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

BACKGROUND: Pharmaceutical 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 individ...

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Autores principales: Vivas-Consuelo, David, Usó-Talamantes, Ruth, Guadalajara-Olmeda, Natividad, Trillo-Mata, José-Luis, Sancho-Mestre, Carla, Buigues-Pastor, Laia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4283085/
https://www.ncbi.nlm.nih.gov/pubmed/25331531
http://dx.doi.org/10.1186/1472-6963-14-462
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author Vivas-Consuelo, David
Usó-Talamantes, Ruth
Guadalajara-Olmeda, Natividad
Trillo-Mata, José-Luis
Sancho-Mestre, Carla
Buigues-Pastor, Laia
author_facet Vivas-Consuelo, David
Usó-Talamantes, Ruth
Guadalajara-Olmeda, Natividad
Trillo-Mata, José-Luis
Sancho-Mestre, Carla
Buigues-Pastor, Laia
author_sort Vivas-Consuelo, David
collection PubMed
description BACKGROUND: Pharmaceutical 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. METHODS: Descriptive 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. RESULTS: The risk adjustment tool proved to have an acceptable level of prediction (R(2) ≥ 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. CONCLUSION: The 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.
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spelling pubmed-42830852015-01-06 Pharmaceutical cost management in an ambulatory setting using a risk adjustment tool Vivas-Consuelo, David Usó-Talamantes, Ruth Guadalajara-Olmeda, Natividad Trillo-Mata, José-Luis Sancho-Mestre, Carla Buigues-Pastor, Laia BMC Health Serv Res Research Article BACKGROUND: Pharmaceutical 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. METHODS: Descriptive 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. RESULTS: The risk adjustment tool proved to have an acceptable level of prediction (R(2) ≥ 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. CONCLUSION: The 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. BioMed Central 2014-10-21 /pmc/articles/PMC4283085/ /pubmed/25331531 http://dx.doi.org/10.1186/1472-6963-14-462 Text en © Vivas-Consuelo et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Vivas-Consuelo, David
Usó-Talamantes, Ruth
Guadalajara-Olmeda, Natividad
Trillo-Mata, José-Luis
Sancho-Mestre, Carla
Buigues-Pastor, Laia
Pharmaceutical cost management in an ambulatory setting using a risk adjustment tool
title Pharmaceutical cost management in an ambulatory setting using a risk adjustment tool
title_full Pharmaceutical cost management in an ambulatory setting using a risk adjustment tool
title_fullStr Pharmaceutical cost management in an ambulatory setting using a risk adjustment tool
title_full_unstemmed Pharmaceutical cost management in an ambulatory setting using a risk adjustment tool
title_short Pharmaceutical cost management in an ambulatory setting using a risk adjustment tool
title_sort pharmaceutical cost management in an ambulatory setting using a risk adjustment tool
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4283085/
https://www.ncbi.nlm.nih.gov/pubmed/25331531
http://dx.doi.org/10.1186/1472-6963-14-462
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