No Vacancy: Microsimulation Modeling of Supply-Side Interventions to Meet Excess Demand for Hospital Services during a Pandemic Flu

Presenter: Andrew Barnes, National Health Foundation

Abstract

Growing concern over acute, unpredictable public health emergencies have led to disaster planning at the local healthcare service and governmental levels. Exogenous shocks to hospital systems, like a pandemic flu, may cause temporary surges in demand for hospital care in excess of current supply. However, quantifying excess demand has been absent from many preparedness efforts. To meet this need, the Pandemic Flu Hospital Planning Model (the Model) was developed and is being tested in the Los Angeles (LA) County hospital market. After testing the Model for pandemic flu events in LA County, the model may be applied to other hospital markets and to simulate the impact of alternate disaster scenarios on demand for hospital services.
The Model is a stochastic, discrete-event microsimulation model of inpatient and emergency department care. In the Model, patients arrive at emergency departments requiring different levels of care or at inpatient wards needing scheduled or urgent services. Using patient-, hospital-, and geographic-level data, patients are routed to hospitals in the system according to historical utilization patterns, patient demographics, and bed type required. Model outputs include numbers of patients needing beds when none are available by week of the pandemic and in each regional market. Other variables estimated include the additional number of beds needed at each hospital to meet demand, numbers of ventilators needed, and inpatient occupancy rates.
In severe flu simulations, current supplies of adult intensive care hospital beds are exhausted within the first four weeks of the pandemic and remain so weeks after the peak of the event. Excess demand for these beds continues to rise over the course of the pandemic as throughput of patients is slowed by higher numbers of sicker patients with longer lengths of stay. Pediatric acute and critical care beds also experience the large increases in demand. Finally, additional supply of critical care beds required to meet expected demand exceeds 20% of current levels.
Much of disaster planning to date has been based on expert opinions or findings from static, “out-of-the-box”, models. Dynamic microsimulation models, like ours, are useful tools for planners to determine how much and where additional supply is required to minimize individual, hospital, and social costs. Health services and health economics research could benefit from more efforts to simulate patient flow through an interconnected system of services to plan for a variety of shocks besides Pandemic Flu including hospital closures and other disasters such as floods and earthquakes.