We address several challenges for applying statistical dialog managers based on Partially Observable Markov Models to real world problems: to deal with large numbers of concepts, we use individual POMDP policies for each concept. To control the use of the concept policies, the dialog manager uses explicit task structures. The POMDP policies model the confusability of concepts at the value level. In contrast to previous work, we use explicit confusability statistics including confidence scores based on real world data in the POMDP models. Since data sparseness becomes a key issue for estimating these probabilities, we introduce a form of smoothing the observation probabilities that maintains the overall concept error rate. We evaluated three POMDP-based dialog systems and a rule-based one in a phone-based user evaluation in a tourist domain. The results show that a POMDP that uses confidence scores, in combination with an improved SLU module, achieves the highest concept precision.