Changing Health Policy Practices and Evaluation of Specialist Physicians Towards City Hospitals

Abstract

Strategic changes and policy implementation have a significant impact on health and health-related issues. The motivation of this study is to evaluate the opinions of specialist physicians towards city hospitals, which is a new and controversial policy action, and to analyze the findings obtained from these opinions by using various classification and machine learning methods. In order to evaluate their views on city hospitals, specialist physicians were divided into three groups using hierarchical clustering method in terms of health service quality and efficiency, coordination of care components, interdisciplinary care teams, and integration of health services dimensions. The differences between these groups were found to be statistically significant in terms of four dimensions (p < 0.0001). Naïve Bayes (AUC=0.896, F1=0.757), one of the machine learning techniques used to predict clusters obtained from four dimensions obtained from the evaluations of specialist physicians, was found to be the best predictor of four-dimensional classroom evaluations.