A flatter curve affords hospitals greater time to prepare for a pandemic surge

This paper quantifies the benefits of flattening the curve (with a constant total patient load over the study period) on the risk of a hospital bed shortage in a pandemic. Using discrete-event simulation of patient care paths in hospitals, synthetic data that eliminates issues of confounding affects from the simultaneous occurrence of regional response actions and/or changes in resources, treatments or other situational circumstances, is produced for estimating hospital capacity for pandemic response. Results from systematically designed numerical experiments produced several findings. These include that the higher the acceleration in pandemic patient demand growth, the greater the impact of the intervention. Cutting this acceleration by 75% from the greatest studied rate created over four additional weeks to prepare for an 80% risk of running out of intensive care beds. Additionally, the greater the acceleration in growth, the fewer the days with a high risk of running out of beds, but the greater the total number of critical patients that could not be served with existing resources. Finally, the lower this acceleration, the fewer resources or modifications needed to cope with the surge, but the longer they are needed. The findings further show how hospitals can benefit from analytical tools that exploit digital health information to predict and plan for need levels and time to onset of these levels. These tools can be embedded within a real-time framework in which automated and early warnings can inform the selection of strategies for managing or coping with expected increases in demand for emergency hospital services.

An Overview of Hospital Capacity Planning and Optimisation.

Health care is uncertain, dynamic, and fast growing. With digital technologies set to revolutionise the industry, hospital capacity optimisation and planning have never been more relevant. The purposes of this article are threefold. The first is to identify the current state of the art, to summarise/analyse the key achievements, and to identify gaps in the body of research. The second is to synthesise and evaluate that literature to create a holistic framework for understanding hospital capacity planning and optimisation, in terms of physical elements, process, and governance. Third, avenues for future research are sought to inform researchers and practitioners where they should best concentrate their efforts. In conclusion, we find that prior research has typically focussed on individual parts, but the hospital is one body that is made up of many interdependent parts. It is also evident that past attempts considering entire hospitals fail to incorporate all the detail that is necessary to provide solutions that can be implemented in the real world, across strategic, tactical and operational planning horizons. A holistic approach is needed that includes ancillary services, equipment medicines, utilities, instrument trays, supply chain and inventory considerations.

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 5 specific interventions and 2 critical shifts in care strategies to significantly increase hospital capacity is also described. CONCLUSIONS: These estimates enable hospitals to repurpose space, modify operations, implement crisis standards of care, collaborate with other health care facilities, or request external support, thereby increasing the likelihood that arriving patients will find an open staffed bed when 1 is needed.