D M Burnett1, D X Cifu, S Kolakowsky-Hayner, J S Kreutzer. 1. Department of Physical Medicine and Rehabilitation, Medical College of Virginia/Virginia Commonwealth University, Richmond, VA, USA. dmburnet@hsc.vcu.edu
Abstract
OBJECTIVE: To describe the distribution of charges, to distinguish between "charge outliers" and nonoutliers, and to identify a model that uses demographics and injury characteristics to predict charge outlier status in individuals with spinal cord injury (SCI). DESIGN: Retrospective data analysis of patients admitted to 24 acute inpatient rehabilitation national Spinal Cord Injury Model Systems centers. Statistical analysis, including proportions, means, and standard deviations (SDs), were compiled for the following variables: demographic and injury information, rehabilitation charges, medical complications, associated injuries, and surgical procedures. SETTING: Tertiary, university medical centers participating in the National Institute on Disability and Rehabilitation Research's SCI Model Systems project. PARTICIPANTS: A total of 13,392 patients who were admitted to 24 acute, intensive, interdisciplinary rehabilitation settings after traumatic SCI between November 1972 and August 1996. MAIN OUTCOME MEASURES: Statistical data analysis was used to determine significance between charge outliers and nonoutliers on the basis of demographic, injury characteristics, and clinical factors. Outliers, under the current diagnostic-related group system, are defined as cases in which lengths of stay exceed the mean by the lesser of 20 days or 1.94 SDs. RESULTS: Statistically significant differences were found between SCI charge outliers and nonoutliers based on ethnicity, education, employment, level of injury, American Spinal Injury Association impairment classification, and sponsor of hospitalization. On average, outliers were 4 years older than nonoutliers, and tended to have more associated injuries, pressure ulcers, surgical procedures, and medical complications. A forward-conditional stepwise multiple logistic regression analysis was used to confirm univariate analysis and to predict the presence or absence of outliers based on the predictor variables. A model for the prediction of SCI charge outlier status was defined. CONCLUSIONS: SCI charge outliers are most likely to be retired, insured, have high cervical level injuries, and be educated beyond high school. Improved treatment efficiency serves as a means of cost reduction and is a reason to identify outlier characteristics.
OBJECTIVE: To describe the distribution of charges, to distinguish between "charge outliers" and nonoutliers, and to identify a model that uses demographics and injury characteristics to predict charge outlier status in individuals with spinal cord injury (SCI). DESIGN: Retrospective data analysis of patients admitted to 24 acute inpatient rehabilitation national Spinal Cord Injury Model Systems centers. Statistical analysis, including proportions, means, and standard deviations (SDs), were compiled for the following variables: demographic and injury information, rehabilitation charges, medical complications, associated injuries, and surgical procedures. SETTING: Tertiary, university medical centers participating in the National Institute on Disability and Rehabilitation Research's SCI Model Systems project. PARTICIPANTS: A total of 13,392 patients who were admitted to 24 acute, intensive, interdisciplinary rehabilitation settings after traumatic SCI between November 1972 and August 1996. MAIN OUTCOME MEASURES: Statistical data analysis was used to determine significance between charge outliers and nonoutliers on the basis of demographic, injury characteristics, and clinical factors. Outliers, under the current diagnostic-related group system, are defined as cases in which lengths of stay exceed the mean by the lesser of 20 days or 1.94 SDs. RESULTS: Statistically significant differences were found between SCI charge outliers and nonoutliers based on ethnicity, education, employment, level of injury, American Spinal Injury Association impairment classification, and sponsor of hospitalization. On average, outliers were 4 years older than nonoutliers, and tended to have more associated injuries, pressure ulcers, surgical procedures, and medical complications. A forward-conditional stepwise multiple logistic regression analysis was used to confirm univariate analysis and to predict the presence or absence of outliers based on the predictor variables. A model for the prediction of SCI charge outlier status was defined. CONCLUSIONS: SCI charge outliers are most likely to be retired, insured, have high cervical level injuries, and be educated beyond high school. Improved treatment efficiency serves as a means of cost reduction and is a reason to identify outlier characteristics.