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Primary care systems are a cornerstone of universally accessible health care. The planning, analysis, and adaptation of primary care systems is a highly non-trivial problem due to the systems’ inherent complexity, unforeseen future events, and scarcity of data. To support the search for solutions, this paper introduces the hybrid agent-based simulation model SiM-Care. SiM-Care models and tracks the micro-interactions of patients and primary care physicians on an individual level. At the same time, it models the progression of time via the discrete-event paradigm. Thereby, it enables modelers to analyze multiple key indicators such as patient waiting times and physician utilization to assess and compare primary care systems. Moreover, SiM-Care can evaluate changes in the infrastructure, patient behavior, and service design. To showcase SiM-Care and its validation through expert input and empirical data, we present a case study for a primary care system in Germany. Specifically, we study the immanent implications of demographic change on rural primary care and investigate the effects of an aging population and a decrease in the number of physicians, as well as their combined effects.

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The multi‐budgeted matching problem (mBM) is a weighted matching problem with k independent edge cost functions. For each cost function, a budget constraint requires the accumulated cost not to exceed a corresponding budget. We show that the mBM is strongly NP‐hard on paths with uniform edge weights and budgets by a reduction from 3‐SAT. Subsequently, we propose a dynamic program for series‐parallel graphs with pseudo‐polynomial run time for a fixed number of budget constraints. As an extension we show how this algorithm can be used to solve the mBM on trees using a graph transformation. Realizing that both these graph classes have a bounded treewidth in common, we introduce a dynamic program based on tree decompositions. This approach leads to a pseudo‐polynomial algorithm for the mBM with fixed k on graphs of bounded treewidth.