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.

Key Words:

• Conditional value at risk
• health care
• simulation
• waiting time distribution

Additional information

Notes on contributors

Christian Dehlendorff

Christian Dehlendorff is a Ph.D. student in Informatics and Mathematical Modeling at the Technical University of Denmark. He has a M.Sc. in Engineering within data analysis and statistics. His research interests are within design of experiments and computer experiments.

Murat Kulahci

Murat Kulahci is an Associate Professor in Informatics and Mathematical Modeling at the Technical University of Denmark. His research interests include design of experiments, statistical process control, and financial engineering. He is a member of the American Statistical Association, European Network of Business and Industrial Statistics (ENBIS), and the Institute of Operations Research and the Management Sciences.

Soren Merser

Soren Merser is a surgeon (MD) at Clinic of Orthopedic Surgery at Frederiksberg Hospital, Denmark. He is a member of Danish Orthopedic Society and his primary research interest is on-line quality control in hospital units.

Klaus Kaae Andersen

Klaus K. Andersen is an Associate Professor in Informatics and Mathematical Modeling at the Technical University of Denmark. He has a Ph.D. in time series analysis and his research interests are within design of experiments and statistical consulting.