papers

Prospects of Adjusted Clinical Groups (ACG’s) in capitated payment risk adjustment

Rev Esp Salud Publica 80:55-65. Published in Spanish.

Dirección de Planificación y Desarrollo Organizativo, Badalona, Serveis Assistencials, Badalona, Barcelona, Spain

BACKGROUND: Most patient classification systems have been designed in the United States for the purpose of availing of a tool providing a means of gauging the use of resources. This study was aimed at calculating the mean relative weights (MRW’s) for the cost of care at several primary care health facilities as compared to those in the U.S. by using the Adjusted Clinical Groups (ACG’s) as a possible capitated payment risk adjustment.

METHODS: Descriptive study. All of the clinical records generated by four primary care facilities throughout 2003 were included. The main measurements were: age and gender, resources (visits and costs) and casuistics. The cost model was determined for each individual patient by differentiating the fixed and variable costs. A regression analysis was made for model adjustment purposes. The relative cost of each ACG was calculated by dividing the mean cost of each category by the mean cost of the population as a whole.

RESULTS: A total of 62,311 records were studied, revealing an average of 4.8 +/- 3.2 diagnoses and 7.8 +/- 7.5 visits/patient/year. The total expense was 24.1 million euros, the fixed and semi-fixed costs totaling 28.9% and the variable costs 71.1%. The mean total cost/patient/year was 387.34 +/- 145.87? (reference). The adjusted explicative power of the cost of care between the two classifications (U.S. classification vs. the one studied) was 64.3%; p = 0.000).

CONCLUSIONS: The generalization of the results must be carefully construed. ACG’s show themselves to be a suitable tool, and the mean U.S. RW’s could be used for adjusting capitated payment risk adjustments in view of the difficulty of availing of full, consistent databases in our environment. Further research would be required to back up the consistency of the results.

PMID: 16553260