Research Alert: November 24, 2025
Rural Hospital Financial Distress Index: Relative Risk in 2025
The Financial Distress Index (FDI) uses historical data about hospital financial performance, government reimbursement, organizational characteristics, and market characteristics to predict the probability of rural hospital financial distress within two years. The model assigns every rural hospital to one of four financial risk categories: highest, mid-highest, mid-lowest, or lowest. This infographic shows a U.S. map of the number and percent of rural hospitals within each state at highest relative financial distress risk in 2025.
Key Takeaways:
- Different tools use different criteria, so “highest risk” can mean different things depending on the measurement. In our model, even hospitals in the highest risk category are still more likely to remain open than to close in any given year. Historically, just over 3 percent of hospitals in our highest risk group have closed in a given year.
- Many states have at least one hospital in the highest level of distress. As has been the case for the last two decades, hospitals in the South are more likely to be in distress. The states with the highest number of hospitals at highest relative risk of financial distress are Texas (13 hospitals at highest relative risk), Alabama (9), Oklahoma (8), and Tennessee (8). The states with the highest percentage of rural hospitals at highest relative risk of financial distress are Alabama (19.6%), Tennessee (16.7%), Virginia (12.9%) and Oklahoma (12.3%).
Tyler Malone, PhD
North Carolina Rural Health Research and Policy Analysis Center
Phone: 919.996.9484
tmalone@email.unc.edu
Additional Resources of Interest:
- Updated Model of Rural Hospital Financial Distress
- Using the Updated Financial Distress Index to Describe Relative Risk of Hospital Financial Distress
- More FORHP-funded research on Hospitals and clinics
- More information about the North Carolina Rural Health Research and Policy Analysis Center
- More information about the Rapid Response to Requests for Rural Data Analysis