When the inevitable next pandemic influenza virus emerges, hospitals will be challenged to meet the requirements of a large cohort of individuals with varying degrees of illness. These patients will likely strain all the resources of hospitals including personnel, medical supplies, pharmaceutical supplies, and medical equipment. Because of the uncertainty regarding the magnitude and the nuances inherent in such events, it is a difficult task for a hospital to right-size its planning. Several tools exist, however, that have been developed to help provide estimates of supply needs including one developed by my colleagues: Panalysis.
To provide a real-world test of Panalysis, a team of us from the Center, Interdisciplinary Solutions, the University of Pennsylvania’s Wharton School, and the Mayo Clinic performed a stress test of the Mayo Clinic’s emergency pandemic supplies using various modeled scenarios. The result of that exercise was just published in the American Journal of Infection Control.
In this paper, my colleagues and I developed several different pandemic influenza scenarios of varying severity and, using Monte Carlo simulation, juxtaposed it against the specific features of Mayo Clinic and its patient catchment region in multiple iterations. Through the simulations, we could generate demand curves for certain supplies such as oseltamivir, gloves, and ventilators allowing insight into what types of demand would be expected for each of these items during various pandemic scenarios.
Using these demand curves, a facility like the Mayo Clinic could determine what level of preparedness they determined it prudent to invest in and compare current stockpiles to desired levels. For example, ventilator inventories could be maintained to be sufficient to meet the demands expected for 75% of the pandemic scenarios generated and an attendant cost generated. Similar cost-benefit analysis could be applied to N-95 respirators, courses of oseltamivir, or any other relevant item.
Every hospital will face unique challenges based on their location, services offered, catchment demographics, and size. Each will also have a differing risk calculus for preparedness and, instead of approaching this vital issue in an off-the-cuff/back-of-the-envelope manner tools such as Panalysis could be implemented to help bring rigor and quantification to these decisions allowing them to be evaluated in a manner much more fitting to their importance.