence: 82%

Residency Program Responses to Early COVID-19 Surges Highlight Tension as to Whether Residents Are Learners or Essential Workers

Martin¹,

Finn²,

Kisielewski³

et al. 2022

Academic Medicine

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Purpose To quantify the extent to which internal medicine (IM) residents provided care for patients with COVID-19 and examine characteristics of residency programs with or without plans (at some point) to exclude residents from COVID-19 care during the first 6 months of the pandemic. Method The authors used data from a nationally representative, annually recurring survey of U.S. IM program directors (PDs) to quantify early (March–August 2020) resident participation in COVID-19 care. The survey was fielded from August to December 2020. PDs reported whether they had planned to exclude residents from COVID-19 care (i.e., PTE status). PTE status was tested for association with program and COVID-19 temporal characteristics, resident schedule accommodations, and resident COVID-19 cases. Results The response rate was 61.5% (264/429). Nearly half of PDs (45.4%, 118/260) reported their program had planned at some point to exclude residents from COVID-19 care. Northeastern U.S. programs represented a smaller percentage of PTE than non-PTE programs (26.3% vs 36.6%; P = .050). PTE programs represented a higher percentage of programs with later surges than non-PTE programs (33.0% vs 13.6%, P = .048). Median percentage of residents involved in COVID-19 care was 75.0 (interquartile range [IQR]: 22.5–100.0) for PTE programs, compared with 95.0 (IQR: 60.0–100.0) for non-PTE programs (P < .001). Residents participated most in intensive care units (87.6%, 227/259) and inpatient wards (80.8%, 210/260). Accommodations did not differ by PTE status. PTE programs reported fewer resident COVID-19 cases than non-PTE programs (median percentage = 2.7 [IQR: 0.0–8.6] vs 5.1 [IQR: 1.6–10.7]; P = .011). Conclusions IM programs varied widely in their reported plans to exclude residents from COVID-19 care during the early pandemic. A high percentage of residents provided COVID-19 care, even in PTE programs. Thus, the pandemic highlighted the tension as to whether residents are learners or employees.

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Section: Discussionsupporting

confidence: 82%

Residency Program Responses to Early COVID-19 Surges Highlight Tension as to Whether Residents Are Learners or Essential Workers

Martin¹,

Finn²,

Kisielewski³

et al. 2022

Academic Medicine

View full text Add to dashboard Cite

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“…The latter is an important operational consideration for managers. Lead time for opening ICU surge capacity can be as short as 3 days [35]; however, the additional lead-time can allow hospitals to properly identify spaces, design staffing plans, re-deploy equipment, and transition out patients that usually use those spaces [36]. This is especially the case in Academic Medical Centers which have very high ICU occupancy levels.…”

Section: Introductionmentioning

confidence: 99%

Forecasting hospital-level COVID-19 admissions using real-time mobility data

Klein

Zenteno

Joseph

et al. 2022

Preprint

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For each of the COVID-19 pandemic waves, hospitals have had to plan for deploying surge capacity and resources to manage large but transient increases in COVID-19 admissions. While a lot of effort has gone into predicting regional trends in COVID-19 cases and hospitalizations, there are far fewer successful tools for creating accurate hospital-level forecasts. At the same time, anonymized phone-collected mobility data proved to correlate well with the number of cases for the first two waves of the pandemic (spring 2020, and fall-winter 2021). In this work, we show how mobility data could bolster hospital-specific COVID-19 admission forecasts for five hospitals in Massachusetts during the initial COVID-19 surge. The high predictive capability of the model was achieved by combining anonymized, aggregated mobile device data about users’ contact patterns, commuting volume, and mobility range with COVID hospitalizations and test-positivity data. We conclude that mobility-informed forecasting models can increase the lead-time of accurate predictions for individual hospitals, giving managers valuable time to strategize how best to allocate resources to manage forthcoming surges.

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Forecasting hospital-level COVID-19 admissions using real-time mobility data

et al. 2023

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Background For each of the COVID-19 pandemic waves, hospitals have had to plan for deploying surge capacity and resources to manage large but transient increases in COVID-19 admissions. While a lot of effort has gone into predicting regional trends in COVID-19 cases and hospitalizations, there are far fewer successful tools for creating accurate hospital-level forecasts. Methods Large-scale, anonymized mobile phone data has been shown to correlate with regional case counts during the first two waves of the pandemic (spring 2020, and fall/winter 2021). Building off this success, we developed a multi-step, recursive forecasting model to predict individual hospital admissions; this model incorporates the following data: (i) hospital-level COVID-19 admissions, (ii) statewide test positivity data, and (iii) aggregate measures of large-scale human mobility, contact patterns, and commuting volume. Results Incorporating large-scale, aggregate mobility data as exogenous variables in prediction models allows us to make hospital-specific COVID-19 admission forecasts 21 days ahead. We show this through highly accurate predictions of hospital admissions for five hospitals in Massachusetts during the first year of the COVID-19 pandemic. Conclusions The high predictive capability of the model was achieved by combining anonymized, aggregated mobile device data about users’ contact patterns, commuting volume, and mobility range with COVID hospitalizations and test-positivity data. Mobility-informed forecasting models can increase the lead-time of accurate predictions for individual hospitals, giving managers valuable time to strategize how best to allocate resources to manage forthcoming surges.

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The Power of Modeling in Emergency Preparedness for COVID-19: A Moonshot Moment for Hospitals

Cited by 3 publications

References 7 publications

Residency Program Responses to Early COVID-19 Surges Highlight Tension as to Whether Residents Are Learners or Essential Workers

Residency Program Responses to Early COVID-19 Surges Highlight Tension as to Whether Residents Are Learners or Essential Workers

Forecasting hospital-level COVID-19 admissions using real-time mobility data

Forecasting hospital-level COVID-19 admissions using real-time mobility data

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