Models for Assessing Strategies for Improving Hospital Capacity for Handling Patients during a Pandemic

Objective: The aim of this study was to investigate the performance of key hospital units associated with emergency care of both routine emergency and pandemic (COVID-19) patients under capacity enhancing strategies. Methods: This investigation was conducted using whole-hospital, resource-constrained, patient-based, stochastic, discrete-event simulation models of a generic 200-bed urban U.S. tertiary hospital serving routine emergency and COVID-19 patients. Systematically designed numerical experiments were conducted to provide generalizable insights into how hospital functionality may be affected by the care of COVID-19 pandemic patients along specially designated care paths under changing pandemic situations from getting ready to turning all of its resources to pandemic care. Results: Several insights are presented. For example, each day of reduction in average ICU length of stay increases intensive care unit patient throughput by up to 24% for high COVID-19 daily patient arrival levels. The potential of five specific interventions and two critical shifts in care strategies to significantly increase hospital capacity is described. Conclusions: These estimates enable hospitals to repurpose space, modify operations, implement crisis standards of care, prepare to collaborate with other health care facilities, or request external support, increasing the likelihood that arriving patients will find an open staffed bed when one is needed.

Protecting Public Health and Preserving the Financial Viability of North Carolina’s Critical Health Care Facilities during Infectious Disease Outbreaks

While hospitals’ primary emphasis during the COVID-19 pandemic has been on ensuring sufficient health-related resource capacity (e.g., ICU beds, ventilators) to serve admitted patients, the impacts of the pandemic on the financial viability of hospitals has also become a critical concern. Data from the period March 2020-Janaury 2021 suggest that the halt to elective and non-emergency inpatient procedures, combined with a reduction in emergency room procedures, led to losses equal to 6.5% of revenue from inpatient procedures, or about $825 million. This study finds that societal measures to reduce the community transmission rates have a larger impact on available healthcare capacity and hospital financial losses than hospital-level decisions. This study illustrates the tradeoffs between hospital capacity, quality of care, and financial risk faced by health care facilities throughout the U.S. as a result of COVID-19, providing potential insights for many hospitals seeking to navigate these uncertain scenarios through adaptive decision-making.

Simple decision rules to predict local surges in COVID-19 hospitalizations during the winter and spring of 2022

Low rates of vaccination, emergence of novel variants of SARS-CoV-2, and increasing transmission relating to seasonal changes leave many U.S. communities at risk for surges of COVID-19 during the winter and spring of 2022 that might strain hospital capacity, as in previous waves. The trajectories of COVID-19 hospitalizations during this period are expected to differ across communities depending on their age distributions, vaccination coverage, cumulative incidence, and adoption of risk mitigating behaviors. Yet, existing predictive models of COVID-19 hospitalizations are almost exclusively focused on national- and state-level predictions. This leaves local policymakers in urgent need of tools that can provide early warnings about the possibility that COVID-19 hospitalizations may rise to levels that exceed local capacity. In this work, we develop simple decision rules to predict whether COVID-19 hospitalization will exceed the local hospitalization capacity within a 4- or 8-week period if no additional mitigating strategies are implemented during this time. These decision rules use real-time data related to hospital occupancy and new hospitalizations associated with COVID-19, and when available, genomic surveillance of SARS-CoV-2. We showed that these decision rules present reasonable accuracy, sensitivity, and specificity (all ≥80%) in predicting local surges in hospitalizations under numerous simulated scenarios, which capture substantial uncertainties over the future trajectories of COVID-19 during the winter and spring of 2022. Our proposed decision rules are simple, visual, and straightforward to use in practice by local decision makers without the need to perform numerical computations.

Effect of disaster preparedness training towards knowledge of COVID-19 among rural society in Banjararum Village, Kulon Progo Regency, Yogyakarta

The number of COVID-19 daily cases in Indonesia reached a record high in 2021, and the prevalence of active cases increased beyond hospital capacity. Disaster preparedness training involving the key role of society is substantial to stop the spread of COVID-19. This study aimed to determine the effect of disaster preparedness training towards the knowledge of COVID-19 pandemic among rural society. The intervention involved 29 participants, who were located in a rural area of the Special Region of Yogyakarta Province. Participants were required to complete the pre-test and post-test to determine the effect of disaster preparedness training on participant knowledge. There was a significant effect of disaster preparedness training on public knowledge about COVID-19 (P <0.005). There was an increase in the mean score between pre-test (9.93) and post-test (11.68). By increasing society’s knowledge, the society can play a maximum role in COVID-19 prevention and control measures.

Modeling COVID-19 care capacity in a major health system

Hospital resources, especially critical care beds and ventilators, have been strained by additional demand throughout the COVID-19 pandemic. Rationing of scarce critical care resources may occur when available resource limits are exceeded. However, the dynamic nature of the COVID-19 pandemic and variability in projections of the future burden of COVID-19 infection pose challenges for optimizing resource allocation to critical care units in hospitals. Connecticut experienced a spike in the number of COVID-19 cases between March and June 2020. Uncertainty about future incidence made it difficult to predict the magnitude and duration of the increased COVID-19 burden on the healthcare system. In this paper, we describe a model of COVID-19 hospital capacity and occupancy that generates estimates of the resources necessary to accommodate COVID-19 patients under infection scenarios of varying severity. We present the model structure and dynamics, procedure for parameter estimation, and publicly available web application where we implemented the tool. We then describe calibration using data from over 3,000 COVID-19 patients seen at the Yale-New Haven Health System between March and July 2020. We conclude with recommendations for modeling tools to inform decision-making using incomplete information during future crises.