Abstract
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.
Highlights
A healthcare service system has been well recognized as a self-organizing system (Rouse 2008; Lipsitz 2012)
By experimenting with the Autonomy-Oriented Computing (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
We present a study on applying AutonomyOriented Computing (AOC), an approach effective in modeling systems from a self-organizing systems perspective (Liu et al 2004), to understand the self-organized regularities relating to patient arrivals and wait times in cardiac surgery services in Ontario
Summary
A healthcare service system has been well recognized as a self-organizing system (Rouse 2008; Lipsitz 2012). Some selforganized regularities in wait times, such as the power-law distribution of variations in specialists’ waiting lists (i.e., the variations in the mean time that patients spend on specialists’ waiting lists) (Smethurst and Williams 2002), have been reported. It is still unclear what and how patients’ and hospitals’ behaviors with respect to underlying factors, such as distance from homes to services, hospital resourcefulness in terms of physician supply, and service performance as measured in wait times, account for such emergent regularities. They include, but are not limited to, the factors of demographics, socioeconomic backgrounds, environmental conditions, as well as the healthcare related behaviors of patients (Cardiac Care Network of Ontario 2005) and hospitals
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have