CASE-MIX ADJUSTMENT USING OBJECTIVE MEASURES OF SEVERITY - THE CASE FOR LABORATORY DATA

Objective. We evaluate the use of routinely gathered laboratory data t o subclassify surgical and nonsurgical major diagnostic categories int o groups homogeneous with respect to length of stay (LOS). Data Source s and Study Setting. The source of data is the Combined Patient Experi ence database (COPE), created by merging data from computerized source s at the University of California San Francisco (UCSF) Medical Center and Stanford University Medical Center for a total sample size of 73,1 17 patient admissions. Study Design. The study is cross-sectional and retrospective. All data were extracted from COPE consecutive admission s, the unit of analysis is an admission. The outcome variable LOS prox ies hospital resource utilization for an inpatient stay. Nine (candida te) predictor variables were derived from seven lab tests (WBC, Na, K, CO2, BUN, ALB, HCT) by recording the whole-stay minimum or maximum te st result. Data Collection/Extraction Methods. Patient groups were for med by first assigning to major diagnostic categories (MDCs) all 73,11 7 admissions. Each MDC was then partitioned into medical and surgical subgroups (sub-MDCs). The 13 sub-MDCs selected for study define a stud y population of 32,599 patients that represents approximately 45 perce nt of inpatients. Within each of the 13 sub-MDCs, patients were random ly assigned to one of two data sets in a ratio of 2:1. The first set w as used to create, the second to validate, three different LOS predict ors. Predictive accuracies of individual DRG classes were compared wit h those of two alternative classification schemes, one formed by recur sive partitioning (the sub-MDC) using only lab test results, the other by partitioning with both lab test results and individual DRGs. Princ ipal Findings. For the eight largest sub-MDCs (81 percent of study pop ulation), individual DRGs explained 23 percent of the within sub-MDC v ariance in LOS, laboratory data classes explained 31 percent, and clas ses derived by considering individual DRGs and laboratory data explain ed 37 percent. (Each result is a weighted average R2. The average numb er of LOS classes into which the eight largest sub-MDCs were partition ed were 20, 10, and 10, respectively. Within six of the eight, partiti oning on the basis of laboratory data alone explained more within sub- MDC variance than did partitioning into individual DRGs. Conclusions. Routine test data improve the accuracy of LOS prediction over that pos sible using DRG classes. We note that the improvements do not result f rom overfitting the data, since the numbers of LOS classes we use to p redict LOS are considerably fewer than the numbers of individual DRGs.