scholarly journals Modelling intensive care unit capacity under different epidemiological scenarios of the COVID-19 pandemic in three Western European countries

2021 ◽  
Author(s):  
Ruth McCabe ◽  
Mara D Kont ◽  
Nora Schmit ◽  
Charles Whittaker ◽  
Alessandra Løchen ◽  
...  
Keyword(s):  
Intensive Care ◽  
Capacity Planning ◽  
Winter Season ◽  
Winter Period ◽  
Spare Capacity ◽  
Hospital Capacity ◽  
Unit Capacity ◽  
Patient Model ◽  
Hospital Capacity Planning ◽  
Bed Numbers

Abstract Background The coronavirus disease 2019 (COVID-19) pandemic has placed enormous strain on intensive care units (ICUs) in Europe. Ensuring access to care, irrespective of COVID-19 status, in winter 2020–2021 is essential. Methods An integrated model of hospital capacity planning and epidemiological projections of COVID-19 patients is used to estimate the demand for and resultant spare capacity of ICU beds, staff and ventilators under different epidemic scenarios in France, Germany and Italy across the 2020–2021 winter period. The effect of implementing lockdowns triggered by different numbers of COVID-19 patients in ICUs under varying levels of effectiveness is examined, using a ‘dual-demand’ (COVID-19 and non-COVID-19) patient model. Results Without sufficient mitigation, we estimate that COVID-19 ICU patient numbers will exceed those seen in the first peak, resulting in substantial capacity deficits, with beds being consistently found to be the most constrained resource. Reactive lockdowns could lead to large improvements in ICU capacity during the winter season, with pressure being most effectively alleviated when lockdown is triggered early and sustained under a higher level of suppression. The success of such interventions also depends on baseline bed numbers and average non-COVID-19 patient occupancy. Conclusion Reductions in capacity deficits under different scenarios must be weighed against the feasibility and drawbacks of further lockdowns. Careful, continuous decision-making by national policymakers will be required across the winter period 2020–2021.

Incorporating efficiency in hospital-capacity planning in Germany

2007 ◽  
Vol 8 (3) ◽  
pp. 213-223 ◽  
Author(s):  
Ludwig Kuntz ◽  
Stefan Scholtes ◽  
Antonio Vera
Keyword(s):  
Capacity Planning ◽  
Hospital Capacity ◽  
Hospital Capacity Planning

Building hospital capacity planning mechanisms in Poland: The impact of 2016/2017 regulatory changes

2018 ◽  
Vol 33 (2) ◽  
pp. e403-e415 ◽  
Author(s):  
Katarzyna Dubas‐Jakóbczyk ◽  
Christoph Sowada ◽  
Alicja Domagała ◽  
Barbara Więckowska
Keyword(s):  
Capacity Planning ◽  
Hospital Capacity ◽  
Regulatory Changes ◽  
Hospital Capacity Planning ◽  
The Impact

scholarly journals A Model of Bed Demand to Facilitate the Implementation of Data-driven Recommendations for COVID-19 Capacity Management

2020 ◽  
Author(s):  
Teng Zhang ◽  
Kelly McFarlane ◽  
Jacqueline Vallon ◽  
Linying Yang ◽  
Jin Xie ◽  
...  
Keyword(s):  
Intensive Care ◽  
Acute Care ◽  
Capacity Planning ◽  
Medical Center ◽  
Capacity Management ◽  
Data Driven ◽  
Severe Illness ◽  
Hospital Capacity ◽  
Medicine Service ◽  
Number Of Patients

Abstract Background:We sought to build an accessible interactive model that could facilitate hospital capacity planning in the presence of significant uncertainty about the proportion of the population that is positive forcoronavirus disease 2019 (COVID-19) and the rate at which COVID-19 is spreading in the population. Our goal was to facilitate the implementation of data-driven recommendations for capacity management with a transparent mathematical simulation designed to answer the specific, local questions hospital leadership considered critical.Methods:The model facilitates hospital planning with estimates of the number of Intensive Care (IC) beds, Acute Care (AC) beds, and ventilators necessary to accommodate patients who require hospitalization for COVID-19 and how these compare to the available resources. Inputs to the model include estimates of the characteristics of the patient population and hospital capacity. We deployed this model as an interactive online tool with modifiable parameters.Results:The use of the model is illustrated by estimating the demand generated by COVID-19+ arrivals for a hypothetical acute care medical center. The model calculated that the number of patients requiring an IC bed would equal the number of IC beds on Day 23, the number of patients requiring a ventilator would equal the number of ventilators available on Day 27, and the number of patients requiring an AC bed and coverage by the Medicine Service would equal the capacity of the Medicine service on Day 21. The model was used to inform COVID-19 planning and decision-making, including Intensive Care Unit (ICU) staffing and ventilator procurement.Conclusion:In response to the COVID-19 epidemic, hospitals must understand their current and future capacity to care for patients with severe illness. While there is significant uncertainty around the parameters used to develop this model, the analysis is based on transparent logic and starts from observed data to provide a robust basis of projections for hospital managers. The model demonstrates the need and provides an approach to address critical questions about staffing patterns for IC and AC, and equipment capacity such as ventilators.Contributions to the literature:· Generation and implementation of data-driven recommendations for hospital capacity management early in the COVID-19 pandemic· The conceptualization, development, and deployment of an interactive simulation model in two weeks· Data-driven capacity management in the presence of significant uncertainty about the expected volume of patients, their clinical needs, and the availability of the workforceTrial Registration: Not applicable

