Julianna Bernardi, Paul Ursino, Kevin Gormley, Colleen Rye, Matt Boyas, Sam Steckley
While the proposed theory-driven framework is broadly applicable beyond the specific medical use case, it relies on careful, domain-informed parameter curation to generate meaningful synthetic datasets. Its flexible, adjustable input settings enable researchers to tailor data generation to their precise methodological requirements, providing a controlled basis for simulation-based validation without implying direct clinical inference.
INTRODUCTION: Hospital closures and other manifestations of financial distress threaten timely access to care across the United States. Widely used, publicly available distress indicators have important limitations when applied across diverse hospital types and operating environments.
RESULTS: The IVM showed strong predictive power and sorted hospitals into meaningful risk groups. In the validation sample, 51% of hospitals were classified as low risk, 30% as mid-low risk, 11% as mid-high risk, and 8% as high risk. Higher-risk hospitals were substantially more likely than lower-risk hospitals to experience financial distress within two years.
CONCLUSION: The IVM provides a flexible, generalizable tool for early identification of vulnerable hospitals and for targeting technical assistance, oversight, or interventions to preserve local access to essential care.