Demand fluctuations lead to variations in output levels, affecting a hospital's efficiency measures. For a typical efficiency measure, data envelopment analysis (DEA) is a nonparametric linear programming technique to estimate the production function and then efficiency. However, using units sold as an output measure could bias the efficiency estimate. In particular, DEA implicitly assumes hospitals will be able to perfectly predict customer demands for hospital services or that hospitals can adjust input resources without any time delays. The present study accounts for the expected output and uses the concept of "effectiveness measure" calculated from the input-truncated production function. A hospital can achieve effective production if its input levels are equal to the effective input levels identified by the forecast output in the next period and the input-truncated production function. That is, the effectiveness measure provides a predictive efficiency analysis. A low effectiveness measure implies that the hospital used more inputs than necessary or more than the forecasted demand, and the implication is consistent with a medical arms race. An empirical study of US hospitals in 2009-2011 was conducted to validate the proposed model and provide managerial insights to drive productivity in the hospital industry.
|Title of host publication||Advances in DEA Theory and Applications|
|Subtitle of host publication||With Examples in Forecasting Models|
|Number of pages||15|
|Publication status||Published - 2016 Oct 21|
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