A neuro-fuzzy approach for identifying practice variations based on modelling relationships between clinical variables and treatment decisions

Variations in treatment decisions made by individual physicians can lead to practice variations in which different doctors may treat patients with the same type of medical conditions differently. There is a need to develop novel decision support systems that can interpret the decision-making behaviour of clinicians and identify the factors influencing particular treatment decisions. Such a tool will ensure that patients with the same medical conditions consistently receive the same kind of treatment and care. This paper proposes a novel neuro-fuzzy decision modelling approach; using neural networks for automatically determining the key clinical characteristics influencing a physician's treatment decisions, and using fuzzy classifiers to model the relationships between clinical characteristics and treatment decisions using linguistically interpretable fuzzy rules. The approach aims to help identify the factors and reasons for variations in treatment decisions made by different physicians in order to improve patient care. In order to demonstrate the usefulness of the proposed work, we conducted several quick and dirty ethnographic studies, which prove that variations in physicians' treatment exist.