In this paper, we apply both agent-based models and queuing models to investigate patient access and patient flow\r\nthrough emergency departments. The objective of this work is to gain insights into the comparative contributions and\r\nlimitations of these complementary techniques, in their ability to contribute empirical input into healthcare policy and\r\npractice guidelines. The models were developed independently, with a view to compare their suitability to emergency\r\ndepartment simulation. The current models implement relatively simple general scenarios, and rely on a combination of\r\nsimulated and real data to simulate patient flow in a single emergency department or in multiple interacting emergency\r\ndepartments. In addition, several concepts from telecommunications engineering are translated into this modeling context.\r\nThe framework of multiple-priority queue systems and the genetic programming paradigm of evolutionary machine\r\nlearning are applied as a means of forecasting patient wait times and as a means of evolving healthcare policy, respectively.\r\nThe modelsââ?¬â?¢ utility lies in their ability to provide qualitative insights into the relative sensitivities and impacts of model input\r\nparameters, to illuminate scenarios worthy of more complex investigation, and to iteratively validate the models as they\r\ncontinue to be refined and extended. The paper discusses future efforts to refine, extend, and validate the models with\r\nmore data and real data relative to physical (spatialââ?¬â??topographical) and social inputs (staffing, patient care models, etc.).\r\nReal data obtained through proximity location and tracking system technologies is one example discussed.
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