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Journal of Medical Systems
Published in: Journal of Medical Systems 4/2012

01-08-2012 | ORIGINAL PAPER

Early Warning System for Financially Distressed Hospitals Via Data Mining Application

Authors: Ali Serhan Koyuncugil, Nermin Ozgulbas

Published in: Journal of Medical Systems | Issue 4/2012

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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.
Literature
1.
Ozgulbas, N., and Koyuncugil, A. S., Financial early warning system for risk detection and prevention from financial crisis. In: Koyuncugil, A. S., and Ozgulbas, N. (Eds.), Surveillance technologies and early warning systems: Data mining applications for risk detection. Idea Group Inc, New York, pp. 76–108, 2010.CrossRef
2.
Koyuncugil, A. S., and Ozgulbas, N., Social aid fraud detection system and poverty map model suggestion based on data mining for social risk mitigation. In: Koyuncugil, A. S., and Ozgulbas, N. (Eds.), Surveillance technologies and early warning systems: Data mining applications for risk detection. Idea Group Inc, New York, pp. 173–193, 2010.CrossRef
3.
Ministry of Health (MoH), 2005 inpatient facilities statistical almanac of the ministry of health, Ankara, 2006
4.
Ministry of Health (MoH), 2006 inpatient facilities statistical almanac of the ministry of health, Ankara, 2007
5.
Ministry of Health (MoH), 2007 inpatient facilities statistical almanac of the ministry of health, Ankara, 2008
6.
Ministry of Health (MoH), 2008 inpatient facilities statistical almanac of the ministry of health, Ankara, 2009
7.
Kisa, A., The Turkish commercial health insurance industry. J. Med. Syst. 25:233–239, 2001.CrossRef
8.
World Bank (WB), Turkey reforming the health sector for improved access and efficiency, document of the world bank, Report No. 24358-TU, March 2003
9.
Giray, U. A., Health system in Turkey, Republic of Turkey ministry of health department of European Union Coordination: Ankara, 2003
10.
11.
12.
13.
State Planning Organization (SPO), Final report of Turkish master health plan. SPO, Ankara, Turkey, 1990.
14.
Ministry of Health (MoH), Reports of study groups. Ministry of Health, Ankara, Turkey, 1992.
15.
European Parliament’s Committee, General Overview of the Public Health Sector in Turkey, European Parliament’s Documents, No: IP/A/ENVI/NT/2006-312006
16.
Vassilou, L., and Tokat, M., Issues and options in health financing in Turkey. Document of World Bank, Report No: 8042-TU, 1990
17.
Ozcan, Y., and Ersoy, K., Efficiency of health care in The Republic of Turkey. TIMS, Alaska, p. XXXII, 1994.
18.
Kavuncubasi, S., and Ersoy, K., Measurement of technical efficiency in hospitals. Journal of Public Management, XXVII(3), 1995
19.
Ozgulbas, N., Measuring effectiveness of ministry of health hospitals’ by data envelopment analysis. J. Prod. 1:69–90, 2003.
20.
Ozgulbas, N., and Okem, G., The relationship between technical and financial performance at the ministry of health’s in Turkey. Global engagement in creating financially viable healthcare systems, Proceedings of Second International Healthcare Conference: 303–308, 2002
21.
Ozgulbas, N., and Kisa, A., Wasteful use of financial resources in public hospitals in Turkey: A trend analysis. Health Care Manager 25:144–149, 2005.
22.
Ozgulbas N., and Koyuncugil, A. S., Application of benchmarking as a strategy for increasing the financial performance, In Proceedings of 9th National Finance Symposium, Nevsehir, Turkey, Sep. 28–30, 2005
23.
Ozgulbas, N., and Koyuncugil, A. S., Financial profiling of public hospitals: An application by data mining. Int. J. Health Plann. Manage. 24(1):69–83, 2009.CrossRef
24.
Ministry of Finance (MoF), Public financial management and control law no. 5018, Published by Republic of Turkey Ministry of Finance Strategy Development Unit: Ankara, 95, 2010
25.
