Modelos de previsão de recursos para antimicrobianos no Hospital Universitário de Santa Maria

Abstract

The scarce resources of public health makes the administrator manage the destination of resources, aiming to rationalize and optimize its collection, in order to improve the assistance to patients because the hospital is a public institution and does not get profits but promotes the community well-being. Thus, the hospital infection is acquired after the patient comes to the hospital of after he goes home and might be associated with his staying in hospital or with hospital procedures. This cost must be avoided. Once the complete eradication is not impossible, it is necessary to analyze and to control the monthly cost of the main antibiotics used for its treatment so that there is enough knowledge to foresee the resource collection to buy them. In this context, the main reason of this research is to carry out a forecast of the monthly cost and of the resource collection needed to purchase those medicine used in the treatment of hospital infections at the University Hospital of Santa Maria. To do so, a methodology for forecast by dynamic and multiple linear regressions was used. They were combined with to a multivariate technique by principal components. The technique of principal components was used to eliminate the multiple linearity existing among the original variants so, the resulting principal components were used as variables in the construction of the model of multiple linear regression and of dynamic regression. Therefore, these methodologies are applied to a case study of public health, in order to foresee and to conclude about which model is more suitable to forecast the monthly cost of antibiotics in hospital infections. The results obtained from the two models were considered satisfactory but the model of dynamic regression was chosen to be more suitable because it presented a mean absolute percentage error (MAPE). Finally, the findings might be a managerial tool for hospital administration when they offer subsides for the budget of planning and of the resource finances, especially in a time when resources are globally scarce, making health even more expensive.