A clinically detailed risk information system for cost.

Area of Science:

• Health Services Research
• Medical Economics
• Public Health

Background:

• Estimating healthcare resource needs for diverse patient subgroups is complex.
• Existing models may not adequately account for disease burden and varying costs across payers.
• Accurate risk adjustment is crucial for equitable healthcare resource allocation, particularly in public programs like Medicaid.

Purpose of the Study:

• To develop and validate a system for quantifying healthcare resource requirements based on disease burden and severity.
• To assess the accuracy, replicability, and cross-payer applicability of the developed models.
• To evaluate the potential of the system as a risk adjustment tool for Medicaid programs.

Main Methods:

• Developed a system based on 173 conditions with up to 3 severity levels.
• Integrated prospective diagnoses with retrospective data elements.
• Utilized data from four payers, standardizing service costs.
• Incorporated models to reduce rewards for biased selection.

Main Results:

• The developed models demonstrated replicability and reasonable accuracy.
• The models effectively explained cost variations across different payers.
• The system showed potential to mitigate biased selection in healthcare resource allocation.
• A prospective model with adjustments for birth episodes and neonatal complications was identified as an effective Medicaid risk adjuster.

Conclusions:

• The disease burden-based system provides a robust method for estimating healthcare resource needs.
• The models offer a valid and adaptable tool for understanding and managing healthcare costs across payers.
• The proposed prospective model holds significant promise for enhancing risk adjustment in Medicaid programs, ensuring equitable resource distribution.