The clustering of public hospitals for the productivity scorecard application

Identifying and Grouping the Roles of Hospitals on an Institutional Basis is one of the study of Ministry of Health. One of the consequences of the restructuring was the publication of Decree Law No. 663. In this context hospitals which are affiliated to Turkey Public Hospital Institution required to be evaluated in 6-month or annual period with this Legislative Decree. As required to this article for evaluation "The Productivity Scorecard Application" has started based on "The Balanced Institutive Scorecard Model". For each indicator, which is involved in "The Productivity Scorecard Application", acceptable values have been determined using different methods. In some indicator cards, acceptable value has been regarded as the average of the similar hospitals service classes. In the study presented in this thesis; it is focused on the study of k-Means clustering algorithm of data mining techniques and hospitals which are affiliated to Turkey Public Hospital Institution and clustering of hospitals whose productivity scorecard will be estimated. Hospitals have been clustered in terms of their financial status, equipment capacity, staff capacity and produced medical services' volume and variety. As a result of clustering work carried out within the scope of this study; 597 hospitals to be assessed by The Productivity Scorecard Application were divided into 16 clusters. 149 attributes have been determined for the hospital data used as input in the clustering algorithm and data has been collected from the data of year 2016. The validity of the clustering results was tested by taking expert opinions and also evaluated according to the distribution ratios of the hospital roles formed in the clusters. After these evaluations, it can be clearly stated that the clustering study has been found to be successful to substantially, taking into account the systematic gathering of data from the application resources and the detection of right outliers. Keywords: Hospital clustering, k-means clustering algorithm, bisecting k-means, productivity scorecard application.