Department of Research, Capital Markets Board of Turkey, Ankara, Turkey.
J Med Syst. 2012 Aug;36(4):2271-87. doi: 10.1007/s10916-011-9694-1. Epub 2011 Apr 20.
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.
本研究旨在通过数据挖掘为医院开发财务预警系统（FEWS）。在研究中，使用了数据挖掘方法——卡方自动交互检测（CHAID）决策树算法，用于财务分析和开发 FEWS。该研究在土耳其卫生部陷入财务困境并需要紧急解决财务问题的公立医院中进行。研究涵盖了 839 家医院，并从卫生部获得了 2008 年的财务数据。研究结果表明，28 家医院（3.34%）的财务业绩良好，811 家医院（96.66%）的财务业绩不佳。根据 FEWS，所涵盖的医院被分为 11 种不同的财务风险状况，并且发现 6 个变量影响医院的财务风险。根据处于财务困境的医院的状况，检测到一个预警信号，并为降低风险制定了财务路线图。
