The clinical routine possess a growing repertoire of diagnostic tests. Clinical foundation models have recently emerged as powerful tools to extracting and integrate diagnostic information from diverse clinical tests, thereby enabling the creation of patient digital twins. However, it remains unclear which diagnostic tests to select and how to design them to ensure foundation models can extract sufficient information for accurate diagnosis. Here, we introduce MAVIS (Multi-modal Active VIew Selection), a reinforcement learning framework that unifies modality selection and feature selection into a single decision process. By leveraging foundation models, MAVIS dynamically determines which diagnostic tests to perform and in what sequence, adapting to individual patient characteristics. Experiments on real-world datasets across multiple clinical tasks demonstrate that MAVIS outperforms conventional approaches in both diagnostic accuracy and uncertainty reduction, while reducing testing costs by over 80\%, suggesting a promising direction for optimizing clinical workflows through intelligent test design and selection.