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Refining case-mix adjustment. The research evidence.

S F Jencks, A Dobson

The New England Journal of Medicine

|September 10, 1987

Summary

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Case-mix adjustment in healthcare aims to compare hospital costs and outcomes by accounting for patient differences. Current Medicare payment systems may improve accuracy without solely relying on better severity of illness measures.

Area of Science:

Health Services Research
Healthcare Economics
Medical Informatics

Background:

Case-mix adjustment is crucial for comparing hospital costs and outcomes by standardizing for patient variations.
The Medicare payment system heavily relies on case-mix adjustment, primarily through diagnosis-related groups (DRGs).

Purpose of the Study:

To review case-mix adjustment methodologies and their application within the Medicare payment system.
To identify potential improvements in payment accuracy, particularly concerning outlier payments, independent of enhanced severity of illness measurement.
To assess the impact of improved severity of illness measures on Medicare payment accuracy and distribution.

Main Methods:

Review of existing literature and analysis of the Medicare payment system's reliance on case-mix adjustment.

Related Experiment Videos

Examination of areas within the payment system, such as outlier payments, for potential accuracy enhancements.

Evaluation of the evidence for and against improved severity of illness measures significantly altering payment distributions.

Main Results:

No current severity of illness measure is expected to substantially improve Medicare payment accuracy if added to or replacing diagnosis-related groups.
Evidence is mixed on whether better severity measurement would significantly alter payment distribution across hospitals.
Significant variation in treatment intensity exists within the same diagnosis-related group, often unrelated to patient severity of illness.

Conclusions:

Improving payment accuracy may be achievable through means other than solely enhancing severity of illness measurement.
While demand for quality of care measures is high, technical challenges remain in ensuring case-mix-adjusted outcomes accurately reflect quality differences.