The Probability of Hospital Bankruptcy: A Stochastic Approach

Ramalingam Shanmugam (), Brad Beauvais, Diane Dolezel, Rohit Pradhan and Zo Ramamonjiarivelo
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Ramalingam Shanmugam: School of Health Administration, Texas State University, 601 University Drive, San Marcos, TX 78666, USA
Brad Beauvais: School of Health Administration, Texas State University, 601 University Drive, San Marcos, TX 78666, USA
Diane Dolezel: Department of Health Informatics and Information Management, Texas State University, 100 Bobcat Way, Round Rock, TX 78665, USA
Rohit Pradhan: School of Health Administration, Texas State University, 601 University Drive, San Marcos, TX 78666, USA
Zo Ramamonjiarivelo: School of Health Administration, Texas State University, 601 University Drive, San Marcos, TX 78666, USA

IJFS, 2024, vol. 12, issue 3, 1-23

Abstract: Healthcare leaders are faced with many financial challenges in the contemporary environment, leading to financial distress and notable instances of bankruptcies in recent years. What is not well understood are the specific conditions that may lead to organizational economic failure. Though there are various models that predict financial distress, existing regression methods may be inadequate, especially when the finance variables follow a nonnormal frequency pattern. Furthermore, the regression approach encounters difficulties due to multicollinearity. Therefore, an alternate stochastic approach for predicting the probability of hospital bankruptcy is needed. The new method we propose involves several key steps to better assess financial health in hospitals. First, we compute and interpret the relationship between the hospital’s revenues and expenses for bivariate lognormal data. Next, we estimate the risk of bankruptcy due to the mismatch between revenues and expenses. We also determine the likelihood of a hospital’s expenses exceeding the state’s median expenses level. Lastly, we evaluate the hospital’s financial memory level to understand its level of financial stability. We believe that our novel approach to anticipating hospital bankruptcy may be useful for both hospital leaders and policymakers in making informed decisions and proactively managing risks to ensure the sustainability and stability of their institutions.

Keywords: bivariate lognormal frequency structure; expected elasticity; hazard levels; correlation; survival function; hospital bankruptcy (search for similar items in EconPapers)
JEL-codes: F2 F3 F41 F42 G1 G2 G3 (search for similar items in EconPapers)
Date: 2024
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