All academic medical centers have a strong desire to maximize the value of their clinical data for secondary use purposes such as quality improvement (QI) and research. However, this need has not been adequately fulfilled due in part to the fact that the data capture functions in current electronic health record systems predominantly focus on clinical documentation and billing, lacking the flexibility to allow the collection of additional data elements critical to QI or research. To address this gap, we designed and developed a dynamic data platform to support clinicians' varied needs for recording additional data about their patients outside of direct patient care (e.g. classifying patient conditions based on the inclusion criteria of a research-oriented patient registry). In this paper, we describe the design considerations of this platform such as data models, query functions, coding and controlled vocabulary, user interface design, access control, and data interoperability. In developing the platform, we partnered with the frontline clinicians in an academic congenital heart canter, and adopted the agile software development approach with numerous rounds of evaluation and iterative refinement. Since 2013, this platform has been successfully used to meet the dynamic QI and research data needs of clinicians in the congenital heart center. Future work includes improving the efficiency and effectiveness of the platform and incorporating cutting-edge data interoperability standards.