摘要
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.
| 原文 | English |
|---|---|
| 主出版物標題 | Advances in DEA Theory and Applications |
| 主出版物子標題 | With Examples in Forecasting Models |
| 發行者 | Wiley-Blackwell |
| 頁面 | 404-418 |
| 頁數 | 15 |
| ISBN（電子） | 9781118946688 |
| ISBN（列印） | 9781118946701 |
| DOIs | |
| 出版狀態 | Published - 2016 十月 21 |
指紋
All Science Journal Classification (ASJC) codes
- Mathematics(all)
引用此文
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Predictive efficiency analysis : A study of us hospitals. / Johnson, Andrew L.; Lee, Chia Yen.
Advances in DEA Theory and Applications: With Examples in Forecasting Models. Wiley-Blackwell, 2016. p. 404-418.研究成果: Chapter
TY - CHAP
T1 - Predictive efficiency analysis
T2 - A study of us hospitals
AU - Johnson, Andrew L.
AU - Lee, Chia Yen
PY - 2016/10/21
Y1 - 2016/10/21
N2 - 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.
AB - 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.
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U2 - 10.1002/9781118946688.ch26
DO - 10.1002/9781118946688.ch26
M3 - Chapter
AN - SCOPUS:85047755035
SN - 9781118946701
SP - 404
EP - 418
BT - Advances in DEA Theory and Applications
PB - Wiley-Blackwell
ER -