B0439
Title: Bias-corrected performance measurement of health care units in the presence of targeted variables
Authors: Panagiotis Zervopoulos - University of Sharjah (United Arab Emirates) [presenting]
Abstract: Noise in data is not uncommon in real-world cases, although it is commonly omitted from performance measurement studies. We develop a stochastic DEA-based methodology to measure performance when the endogenous (e.g. efficiency) and exogenous variables (e.g. perspectives of patients satisfaction), which are incorporated in the assessment, are inversely related. This methodology identifies benchmark units that are not only highly performing but are also assigned scores for their exogenous variables, which are at least equal to user-defined critical values. The measurements we obtain are bias corrected as we apply a multi-parametric method for bias correction, which results in better estimates than the bootstrap-based methods.
