Department of Computer Science
Understanding self-organized regularities in healthcare services based on autonomy oriented modeling
© 2015, The Author(s). Self-organized regularities in terms of patient arrivals and wait times have been discovered in real-world healthcare services. What remains to be a challenge is how to characterize those regularities by taking into account the underlying patients’ or hospitals’ behaviors with respect to various impact factors. This paper presents a case study to address such a challenge. Specifically, it models and simulates the cardiac surgery services in Ontario, Canada, based on the methodology of Autonomy-Oriented Computing (AOC). The developed AOC-based cardiac surgery service model (AOC-CSS model) pays a special attention to how individuals’ (e.g., patients and hospitals) behaviors and interactions with respect to some key factors (i.e., geographic accessibility to services, hospital resourcefulness, and wait times) affect the dynamics and relevant patterns of patient arrivals and wait times. By experimenting with the AOC-CSS model, we observe that certain regularities in patient arrivals and wait times emerge from the simulation, which are similar to those discovered from the real world. It reveals that patients’ hospital-selection behaviors, hospitals’ service-adjustment behaviors, and their interactions via wait times may potentially account for the self-organized regularities of wait times in cardiac surgery services.
Autonomy-Oriented Computing (AOC), Cardiac surgery services, Complex systems, Patient arrivals, Self-organized regularities, Wait times
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Tao, L., & Liu, J. (2015). Understanding self-organized regularities in healthcare services based on autonomy oriented modeling. Natural Computing, 14 (1), 7-24. https://doi.org/10.1007/s11047-014-9472-3