Hospital cost per case: analyses using a statewide data system

Area of Science:

• Health Services Research
• Hospital Management
• Health Economics

Background:

• Understanding hospital cost drivers is crucial for effective financial management and policy development.
• Previous research has identified various factors influencing healthcare expenditures, but a comprehensive model integrating complexity, severity, and hospital characteristics is needed.

Purpose of the Study:

• To establish relationships between hospital cost per case and key independent variables.
• To develop predictive models for hospital costs based on case mix, patient characteristics, and hospital factors.
• To inform strategies for identifying high-cost hospitals and for prospective reimbursement.

Main Methods:

• Utilized data from 216,000 discharges from Maryland's acute general hospitals.
• Employed 383 Diagnostic Related Groups (DRGs) to calculate an information theoretic measure of case mix complexity.
• Developed multiple linear regression equations to analyze cost predictors.

Main Results:

• Case mix complexity, patient age, proportion of high-risk patients, average length of stay, and nonphysician salary levels were significant predictors of cost per case.
• The developed models explained up to 88% of the variance in between-hospital cost per case.
• Distinct models were developed for metropolitan and rural hospitals, highlighting group-specific cost drivers.

Conclusions:

• Hospital cost per case is predictable using a combination of clinical and operational factors.
• Findings support the identification and regulation of unexpectedly high-cost hospitals.
• The study provides a foundation for prospective cost per case reimbursement models.