Precision medicine involves a paradigm shift toward personalized data-driven clinical decisions. The concept of a medical "digital twin" has recently become popular to designate digital representations of patients as a support for a wide range of data science applications. However, the concept is ambiguous when it comes to practical implementations. Here, we propose a medical digital twin framework with a data-centric approach. We argue that a single digital representation of patients cannot support all the data uses of digital twins for technical and regulatory reasons. Instead, we propose a data architecture leveraging three main families of digital representations: (1) multimodal dashboards integrating various raw health records at points of care to assist with perception and documentation, (2) virtual patients, which provide nonsensitive data for collective secondary uses, and (3) individual predictions that support clinical decisions. For a given patient, multiple digital representations may be generated according to the different clinical pathways the patient goes through, each tailored to balance the trade-offs associated with the respective intended uses. Therefore, our proposed framework conceives the medical digital twin as a data architecture leveraging several digital representations of patients along clinical pathways.