The identification of high-risk patients is a critical component in improving patient outcomes and managing costs. This paper describes the Intelligent Care Delivery Analytics platform (ICDA), a system which enables risk assessment analytics that process large collections of dynamic electronic medical data to identify at-risk patients. ICDA works by ingesting large volumes of data into a common data model, then orchestrating a collection of analytics that identify at-risk patients. It also provides an interactive environment through which users can access and review the analytics results. In addition, ICDA provides APIs via which analytics results can be retrieved to surface in external applications. A detailed review of ICDA's architecture is provided. Descriptions of four use cases are included to illustrate ICDA's application within two different data environments. These use cases showcase the system's flexibility and exemplify the types of analytics it enables.