Warner, J., Bankruptcy costs: Some evidence. J. Finance 32:337–347, 1977.CrossRef
26.
27.
Beaver, W., Financial ratios as predictors of failure. J. Acc. Res. 4:71–111, 1966.CrossRef
28.
Altman, E., Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, September, pp. 589–609, 1968.
29.
Deakin, E. B., A discriminat analysis of predictors of business failure. J. Acc. Res. 10(1):167–179, 1972.CrossRef
30.
Altman, E. I., Haldeman, G., and Narayanan, P., Zeta analysis: A new model to identify bancrupcy risk of corporations. Journal of Banking and Finance, June, 29–54, 1977
31.
Taffler, R. J., and Tisshaw, H., Going, going, gone-four factors which factors which predict. Accountancy, March, pp. 50–54, 1977.
32.
Zmijewski, M. E., Methodological issues related to the estimation of financial distress prediction models, Journal of Accounting Research, Supplement: 59–82, 1984
33.
Zavgren, C., Assessing the vulnerability to failure of American industrial firms: A logistics analysis. J. Acc. Res. 22:59–82, 1985.
34.
Jones, F., Current techniques in bankruptcy prediction. J. Acc. Lit. 6:131–164, 1987.
35.
Pantalone, C., and Platt, M., Predicting failures of savings and loan associations. AREUEA J. 15:46–64, 1987.
36.
Meyer, P. A., and Pifer, H. W., Prediction of bank failures. J. Finance XXV(4):853–886, 1970.
37.
Brockett, P. L., and Cooper, W. W., Report to the state auditor and the state board of insurance on early warning systems to monitor the performance of insurance companies in Texas, Office of the State Auditor, Austin, TX., 1990
38.
Coyne, J., and Meadows, D., California HMOs may provide national forecast. Healthc. Financ. Manage. 45(5):34–39, 1991.
39.
Lee, S. H., and Urrutia, J. L., Analysis of insolvency prediction in the property-liability insurance industry: A comparison of logit and hazard models. J. Risk Insur. 63:121–130, 1996.CrossRef
40.
Barniv, R., and Hathorn, J., The merger or insolvency alternative in the insurance industry. J. Risk Insur. 64(1):89–113, 1997.CrossRef
41.
Trieschmann, J. S., and Pinches, G. E., A multivariate model for predicting financially distressed property-liability insurers. J. Risk Insur. 40:327–338, 1973.CrossRef
42.
Ambrose, J. M., and Seward, J. A., Best’s ratings financial ratios and prior probabilities in insolvency prediction. J. Risk Insur. 55:229–244, 1998.
43.
Barniv, R., and McDonald, J. B., Identifying financial distress in the insurance industry: A synthesis of methodological and empirical issues. J. Risk Insur. 59:543–573, 1992.CrossRef
44.
Laitinen, K., and Chong, H. G., Early warning system for crisis in SMEs: Preliminary evidence from Finland to the UK. J. Small Bus. Enterprise Dev. 6(1):89–102, 1999.CrossRef
45.
Yang, B., Ling, X. L., Hai, J., and Jing, X., An early warning system for loan risk assessment using artificial neural networks. Knowl.-Based Syst. 14(5–6):303–306, 2001.CrossRef
46.
Salas, V., and Saurina, J., Credit risk in two institutional regimes: Spanish commercial and savings banks. J. Financ. Serv. Res. 22(3):203–224, 2002.CrossRef
47.
Edison, H. J., Do indicators of financial crises work? An evaluation of an early warning system. Int. J. Financ. Econ. 8(1):11–53, 2003.CrossRef
48.
El-Shazly, A., Early warning of currency crises: An econometric analysis for Egypt. Middle East Bus. Econ. Rev. 18(1):34–48, 2003.
49.
Jacobs, L. J., and Kuper, G. H., Indicators of financial crises do work! An early-warning system for six Asian countries. CCSO Working Paper 13. Department of Economics, University of Groningen, the Netherlands, 2004
50.
