A data-rich agent-based decision support model for hospital consolidation

In the complex space of the healthcare system, we need tools that help decision makers to interactively and inexpensively eliminate infeasible approaches prior to executing expensive changes, and focus on only the most promising strategies. A data-rich agent-based decision support model in the context of hospital consolidation is introduced to address a variety of scenarios. By “rich,” we mean extensive rule sets and information sources: these include agents' financial statements, operational performance, as well as a detailed set of strategic objectives. An application of this model is to analyze hospitals' merger and acquisition activities in New York City based on overarching characteristics for the 5,000+ hospitals nationwide. The model is accompanied by a large-scale interactive visualization, where an array of seven 8′ × 20′, 180 degree touch-screen monitors are installed. This unique approach enables non-technically oriented stakeholders an immersive experience that enables asking many “what if” questions and greatly increases their comfort levels through evidence-based decision making. The purpose of this research is to integrate various information sources to facilitate strategic decision-making processes and understand path dependencies.