Including quality, access, and efficiency in healthcare cost models.

The authors investigated cost models that incorporate quality, access, and efficiency to provide decision support for resource forecasting in the multi-billion-dollar U.S. Army health system. As the Army relocates thousands of troops, the medical system must plan for changes in demand; this study supports that effort. Loglinear cost models that include data envelopment analysis (DEA) efficiency scores were evaluated through ordinary least squares estimation, ridge regression, and robust regression, and serve as the analytical framework. Parsimonious models that incorporate a simple volume-complexity metric, a DEA metric, a quality metric, and medical center status variable provide superior forecasting capability.

Cost drivers and resource allocation in military health care systems.

This study illustrates the feasibility of incorporating technical efficiency considerations in the funding of military hospitals and identifies the primary drivers for hospital costs. Secondary data collected for 24 U.S.-based Army hospitals and medical centers for the years 2001 to 2003 are the basis for this analysis. Technical efficiency was measured by using data envelopment analysis; subsequently, efficiency estimates were included in logarithmic-linear cost models that specified cost as a function of volume, complexity, efficiency, time, and facility type. These logarithmic-linear models were compared against stochastic frontier analysis models. A parsimonious, three-variable, logarithmic-linear model composed of volume, complexity, and efficiency variables exhibited a strong linear relationship with observed costs (R(2) = 0.98). This model also proved reliable in forecasting (R(2) = 0.96). Based on our analysis, as much as $120 million might be reallocated to improve the United States-based Army hospital performance evaluated in this study.