Php11 Efficiency in Drug Prescription Measured Bythe Application of Adjusted Clinical Groups in Five Spanish Primary Care Centres

Sicras-Mainar, A.; Navarro‐Artieda, Ruth; Ruano-Ruano, I; Velasco-Velasco, Soledad; Frias-Garrido, X; Llopart, Josep Ramon; Llausí-Sellés, Rosa

doi:10.1016/s1098-3015(10)65289-8

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“…Although a few articles attempted to predict or explain variations of medication use by applying the Johns Hopkins ACG case-mix system, these analytical models are mainly based on diagnosis-based risk adjusters (i.e. the EDCs, ADGs, or ACGs) within the ACG system [ 10 , 11 , 46 ]. Two recent articles reported studies that had applied the Johns Hopkins ACG system for identifying high-risk patients and predicting healthcare utilization.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Rx-defined morbidity groups and diagnosis- based risk adjusters for predicting healthcare costs in Taiwan

Kuo

Lai

2010

BMC Health Serv Res

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BackgroundMedication claims are commonly used to calculate the risk adjustment for measuring healthcare cost. The Rx-defined Morbidity Groups (Rx-MG) which combine the use of medication to indicate morbidity have been incorporated into the Adjusted Clinical Groups (ACG) Case Mix System, developed by the Johns Hopkins University. This study aims to verify that the Rx-MG can be used for adjusting risk and for explaining the variations in the healthcare cost in Taiwan.MethodsThe Longitudinal Health Insurance Database 2005 (LHID2005) was used in this study. The year 2006 was chosen as the baseline to predict healthcare cost (medication and total cost) in 2007. The final sample size amounted to 793 239 (81%) enrolees, and excluded any cases with discontinued enrolment. Two different kinds of models were built to predict cost: the concurrent model and the prospective model. The predictors used in the predictive models included age, gender, Aggregated Diagnosis Groups (ADG, diagnosis- defined morbidity groups), and Rx-defined Morbidity Groups. Multivariate OLS regression was used in the cost prediction modelling.ResultsThe concurrent model adjusted for Rx-defined Morbidity Groups for total cost, and controlled for age and gender had a better predictive R-square = 0.618, compared to the model adjusted for ADGs (R2 = 0.411). The model combined with Rx-MGs and ADGs performed the best for concurrently predicting total cost (R2 = 0.650). For prospectively predicting total cost, the model combined Rx-MGs and ADGs (R2 = 0.382) performed better than the models adjusted by Rx-MGs (R2 = 0.360) or ADGs (R2 = 0.252) only. Similarly, the concurrent model adjusted for Rx-MGs predicting pharmacy cost had a better performance (R-square = 0.615), than the model adjusted for ADGs (R2 = 0.431). The model combined with Rx-MGs and ADGs performed the best in concurrently as well as prospectively predicting pharmacy cost (R2 = 0.638 and 0.505, respectively). The prospective models showed a remarkable improvement when adjusted by prior cost.ConclusionsThe medication-based Rx-Defined Morbidity Groups was useful in predicting pharmacy cost as well as total cost in Taiwan. Combining the information on medication and diagnosis as adjusters could arguably be the best method for explaining variations in healthcare cost.

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Section: Discussionmentioning

confidence: 99%