Modelling of Practice Sharing in Complex Distributed Healthcare System

This research investigates how collectives of doctors influence their diagnostic method preferences within small-world network social structures through participation in diverse types of medical practice-sharing activities across different scales. We propose an approach based on vectorization of the preferences for various diagnostic methods among physicians, quantifying their openness to these methods using the Shannon diversity index. Utilizing theoretical foundations from threshold models, influence models, and the Hegselmann-Krause model, we designed simulation experiments for teaching activities and seminars to explore the dynamic changes in preference vectors and Shannon diversity indices among these doctors in a small-world network. We evaluated our approach with a real-world data set on vertigo treatment by several clinical specialists of different specialty (neurologists, otolaryngologist). Building on real data from this initial group, we then simulated data for a large number of doctors from various medical communities to examine phenomena in larger-scale systems . Hierarchical networks featuring small-world properties were developed to simulate “local” within-community and “global” across-community seminars, reflecting different intra- and inter-community scenarios. The experiments show different patterns of practice converging during simulation in various scales and scenarios. The findings of this study provide significant insights for further research into practice-based knowledge sharing among healthcare professionals, highlighting the nuanced interplay between social network structures and professional consensus formation.