Pharmaceutical cost management in an ambulatory setting using a risk adjustment tool

Abstract

BackgroundPharmaceutical expenditure is undergoing very high growth, and accounts for 30% of overall healthcare expenditure in Spain. In this paper we present a prediction model for primary health care pharmaceutical expenditure based on Clinical Risk Groups (CRG), a system that classifies individuals into mutually exclusive categories and assigns each person to a severity level if s/he has a chronic health condition. This model may be used to draw up budgets and control health spending.MethodsDescriptive study, cross-sectional. The study used a database of 4,700,000 population, with the following information: age, gender, assigned CRG group, chronic conditions and pharmaceutical expenditure. The predictive model for pharmaceutical expenditure was developed using CRG with 9 core groups and estimated by means of ordinary least squares (OLS). The weights obtained in the regression model were used to establish a case mix system to assign a prospective budget to health districts.ResultsThe risk adjustment tool proved to have an acceptable level of prediction (R2 ≥ 0.55) to explain pharmaceutical expenditure. Significant differences were observed between the predictive budget using the model developed and real spending in some health districts. For evaluation of pharmaceutical spending of pediatricians, other models have to be established.ConclusionThe model is a valid tool to implement rational measures of cost containment in pharmaceutical expenditure, though it requires specific weights to adjust and forecast budgets.

Measurement of health-related quality by multimorbidity groups in primary health care

“…In our study, the population classified in MHS 1 (“healthy” or “non-users”) represented 53.9%, which differed from 61% in the Spanish Autonomous Communities of Madrid [10] or from 67% in the Spanish Autonomous Communities of Catalonia [8]. This was due to the gap that appears when implementing information systems [43, 44]. Following up the prevalence of chronic diseases is a good indicator to know and monitor their degree of implementing diagnosis codes [42].…”

Erratum to: Pharmaceutical cost management in an ambulatory setting using a risk adjustment tool

“…Following the publication of this article [1] the authors would like to correct their ‘Acknowledgments’ section to read: “This work was supported by “Instituto de Salud Carlos III - Ministerio de Economía y Competitividad” and the European Union (FEDER funds) — FIS PI12/00037”. The authors would like to thank members (Juan Bru and Inma Saurí) of the Pharmacoeconomics Office of the Valencian Health Department.…”

Evaluación del modelo de descripción de pacientes: Grupo de Riesgo Clínico (CRG) como indicador y predictor de consumo de recursos y su relación con los Factores de Riesgo Cardiovascular y la Calidad de Vida Relacionada con la Salud

“…Más recientemente, el mismo grupo de trabajo (Vivas-Consuelo et al, 2014a), aplicó el modelo CRG sobre toda la población de la CV, unos 4,7 millones de individuos durante los años 2012 y 2013, con los datos de la historia clínica electrónica de atención primaria (SIA), de los hospitales (CMBD) y del módulo de prescripción farmacéutica (GAIA), y demostraron la relación entre el gasto farmacéutico y la morbilidad. Además D. Vivas (Vivas-Consuelo et al, 2014a), en el modelo de estratificación de la población mediante los CRGs, relacionaron cada estado de salud con el gasto farmacéutico y desarrollaron sus propios pesos para cada estado de salud básico de CRG.…”

“…Además D. Vivas (Vivas-Consuelo et al, 2014a), en el modelo de estratificación de la población mediante los CRGs, relacionaron cada estado de salud con el gasto farmacéutico y desarrollaron sus propios pesos para cada estado de salud básico de CRG. Fuente: adaptado del estudio realizado por D. Vivas (Vivas-Consuelo et al, 2014a) …”

“…Análisis descriptivo por edad y pesos simples del CRG Una vez estratificada la población en los 9 estados de salud, se asigna a cada CRG un peso cuyo valor es numérico y representa la morbilidad (Vivas-Consuelo et al, 2014a). Ver Tabla 10.…”