CONTEXT: Length of stay data are increasingly used to monitor ICU economic performance. How such material is presented greatly affects its utility. OBJECTIVE: To develop a weighted length of stay index and to estimate expected length of stay. To assess alternative ways to summarize weighted length of stay to evaluate ICU economic performance. DESIGN: Retrospective database study. SUBJECTS: Data for 751 ICU patients in 1998 at two hospitals used to develop weighted length of stay index. Data on 42,237 patients from 72 ICUs used as the basis of economic performance evaluation. MAIN OUTCOME MEASURES: Difference between actual and expected weighted length of stay, where expected weighted length of stay is based on patient clinical characteristics. RESULTS: Length of stay statistically explains approximately 85 to 90% of interpatient variation in hospital costs. The first ICU day is approximately four times as expensive, and other ICU days approximately 2.5 times as expensive, as non-ICU hospital days. In a regression model for weighted length of stay, patient clinical characteristics explain 26% of variation. ICU economic performance can be measured by excess weighted length of stay of a "typical" patient or by occurrence of long excess weighted lengths of stay. Although different summary measures of performance are highly correlated, choice of measure affects relative ranking of some ICUs' performance. CONCLUSION: Providers of statistical data on ICU economic performance should adjust length of stay for patient characteristics and provide multiple summary measures of the statistical distribution, including measures that address both the typical patient and outliers.
CONTEXT: Length of stay data are increasingly used to monitor ICU economic performance. How such material is presented greatly affects its utility. OBJECTIVE: To develop a weighted length of stay index and to estimate expected length of stay. To assess alternative ways to summarize weighted length of stay to evaluate ICU economic performance. DESIGN: Retrospective database study. SUBJECTS: Data for 751 ICU patients in 1998 at two hospitals used to develop weighted length of stay index. Data on 42,237 patients from 72 ICUs used as the basis of economic performance evaluation. MAIN OUTCOME MEASURES: Difference between actual and expected weighted length of stay, where expected weighted length of stay is based on patient clinical characteristics. RESULTS: Length of stay statistically explains approximately 85 to 90% of interpatient variation in hospital costs. The first ICU day is approximately four times as expensive, and other ICU days approximately 2.5 times as expensive, as non-ICU hospital days. In a regression model for weighted length of stay, patient clinical characteristics explain 26% of variation. ICU economic performance can be measured by excess weighted length of stay of a "typical" patient or by occurrence of long excess weighted lengths of stay. Although different summary measures of performance are highly correlated, choice of measure affects relative ranking of some ICUs' performance. CONCLUSION: Providers of statistical data on ICU economic performance should adjust length of stay for patient characteristics and provide multiple summary measures of the statistical distribution, including measures that address both the typical patient and outliers.
Authors: Mark Unroe; Jeremy M Kahn; Shannon S Carson; Joseph A Govert; Tereza Martinu; Shailaja J Sathy; Alison S Clay; Jessica Chia; Alice Gray; James A Tulsky; Christopher E Cox Journal: Ann Intern Med Date: 2010-08-03 Impact factor: 25.391