Abstract
BackgroundAn increase in chronic conditions is currently the greatest threat to human health and to the sustainability of health systems. Risk adjustment systems may enable population stratification programmes to be developed and become instrumental in implementing new models of care.The objectives of this study are to evaluate the capability of ACG-PM, DCG-HCC and CRG-based models to predict healthcare costs and identify patients that will be high consumers and to analyse changes to predictive capacity when socio-economic variables are added.MethodsThis cross-sectional study used data of all Basque Country citizens over 14 years of age (n = 1,964,337) collected in a period of 2 years. Data from the first 12 months (age, sex, area deprivation index, diagnoses, procedures, prescriptions and previous cost) were used to construct the explanatory variables. The ability of models to predict healthcare costs in the following 12 months was assessed using the coefficient of determination and to identify the patients with highest costs by means of receiver operating characteristic (ROC) curve analysis.ResultsThe coefficients of determination ranged from 0.18 to 0.21 for diagnosis-based models, 0.17-0.18 for prescription-based and 0.21-0.24 for the combination of both. The observed area under the ROC curve was 0.78-0.86 (identifying patients with a cost higher than P-95) and 0.83-0.90 (P-99). The values of the DCG-HCC models are slightly higher and those of the CRG models are lower, although prescription information could not be used in the latter. On adding previous cost data, differences between the three systems decrease appreciably. Inclusion of the deprivation index led to only marginal improvements in explanatory power.ConclusionThe case-mix systems developed in the USA can be useful in a publicly financed healthcare system with universal coverage to identify people at risk of high health resource consumption and whose situation is potentially preventable through proactive interventions.
Highlights
An increase in chronic conditions is currently the greatest threat to human health and to the sustainability of health systems
Our study has revealed that the three case-mix systems used (ACG-PM, Clinical Risk Groups (CRG) and Diagnostic Cost Groups/Hierarchical Condition Categories (DCG-HCC)) show sufficient capability to predict use of health resources and identify people with high needs over the 12 months in an environment other than that for which they were designed and using other sources of information, namely, a publicly-funded universal coverage system and in the absence of an information system based on the use of claim data
Our study has shown that case-mix systems developed in the U.S can be used in a publicly financed healthcare system with universal health insurance such as that of the Basque Country, to predict consumption of health resources
Summary
An increase in chronic conditions is currently the greatest threat to human health and to the sustainability of health systems. Risk adjustment systems may enable population stratification programmes to be developed and become instrumental in implementing new models of care. In this context, in 2010 the Basque Government’s Department of Health published a Strategy to tackle the challenge of Chronicity in the Basque Country [2], containing a series of policies and projects to reinvent the healthcare delivery model and adapt it to this new situation. In order for interventions to be effective and efficient, they should be implemented among those patients whose care needs match the profile for which they were designed. This fact raises the need to develop a population stratification system based on risk adjustment mechanisms. Systems that contain these variables are easier to interpret for the healthcare professionals responsible for caring for these patients
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
