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Revista Española de Salud Pública

versión On-line ISSN 2173-9110versión impresa ISSN 1135-5727

Resumen

MERA FLORES, Ana María; BUSTO BONIFAZ, Sebastián del  y  BERNAL SOBRINO, José Luis. Assessment of Three Risk Adjustment Systems as Predictors of the Consumption of Medicines and Medical Supplies at Polyvalent Hospitalization Units. Spain. Rev. Esp. Salud Publica [online]. 2016, vol.90, e40018.  Epub 26-Sep-2016. ISSN 2173-9110.

Background:

The use of medicines and medical supplies is a significant component of health expenditure, linked to healthcare quality and efficient resource allocation. This study aimed to evaluate three risk adjustment systems predictive power of the consumption of medicines and medical supplies at polyvalent hospitalization units (PHU).

Methods:

This is an observational, retrospective study of the resources utilization in PHU between 2010 and 2013. We fitted linear regression models and evaluated their goodness of fit for three different predictors: Charlson Comorbidity Index (CCI), All Patients DRG (AP-DRG) and All Patients Refined DRG (APR-DRG) relative weights, and each one of them corrected by the length of stay. We analyzed hospitalization episodes included in the Minimum Basic Data Set (MBDS) from Fuenlabrada University Hospital. Data about the use of medicines and medical supplies were obtained from pharmacy and supply chain management information systems.

Results:

Significant correlation was found between the annual consumption and the predictors considered (r=0.879 for CCI; r=0.622 for AP-DRG and r=0.514 for APR-DRG; p<0.01). The CCI corrected by length of stay was the variable that best fit presented (Ṝ2=0.863; p<0.001).

Conclusions:

The best predictive ability of CCI indicates that resource utilization depends more of the concurrent presence of additional pathology than the case mix calculated for iso-resource groups.

Palabras clave : Drug utilization; Equipment and supplies; Materials management; hospital; Inpatients; Risk adjustment; Nursing station; Comorbidity; Diagnosis related groups; Management information systems.

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