How to outsmart biases in health plan claims data

Area of Science:

• Healthcare Economics
• Health Plan Administration
• Risk Management

Background:

• Negotiating healthcare contracts involves setting payment rates like per member per month (PMPM) and case rates.
• Historical data from Health Maintenance Organizations (HMOs) is often used but can be biased and incomplete, posing risks.
• Accurate rate-setting is crucial for financial stability and successful contract outcomes.

Purpose of the Study:

• To provide expert advice for mitigating risks associated with setting PMPM and case rates.
• To offer practical strategies for utilizing potentially flawed HMO historical data.
• To present benchmark utilization projections for commercial and Medicare populations to aid in rate setting.

Main Methods:

• The study outlines 8 specific tips for analyzing and applying HMO-provided historical data.
• It incorporates benchmark utilization projections derived from analysis of commercial and Medicare populations.
• The methodology focuses on risk reduction strategies in the context of contract negotiation.

Main Results:

• Implementation of the 8 tips can significantly reduce the risks associated with using biased or incomplete HMO data.
• Benchmark utilization projections offer a more reliable basis for forecasting compared to solely relying on historical data.
• The advice aims to improve the accuracy and fairness of PMPM and case rate negotiations.

Conclusions:

• