Agent-based modeling (ABM) is a modeling and simulation paradigm well-suited to social systems where agents interact and have some degree of autonomy. In their most basic sense, ABMs consist of agents (generally, individuals) interacting in an environment according to a set of behavioural rules. The foundational premise and the conceptual depth of ABM is that simple rules of individual behaviour will aggregate to illuminate complex and/or emergent group-level phenomena that are not specifically encoded by the modeler and that cannot be predicted or explained by the agent-level rules. In essence, ABM has the potential to reveal a whole that is greater than the sum of its parts. In this thesis, ABMs have been utilized as a modeling framework for three specific healthcare applications, including: • the development of an ABM of an emergency department within a hospital allowing the modeling of contact-based infectious diseases such as influenza, and simulating various mitigation strategies; • the development of an ABM to model the effectiveness of a real-time location system (RTLS) using radio frequency identification (RFID) in an emergency department, used for patient tracking as one measure of hospital efficiency; and, • the development of an ABM to test strategies for disaster preparedness (high volume, high risk patients) using a fictitious case of zombies in an emergency department. Although each ABM was purposeful and meaningful for its custom application, each ABM also represented an iteration toward the development of a generic ABM framework. Finally, a thorough critique of ABMs and the modifications required to create a more robust framework are provided.