51.
Price, C., Cameron, A. E., and Price, D. L., Distress detectors measures for predicting financial trouble in hospitals. Healthc. Financ. Manage. 59(8):74–80, 2005.
52.
Brockett, P. L., Golden, L. L., Jang, J., and Yang, C., A comparison of neural network, statistical methods and variable. J. Risk Insur. 73(3):397–419, 2006.CrossRef
53.
Abumustafa, N. I., Development of an early warning model for currency crises in emerging economies: An empirical study among Middle Eastern countries. Int. J. Manage. 23(3):403, 2006.
54.
Kyong, J. O, Tae, Y. K, Chiho, K., and Suk, J. L., Using neural networks to tune the fluctuation of daily financial condition indicator for financial crisis forecasting, Advances in Artificial Intelligence, doi:10.​1007/​11941439_​65 Volume 4304/2006
55.
Katz, M., Multivariable analysis: A practical guide for clinicians, New York: Churchill-Livingstone: 200, 2006
56.
Koyuncugil, A. S., and Ozgulbas, N., Developing financial early warning system via data mining, In Proceedings Book of 4th Congress of SMEs and Productivity, Istanbul: 153–166, 2007
57.
Koyuncugil, A. S., and Ozgulbas, N., Detecting financial early warning signs in Istanbul stock exchange by data mining. Int. J. Bus. Res. VII(3):188–193, 2007.
58.
Davis, E. P., and Karim, D., Comparing early warning systems for banking crises. J. Financ. Stability 4(2):89–120, 2008.CrossRef
59.
Davis, E. P., and Karim, D., Could early warning systems have helped to predict the sub-prime crisis? Natl Inst. Econ. Rev. 206(1):35–47, 2008.CrossRef
60.
Coyne, J. S., and Singh, S. G., The early indicators of financial failure: A study of bankrupt and solvent health systems. J. Healthc. Manage. 53(5):333–346, 2008.
61.
Koyuncugil, A. S., and Ozgulbas, N., Strengths and weaknesses of SMEs listed in ISE: A CHAID decision tree application, Journal of Dokuz Eylul University, Faculty of Economics and Administrative Sciences, 23(1): 1–22, 2008
62.
Koyuncugil, A. S., and Ozgulbas, N., Measuring and hedging operational risk by data mining. Proceedings Book of World Summit on Economic-Financial Crisis and International Business, Washington: 1–6, 2009
63.
Koyuncugil, A. S., and Ozgulbas, N., An intelligent financial early warning system model based on data mining for SMEs. In Proceedings of the International Conference on Future Computer and Communication, Kuala Lumpur, Malaysia. doi:10.​1109/​ICFCC.​2009.​118: 662–666, 2009
64.
Frawley, W., Piatetsky-Shapiro, G., and Matheus, C., Knowledge discovery in databases: An overview, AI Magazine: Fall: 213–28, 1992
65.
Hand, D., Mannila, H., and Smyth, P., Principles of data mining. MIT Press, Cambridge, p. 555, 2001.
66.
67.
Monk, E., and Wagner, B., Concepts in enterprise resource planning, 2nd edition. Thomson Course Technology, Boston, 2006.
68.
Tam, K. Y., and Kiang, M. Y., Managerial applications of neural networks: The case of bank failure predictions. Decis. Sci. 38:926–948, 1992.MATH
69.
Lee, K. C., Han, I., and Kwon, Y., Hybrid neural network models for bankruptcy predictions. Decis. Support Syst. 18:63–73, 1996.CrossRef
70.
Kumar, N., Krovi, R., and Rajagopalan, B., Financial decision support with hybrid genetic and neural based modeling tools. Eur. J. Oper. Res. 103:339–349, 1997.MATHCrossRef
71.
Nazem, S., and Shin, B., Data mining: New arsenal for strategic decision making. J. Database Manage. 10:39–42, 1999.
72.
