Version 1
: Received: 1 July 2024 / Approved: 2 July 2024 / Online: 2 July 2024 (11:17:19 CEST)
How to cite:
Shanmugam, R.; Beauvais, B.; Dolezel, D.; Pradhan, R.; Ramamonjiarivelo, Z. The Probability of Hospital Bankruptcy: A Stochastic Approach. Preprints2024, 2024070224. https://doi.org/10.20944/preprints202407.0224.v1
Shanmugam, R.; Beauvais, B.; Dolezel, D.; Pradhan, R.; Ramamonjiarivelo, Z. The Probability of Hospital Bankruptcy: A Stochastic Approach. Preprints 2024, 2024070224. https://doi.org/10.20944/preprints202407.0224.v1
Shanmugam, R.; Beauvais, B.; Dolezel, D.; Pradhan, R.; Ramamonjiarivelo, Z. The Probability of Hospital Bankruptcy: A Stochastic Approach. Preprints2024, 2024070224. https://doi.org/10.20944/preprints202407.0224.v1
APA Style
Shanmugam, R., Beauvais, B., Dolezel, D., Pradhan, R., & Ramamonjiarivelo, Z. (2024). The Probability of Hospital Bankruptcy: A Stochastic Approach. Preprints. https://doi.org/10.20944/preprints202407.0224.v1
Chicago/Turabian Style
Shanmugam, R., Rohit Pradhan and Zo Ramamonjiarivelo. 2024 "The Probability of Hospital Bankruptcy: A Stochastic Approach" Preprints. https://doi.org/10.20944/preprints202407.0224.v1
Abstract
Healthcare leaders are faced with many financial challenges in the contemporary environment, leading to many bankruptcies in recent years. What is not well understood are the specific conditions that lead to organizational economic failure. Ideally, hospital leaders could examine the literature for guidance to predict bankruptcy before it occurs. However, existing regression methods have proven inadequate, especially when the finance variables follow a nonnormal frequency pattern. Furthermore, the regression approach encounters difficulties due to multicollinearity and nonnormality issues. Therefore, an alternate stochastic approach for predicting the probability of hospital bankruptcy is warranted. This new method includes computing and interpreting the correlations between the hospital’s revenue and expenses for bivariate lognormal data, calculating the risk of bankruptcy due to the mismatch between revenues and expenses, determining the likelihood of a hospital’s expense exceeding the state’s median expense level, and evaluating a hospital’s financial memory level. Ultimately in our research, we demonstrate a novel approach to anticipate hospital bankruptcy, which can be particularly useful for health policy analysts and hospital leaders in making informed financial decisions and proactively managing risks to ensure the sustainability and stability of their institutions.
Public Health and Healthcare, Public Health and Health Services
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
