This item is available under a Creative Commons License for non-commercial use only
Statistics, Health care sciences and services, Hospital administration, Business and Management.
Using original data that we have collected on referral relations between 110 hospitals serving a large regional community, we show how recently derived Bayesian exponential random graph models may be adopted to illuminate core empirical issues in research on relational coordination among healthcare organisations. We show how a rigorous Bayesian computation approach supports a fully probabilistic analytical framework that alleviates well-known problems in the estimation of model parameters of exponential random graph models. We also show how the main structural features of interhospital patient referral networks that prior studies have described can be reproduced with accuracy by specifying the system of local dependencies that produce – but at the same time are induced by – decentralised collaborative arrangements between hospitals.
Caimo, A., Pallotti, F. and Lomi, A. (2017), "Bayesian exponential random graph modelling of interhospital patient referral networks," Statistics in Medicine, 36(18), 2902 - 2920. DOI:10.1002/sim.7301