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The Predictive Factors of Hospital Bankruptcy-An Exploratory Study.

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Area of Science:

  • Healthcare Management
  • Health Economics
  • Data Science in Healthcare

Background:

  • The US healthcare industry faces increasing hospital bankruptcies, affecting community access, cost, and quality of care.
  • Limited research exists on predictive financial models for hospital bankruptcy.
  • Understanding factors influencing bankruptcy is crucial for healthcare leaders.

Purpose of the Study:

  • To identify contemporary structural and operational factors influencing hospital bankruptcy.
  • To develop predictive models for hospital financial distress.
  • To guide healthcare leaders in avoiding future bankruptcy.

Main Methods:

  • Cross-sectional analysis of 3121 short-term acute care hospitals in the US.
  • Development of three predictive models: logistic regression, linear support vector machine (SVM), and perceptron neural network.
  • Utilized data from Definitive Healthcare and Becker's Hospital Review 2019, with 32 variables and 27 observed bankruptcies.

Main Results:

  • 18 variables were consistently identified by all three models as significant predictors of hospital bankruptcy.
  • The models provide a quantitative basis for understanding bankruptcy risk factors.
  • The study highlights specific organizational and operational elements linked to financial failure.

Conclusions:

  • Healthcare institutions can leverage knowledge of predictive factors to build stronger financial structures.
  • Proactive measures based on identified risk factors can help avoid financial distress.
  • Ensuring long-term financial viability is essential for sustained healthcare provision.