Recently hospitals struggle to control the cost of care while maintaining optimal outcomes. To respond to this challenge, we developed an interactive web platform which utilizes a multiple linear regression model. The user can create and furthermore alter a clinical scenario, during a patient hospitalization to see the dynamic prediction of total charges, via interactive sessions. The R2 value of our model is 0.655 and the standard error of the estimate is 38,732. Predictors with high coefficient scores include the cardioverter implantation, mechanical ventilation, implant of pulsation balloon and hospital-acquired conditions such as staphylococcus aureus septicemia. Our findings indicate that (a) integration of predictive models into clinical decision support systems is feasible and use of regression methods provide direct feedback on the effect of any clinical practice to the in-hospital charges (b) medical claims data can provide a useful estimation of the in-hospital charges (c) hospital acquired conditions have significant impact on the in-hospital charges.