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Early Warning System for Financially Distressed Hospitals Via Data Mining Application

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Abstract

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.

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Authors and Affiliations

  1. Department of Research, Capital Markets Board of Turkey, Eskisehir Yolu 8. km., No: 156, Balgat, 06530, Ankara, Turkey

    Ali Serhan Koyuncugil

  2. Department of Healthcare Management, Faculty of Health Sciences, Baskent University, Eskisehir Yolu 20. km, 06810, Ankara, Turkey

    Nermin Ozgulbas

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  1. Ali Serhan Koyuncugil
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Correspondence to Ali Serhan Koyuncugil.

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Koyuncugil, A.S., Ozgulbas, N. Early Warning System for Financially Distressed Hospitals Via Data Mining Application. J Med Syst 36, 2271–2287 (2012). https://doi.org/10.1007/s10916-011-9694-1

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