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Estimating hospital inefficiency: does case mix matter?

M D Rosko1, J A Chilingerian

  • 1Graduate Program in Health and Medical Services Administration, Widener University, Chester, Pennsylvania 19013, USA.

Journal of Medical Systems
|May 13, 1999
PubMed
Summary

Hospital efficiency is influenced by various factors. Analyzing 195 Pennsylvania hospitals revealed that the Diagnosis-Related Group (DRG) case mix index significantly reduced inefficiency, while severity of illness had a minor impact.

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Area of Science:

  • Health Economics
  • Operations Research
  • Healthcare Management

Background:

  • Hospital efficiency is a critical concern in healthcare.
  • Understanding factors influencing hospital inefficiency is essential for resource allocation and quality improvement.
  • Previous studies often lacked detailed output measures like patient-level severity of illness.

Purpose of the Study:

  • To analyze factors affecting hospital efficiency using a two-stage stochastic frontier analysis.
  • To estimate inefficiency scores and identify key drivers of X-inefficiency in acute care hospitals.
  • To test hypotheses derived from X-inefficiency Theory in the context of hospital operations.

Main Methods:

  • A two-stage stochastic frontier analysis (SFA) was employed.

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  • The first stage utilized a translog cost-function to estimate hospital inefficiency scores.
  • The second stage regressed inefficiency scores against independent variables, including the Diagnosis-Related Group (DRG) case mix index (CMI) and severity of illness data.

    • Main Results:

    • Estimated mean inefficiency scores ranged from 0.075 to 0.180.
    • The DRG case mix index (CMI) was found to reduce estimated inefficiency by over 50%.
    • The inclusion of a severity of illness variable showed a minimal incremental effect when CMI was already included.

    Conclusions:

    • Hospital inefficiency is inversely associated with regulatory pressures and industry concentration.
    • The DRG case mix index is a significant factor in reducing hospital inefficiency.
    • Patient-level severity of illness data, while valuable, had a limited impact on inefficiency estimates when controlling for CMI.