Chart curation is a major bottleneck for oncology clinical practice and research. Existing automated approaches are insufficient to address this bottleneck as important patient data are stored as natural language rather than structured data. Current tools for automated processing of natural language remain inaccurate, require substantial human oversight, and lack oncology-specific knowledge. DAGCAP is a modular, interoperable, comprehensive end-to-end workflow aimed at decreasing chart curation effort by 90% while maintaining human-level accuracy. Users will be able to precisely define data variables using a guided, interactive process, and these variables will be mapped to commonly used data models as well as those defined or imported by users. The proposed research spans 1) Tool Features - building out the software components that will enable drastic improvement in chart curation capabilities; 2) Data Types & Semantics - introduce and train DAGCAP to understand and map between a variety of data types, data models, and enable data analysis and use of downstream tools; and 3) Use Cases - representative of real-world problems that we will use to develop, test, and improve DAGCAP.