Governance-Driven Emergence in Healthcare System-of-Systems: Insights from Agent-Based Modeling

The U.S. healthcare delivery infrastructure functions as a complex socio-technical system-of-systems (SoS). In this environment, autonomous, interdependent entities (providers, insurers, and IT networks) must balance local efficiency with system-wide coordination. Existing governance mechanisms frequently fail to prevent operational fragmentation, yet the conditions under which governance pressure generates or inhibits coordination remain poorly understood. This study introduces a three-parameter cost framework. The framework comprises default operational costs, incoherence penalties for structural misalignment, and switching costs associated with modernization. This framework enables the examination of how governance structures shape emergent alignment in healthcare SoS. The three cost components are grounded in the Confluence Interoperability Covenant (CIC), a governance framework that codifies standards for interaction among autonomous healthcare systems and extends it into a computationally tractable form for simulation-based analysis. The contribution lies in jointly operationalizing these three cost components interacting with each other within a healthcare SoS governance framework using agent-based modeling and simulation (ABMS) and examining how their interaction shapes coordination under decentralized, periodic adaptation. Using a stylized exploratory ABMS framework implemented on a network, we analyze how these parameters influence coordination dynamics across a range of simulated governance conditions. The results reveal three emergent patterns. First, higher incoherence costs accelerate the transition from fragmented legacy configurations toward greater alignment. Second, high switching costs can create lock-in dynamics in which inefficient arrangements persist because transition burdens suppress adaptation. Third, the relationship between governance pressure and speed of coordination is non-monotonic: at early review horizons, moderate incoherence costs can produce faster coordination than maximal pressure, whereas stronger pressure eventually produces lower entropy as the simulation progresses. This finding reflects a transient finite-horizon coordination advantage rather than a fixed optimal governance level. This last finding suggests that governance effectiveness is horizon-dependent rather than a simple linear function of regulatory intensity. These findings contribute to a complexity-informed understanding of healthcare governance by showing that alignment in adaptive SoS depends not only on the intensity of interoperability pressure but on its interaction with institutional transition burdens. The model is designed as a mechanism-exploration framework to support future empirically grounded policy analysis and data collection.