Eklund, T., Back, B., Vanharanta, H., and Visa, A., Using the self- organizing map as a visualization tool in financial benchmarking. Inf. Vis. 2:171–181, 2003.CrossRef
73.
Hoppszallern, S., Healthcare benchmarking. Hosp. Health Netw. 77:37–44, 2003.
74.
Derby, B. L., Data mining for improper payments. J. Gov. Financ. Manage. 52:10–13, 2003.
75.
Chang, S., Chang, H., Lin, C., and Kao, S., The effect of organizational attributes on the adoption of data mining techniques in the financial service industry: An empirical study in Taiwan. Int. J. Manage. 20:497–503, 2003.
76.
Kloptchenko, A., Eklund, T., Karlsson, J., Back, B., Vanhatanta, H., and Visa, A., Combining data and text mining techniques for analyzing financial reports. Intell. Syst. Acc. Finance Manage. 12:29–41, 2004.CrossRef
77.
Magnusson, C., Arppe, A., Eklund, T., and Back, B., The language of quarterly reports as an indicator of change in the company’s financial status. Inf. Manage. 42:561–570, 2005.
78.
Koyuncugil, A. S., and Ozgulbas, N., Is there a specific measure for financial performance of SMEs. Bus. Rev. Camb. 5(2):314–319, 2006.
79.
Koyuncugil, A. S., and Ozgulbas, N., Financial profiling of SMEs: An application by data mining, In Proceedings of the European Applied Business Research (EABR) Conference, 2006
80.
Koyuncugil, A. S., and Ozgulbas, N., Determination of factors affected financial distress of SMEs listed in ISE by data mining, Proceedings of 3rd Congress of SMEs and Productivity, KOSGEB and Istanbul Kultur University, Istanbul, 2006
81.
Koyuncugil, A. S., and Ozgulbas, N., Early warning system approach to SMEs based on data mining as a financial risk detector. Hakikur Rahman (Ed), Data mining applications for empowering knowledge societies, Idea Group Inc., USA, 2008
82.
Ozgulbas, N., and Koyuncugil, A. S., Profiling and determining the strengths and weaknesses of SMEs listed in ISE by the data mining decision trees algorithm CHAID, Proceedings of 10th National Finance Symposium, Izmir, 2006
83.
Fayyad, G., Piatetsky-Shapiro, P., and Symth, P., From data mining to knowledge discovery in databases. AI Mag. 17(3):37–54, 1996.
84.
Koyuncugil, A. S., Fuzzy data mining and its application to capital markets. Unpublished Ph.D. Thesis, Ankara University, 2006
85.
Berson, A., Smith, S., and Thearling, K., Building data mining applications for CRM. McGraw-Hill, USA, p. 510, 2000.
86.
Kovalerchuk, B., and Vityaev, E., Data mining in finance. Kluwer Academic Publishers, Hingham MA USA, 2000.MATH
87.
SPSS, Answer Tree 3.0 user’s guide. SPSS Inc.: USA, 2001
88.
Cleverley, W. O., Improving financial performance: A study of 50 hospitals. Hosp. Health Serv. Adm. 35(2):173–187, 1990.
89.
Cleverley, W. O., Does hospital financial performance measure up? Healthc. Financ. Manage. 46(5):20–26, 1992.
90.
Cleverly, W. O., Understanding your hospital’s true financial position and changing it. Health Care Manage. Rev. 20(2):62–73, 1995.
91.
Cleverley, W. O., and Baserman, S. J., Pauerns of financing for the largest hospital systems in the United States. J. Healthc. Manage. 50(6):361–365, 2005.
92.
Volvana, J., and Sloan, F., Hospital profitability and capital structure: A comparative anaysis. Health Serv. Reserch 23(3):343–357, 1988.
Metadata
Title
Early Warning System for Financially Distressed Hospitals Via Data Mining Application
Authors
Ali Serhan Koyuncugil
Nermin Ozgulbas
Publication date
01-08-2012
Publisher
Springer US
Published in
Journal of Medical Systems / Issue 4/2012
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-011-9694-1

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