OBJECTIVE: To develop a case mix model for inpatient substance abuse treatment to assess the effect of case mix on readmission across Veterans Affairs Medical Centers (VAMCs). DATA SOURCES/STUDY SETTING: The computerized patient records from the 116 VAMCs with inpatient substance abuse treatment programs between 1987 and 1992. STUDY DESIGN: Logistic regression was used on patient data to model the effect of demographic, psychiatric, medical, and substance abuse factors on readmission to VAMCs for substance abuse treatment within six months of discharge. The model predictions were aggregated for each VAMC to produce an expected number of readmissions. The observed number of readmissions for each VAMC was divided by its expected number to create a measure of facility performance. Confidence intervals and rankings were used to examine how case mix adjustment changed relative performance among VAMCs. DATA COLLECTION/EXTRACTION METHODS: Ward where care was provided and ICD-9-CM diagnosis codes were used to identify patients receiving treatment for substance abuse (N = 313,886). PRINCIPAL FINDINGS: The case mix model explains 36 percent of the observed facility level variation in readmission. Over half of the VAMCs had numbers of readmissions that were significantly different than expected. There were also noticeable differences between the rankings based on actual and case mix-adjusted readmissions. CONCLUSIONS: Secondary data can be used to build a reasonably stable case mix model for substance abuse treatment that will identify meaningful variation across facilities. Further, case mix has a large effect on facility level readmission rates for substance abuse treatment. Uncontrolled facility comparisons can be misleading. Case mix models are potentially useful for quality assurance efforts.
OBJECTIVE: To develop a case mix model for inpatient substance abuse treatment to assess the effect of case mix on readmission across Veterans Affairs Medical Centers (VAMCs). DATA SOURCES/STUDY SETTING: The computerized patient records from the 116 VAMCs with inpatient substance abuse treatment programs between 1987 and 1992. STUDY DESIGN: Logistic regression was used on patient data to model the effect of demographic, psychiatric, medical, and substance abuse factors on readmission to VAMCs for substance abuse treatment within six months of discharge. The model predictions were aggregated for each VAMC to produce an expected number of readmissions. The observed number of readmissions for each VAMC was divided by its expected number to create a measure of facility performance. Confidence intervals and rankings were used to examine how case mix adjustment changed relative performance among VAMCs. DATA COLLECTION/EXTRACTION METHODS: Ward where care was provided and ICD-9-CM diagnosis codes were used to identify patients receiving treatment for substance abuse (N = 313,886). PRINCIPAL FINDINGS: The case mix model explains 36 percent of the observed facility level variation in readmission. Over half of the VAMCs had numbers of readmissions that were significantly different than expected. There were also noticeable differences between the rankings based on actual and case mix-adjusted readmissions. CONCLUSIONS: Secondary data can be used to build a reasonably stable case mix model for substance abuse treatment that will identify meaningful variation across facilities. Further, case mix has a large effect on facility level readmission rates for substance abuse treatment. Uncontrolled facility comparisons can be misleading. Case mix models are potentially useful for quality assurance efforts.
Authors: Chuan-Fen Liu; Anne E Sales; Nancy D Sharp; Paul Fishman; Kevin L Sloan; Jeff Todd-Stenberg; W Paul Nichol; Amy K Rosen; Susan Loveland Journal: Health Serv Res Date: 2003-10 Impact factor: 3.402