Predicting Financial Failure of Public Teaching Hospitals: An Application of Fuzzy Clustering Analysis

Özet

Purpose – To develop a novel prediction model and determine the variables that best predict hospitals’ financial failures. Design/methodology/approach – A fuzzy C-means clustering analysis was conducted using financial ratios from MoH Hospitals’ financial databases. Hospital Fuzzy Financial Health Scores (HF-HFS) then were calculated the as the degree of financial failure measured through the selected n-cluster model. Findings – The results show that the number of teaching hospitals experiencing financial failure has increased. HF-FHS scores also were compared with the modified Altman Z scores a commonly used financial distress measure in the hospital service sector. The HF-FHS scores in good and poor financial conditions differ significantly. The HF-FHS scores were also strongly correlated with the Altman Z scores. Discussion – The rising trend in the number of hospitals experiencing financial difficulties over the years may cause some hospitals in financial distress to fail to fulfill their obligations, which may disrupt the services provided. In such a situation, good management skills, which are the most important factor reducing the financial risks of hospitals, will not work after a certain point. Conclusions – The findings indicate Fuzzy C Means clustering as a viable option to evaluate the financial failure of hospitals compared to more traditional methods such as Altman Z scores. Hospitals still face financial pressures due to market and structural factors such as the global budget repayment system, pricing and collection time, and insufficient competition conditions. Predicting hospitals’ financial failures can help develop managerial policies and strategies to recognize and combat risks, improve performance, and improve the current situation of hospitals.