6. Statistical analysis will be done as follows. Assuming that total completeness and correctness are two dependent variables, as they are bounded variables between zero and 1. Thus, a beta regression model with a logit link function will be used for the mean response model; a log link function will be fitted for the precision model. This model is based on the assumption that the dependent variable is beta-distributed and that its mean is related to a set of regressors through a linear predictor with unknown coefficients and a link function. The full model shall include three factors: (1) DSS mode (DSS and Non-DSS), (2) Level of training (e.g., 1st year resident, experienced resident, specialist), and (3) Scenario (as different scenarios will be used). The pseudo R2 value (squared correlation of linear predictor and link-transformed response) will be used to measure the overall goodness of fit of the model. A backward elimination algorithm will be used to assess what are the important factors. Unlike the total completeness measure, which was computed for the overall set of decision-points per scenario per clinician, the investigators will also analyze the completeness measure for each decision-point (e.g., "order lab test"), increasing resolution and looking at the clinician's guideline-based action per decision; therefore, the measured variable is binary, and a logistic regression with a logit link function will be used. As in the previous case, the full model will include three factors; a backward elimination algorithm will be used to reach the final model.