Conditional Value at Risk as a Measure for Waiting Time in Simulations of Hospital Units

Abstract The utility of conditional value at risk (CVaR) of a sample of waiting times as a measure for reducing long waiting times is evaluated with special focus on patient waiting times in a hospital. CVaR is the average of the longest waiting times, i.e., a measure at the tail of the waiting time distribution. The presented results are based on a discrete event simulation (DES) model of an orthopedic surgical unit at a university hospital in Denmark. Our analysis shows that CVaR offers a highly reliable performance measure. The measure targets the longest waiting times and these are generally accepted to be the most problematic from the points of view of both the patients and the management. Moreover, CVaR can be seen as a compromise between the well known measures: average waiting time and the maximum waiting time.

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