Applying Altman's Z-Score Model of bankruptcy for the prediction of financial distress of rural hospitals in western Pennsylvania

This study has examined a sample of 65 rural hospitals to determine whether applying Altman's Z-Score Model with modified financial ratios of liquidity, profitability, efficiency and productivity will yield significant prediction power for rural hospitals' financial status as it does in non-manufacturing corporations. The statistical conclusions of this study show that both Discriminate Analysis and Logistic Regression models are able to predict rural hospitals' financial status as to whether they are going to succeed or fail and close.; The Logistic Regression model has shown higher prediction power than that of Discriminate Analysis. It has correctly classified 100% of both closed (failed) and open (successful) hospitals for a three-year time span, while Discriminate analysis has correctly classified 90.2% of the same hospitals.; The study proves that all the four financial ratios used in this study are good predictors of financial performance. Liquidity and profitability ratios have the highest contribution to the results of Z-Scores, followed by productivity and efficiency.; The study also has many implications for decision makers. Decision makers have to develop an efficient collection system, set regular operational and financial plans that include reasonable profit margin, and devise alternative plans to avoid technical insolvency by using the four financial ratios as indicators of financial status to serve as warning signs for corrective actions. Decision makers also have to lobby state representatives and political parties to help rural hospitals get fair compensation for the services that they provide and expedite the time span of payments.; Further studies are recommended. These future studies may very well support my findings and enhance my model's reliability. For example, other studies may apply the statistical models used in this study on a larger sample of hospitals, and add more ratios to gain a higher prediction power. Applying the same models on other service organizations such as urban hospitals may further support my general conclusions, or may lead to the development of different models. In addition, new research may implement procedures similar to mine on for-profit hospitals in order to investigate the differences between both businesses, and make a sound comparison between them.