A hospital capacity planning model

OR Insight ◽  
1995 ◽  
Vol 8 (2) ◽  
pp. 12-15
Author(s):  
Darryl Gove ◽  
David Hewett
Keyword(s):  
Capacity Planning ◽  
Planning Model ◽  
Hospital Capacity ◽  
Hospital Capacity Planning

Hospital capacity planning for disaster emergency management

2010 ◽  
Vol 44 (3) ◽  
pp. 151-160 ◽  
Author(s):  
Pengfei Yi ◽  
Santhosh K. George ◽  
Jomon Aliyas Paul ◽  
Li Lin
Keyword(s):  
Emergency Management ◽  
Capacity Planning ◽  
Hospital Capacity ◽  
Hospital Capacity Planning

scholarly journals Hospital capacity planning: from measuring stocks to modelling flows

2010 ◽  
Vol 88 (8) ◽  
pp. 632-636 ◽  
Author(s):  
Bernd Rechel ◽  
Stephen Wright ◽  
James Barlow ◽  
Martin McKee
Keyword(s):  
Capacity Planning ◽  
Hospital Capacity ◽  
Hospital Capacity Planning

scholarly journals A model to estimate bed demand for COVID-19 related hospitalization

2020 ◽  
Author(s):  
Teng Zhang ◽  
Kelly McFarlane ◽  
Jacqueline Vallon ◽  
Linying Yang ◽  
Jin Xie ◽  
...  
Keyword(s):  
Intensive Care ◽  
Acute Care ◽  
Capacity Planning ◽  
The United States ◽  
World Health ◽  
Sensitivity Analyses ◽  
Severe Illness ◽  
Hospital Capacity ◽  
Medicine Service ◽  
Number Of Patients

AbstractAs of March 23, 2020 there have been over 354,000 confirmed cases of coronavirus disease 2019 (COVID-19) in over 180 countries, the World Health Organization characterized COVID-19 as a pandemic, and the United States (US) announced a national state of emergency.1, 2, 3 In parts of China and Italy the demand for intensive care (IC) beds was higher than the number of available beds.4, 5 We sought to build an accessible interactive model that could facilitate hospital capacity planning in the presence of significant uncertainty about the proportion of the population that is COVID-19+ and the rate at which COVID-19 is spreading in the population. Our approach was to design a tool with parameters that hospital leaders could adjust to reflect their local data and easily modify to conduct sensitivity analyses.We developed a model to facilitate hospital planning with estimates of the number of Intensive Care (IC) beds, Acute Care (AC) beds, and ventilators necessary to accommodate patients who require hospitalization for COVID-19 and how these compare to the available resources. Inputs to the model include estimates of the characteristics of the patient population and hospital capacity. We deployed this model as an interactive online tool.6 The model is implemented in R 3.5, RStudio, RShiny 1.4.0 and Python 3.7. The parameters used may be modified as data become available, for use at other institutions, and to generate sensitivity analyses.We illustrate the use of the model by estimating the demand generated by COVID-19+ arrivals for a hypothetical acute care medical center. The model calculated that the number of patients requiring an IC bed would equal the number of IC beds on Day 23, the number of patients requiring a ventilator would equal the number of ventilators available on Day 27, and the number of patients requiring an AC bed and coverage by the Medicine Service would equal the capacity of the Medicine service on Day 21.In response to the COVID-19 epidemic, hospitals must understand their current and future capacity to care for patients with severe illness. While there is significant uncertainty around the parameters used to develop this model, the analysis is based on transparent logic and starts from observed data to provide a robust basis of projections for hospital managers. The model demonstrates the need and provides an approach to address critical questions about staffing patterns for IC and AC, and equipment capacity such as ventilators.

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