Concept Bottleneck Models (CMBs) are designed to ground image classification on human-understandable concepts, to make model decisions interpretable. Crucially, the CBM design also allows for interventions, giving users the ability to modify internal concept choices to intuitively influence the decision behavior of the model. However, existing approaches often require numerous human interventions per image to achieve strong performances, posing practical challenges in scenarios where obtaining human inputs is expensive. This is primarily due to the independent treatment of concepts during intervention, where a change of one concept does not influence the use of other ones in the model's final decision. To address this issue, we propose a simple concept correction technique to automatically realign concept assignments post-intervention by exploiting statistical relationships between them. In doing so, our approach improves intervention efficacy and raises both classification and concept prediction accuracy across various architectures and real-world datasets.In addition, it easily integrates into existing concept-based architectures without requiring changes to the models themselves. We anticipate that our method will reduce the cost of human-model collaboration, and enhance the feasibility of CBMs in resource-constrained environments.