Early warning system for financially distressed hospitals via data mining application.

The aim of this study is to develop a Financial Early Warning System (FEWS) for hospitals by using data mining. A data mining method, Chi-Square Automatic Interaction Detector (CHAID) decision tree algorithm, was used in the study for financial profiling and developing FEWS. The study was conducted in Turkish Ministry of Health's public hospitals which were in financial distress and in need of urgent solutions for financial issues. 839 hospitals were covered and financial data of the year 2008 was obtained from Ministry of Health. As a result of the study, it was determined that 28 hospitals (3.34%) had good financial performance, and 811 hospitals (96.66%) had poor financial performance. According to FEWS, the covered hospitals were categorized into 11 different financial risk profiles, and it was found that 6 variables affected financial risk of hospitals. According to the profiles of hospitals in financial distress, one early warning signal was detected and financial road map was developed for risk mitigation.

Detecting road maps for capacity utilization decisions by Clustering Analysis and CHAID Decision Trees.

The aims of this study are to provide a standard CUR value, to determine financial and organizational factors which affect the capacity utilization and develop road maps for increasing capacity utilization. To reach these aims by an objective method, we used data mining method that discovers hidden and useful pattern in a large amount of data. Two different method of data mining were used in two stages for this study. In first step, standard value of CUR was determined by K-means Clustering Analysis. CHAID Decision Tree Algorithm as a second method was implemented for determination of impact factors that provided steps for road maps. The study was concerned Turkish Ministry of Health public hospitals. 592 hospitals were covered and financial and operational data of the year 2004 were used in the study. Finally two different road maps were developed and suggestions were made according the results of the study.

Donor research and matching system based on data mining in organ transplantation.

It is very important to identify the appropriate donor in organ transplantation under the time constraint. Clearly, adequate time must be spent in appropriate donor research in that kind of vital operation. On the other hand, time is very important to search for other alternatives in case of inappropriate donor. However, the possibility for determining the most probable donors as fast as possible has an great importance in using time efficiently. From this point view, the main objective of this paper is developing a system which provides probabilistic prior information in donor transplantation via data mining. While the sytem development process, the basic element is the data of successful organ transplantations. Then, the hidden information and patterns will be discovered from this data. Therefore, this process requires the data mining methods from its definition. In this study, an appropriate donor detection system design based on data mining is suggested.

Financial profiling of public hospitals: an application by data mining.

This paper presents an application of the data mining method to determine the financial profiles of the public hospitals in Turkey. The study is based on the data compiled in 2004, covering 645 public hospitals run by the Ministry of Health (MoH) as the main provider of primary and secondary health services in Turkey. The public hospitals, currently financed by a mixture of funds allocated from the general budget and individually operated revolving funds, need urgent solutions to their financial problems as a part of an ongoing national reform effort. The analysis adopts the Chi-Square Automatic Interaction Detector (CHAID) decision tree algorithm, as one of the most efficient and up-to-date data mining method used for segmentation. The study has found that the public hospitals could be categorized by the CHAID into 12 different profiles in terms of their financial performance. These profiles have guided us in determining the key financial indicators to be focused upon in the public hospitals and present best practices to improve their respective financial performances. The findings have also allowed policy suggestions as to the financial strategies that may be considered in improving the financial performance of the public hospitals toward a successful health sector reform in Turkey.