Design of a Model to Predict Surge Capacity Bottlenecks for Burn Mass Casualties at a Large Academic Medical Center

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Original Research

Introduction

In 2006, the Institute of Medicine published Hospital-Based Emergency Care: At the Breaking Point¹stating that hospitals have limited ability to manage small-scale and, much less, large-scale disasters. Simultaneously, the Society for Academic Emergency Medicine established priorities for the study of surge,²among which was the ability to predict maximal surge capacity.

Surge capacity is the maximum ability of a health care entity to deliver required resources to handle a significant increase in demand relative to baseline.³Components of surge capacity are recognized as staffing, supplies and equipment, beds, and management system.^3-6Developing tools that accurately predict surge capacity will aid hospitals to more effectively prepare for and respond to mass-casualty incidents (MCI) and disasters.

Existing surge models in the published literature consist of a model developed by the Chicago Department of Public Health,⁷the FluSurge model developed by the Centers for Disease Control and Prevention,⁸and the Hospital Surge Model developed by the Agency for Healthcare Research and Quality (AHRQ).⁹The Chicago model estimates the quantity of supplies and equipment required to take care of different numbers of mass casualties resulting from a number of different types of MCIs. The AHRQ model also accounts for staffing and bed requirements, given different MCI scenarios. Neither of these two models takes into account the baseline quantity of resources (beds, staffing, floor, and operative resources) available at a hospital at the time an MCI occurs, or resource utilization by non-MCI patients. The FluSurge model estimates the increase in demand for resources during an influenza pandemic, but is otherwise limited in its estimates of strains on institutional resources.

Burn care is often necessary across different types of MCIs, is highly specialized, and time- and resource-intensive in ways that challenge health care institutions with multiple layers of complexity. Therefore, a simulation model that can account for baseline patient flow and resource utilization while incorporating the effect of a large influx of patients from a burn MCI can serve as a powerful tool that can predict and help address bottlenecks resulting from insufficient resources, before they occur.

The burn model that resulted from this research can serve as an example of a...