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Measuring Delay Discounting in Humans Using an Adjusting Amount Task

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Published on: January 9, 2016

Measuring hospital efficiency with Data Envelopment Analysis: nonsubstitutable vs. substitutable inputs and outputs.

Darold T Barnum¹, Surrey M Walton, Karen L Shields

¹Department of Managerial Studies, University of Illinois, Chicago, IL, USA. dbarnum@uic.edu

Journal of Medical Systems

|August 13, 2010

Summary

This summary is machine-generated.

Conventional Data Envelopment Analysis (DEA) overestimates hospital efficiency due to non-substitutable variables. New indicators are needed for accurate hospital efficiency measurement when inputs/outputs are not interchangeable.

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Last Updated: Jun 10, 2026

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

Area of Science:

Health Services Research
Operations Research
Econometrics

Background:

Data Envelopment Analysis (DEA) assumes substitutability between inputs and outputs, a condition often violated in healthcare settings.
Conventional DEA models applied to hospitals frequently use non-substitutable variables, leading to theoretical and practical inconsistencies.

Purpose of the Study:

To develop and validate new efficiency indicators for Data Envelopment Analysis (DEA) that accommodate non-substitutable variables.
To compare the performance of these novel indicators against traditional DEA measures in assessing hospital efficiency.

Main Methods:

Development of novel efficiency indicators designed for non-substitutable inputs and outputs.
Application of both conventional and new DEA measures to a sample of 87 community hospitals.
Comparative analysis of efficiency estimates derived from the two sets of DEA models.

Main Results:

Conventional DEA models significantly overestimated average hospital efficiency.
A substantial number of inefficient hospitals were incorrectly classified as efficient by standard DEA.
Discrepancies in efficiency overestimation varied significantly among hospitals, compromising comparative analyses.

Conclusions:

Standard Data Envelopment Analysis (DEA) models are unreliable for measuring hospital efficiency when inputs/outputs are non-substitutable.
The use of efficiency indicators valid for non-substitutability is recommended for accurate hospital performance assessment.
Alternative approaches, such as appropriate weighting or statistical combination of non-substitutable variables, should precede DEA application in such cases.