A computer simulation model of hospital mergers

Area of Science:

• Health economics
• Healthcare management
• Financial modeling

Background:

• Hospital mergers are complex financial undertakings.
• Accurate cost prediction is crucial for successful integration.
• Evaluating multiple merger scenarios requires robust modeling.

Purpose of the Study:

• To develop and apply a statistical cost-prediction model for a potential three-hospital merger.
• To simulate future costs under various merger options and unmerged scenarios.
• To provide data-driven insights for financial planning in healthcare consolidation.

Main Methods:

• Utilized a statistically based cost-prediction model.
• Adapted the model for different merger configurations.
• Employed stochastic simulation to forecast future costs for merged and unmerged hospitals.

Main Results:

• The study generated cost predictions based on the adapted statistical model.
• Stochastic simulation provided a range of potential future costs.
• Model outputs offer insights into the financial implications of different merger strategies.

Conclusions:

• The statistical and simulation models provide a framework for assessing merger costs.
• Findings support informed decision-making in hospital mergers.
• This approach enhances financial risk assessment in healthcare mergers.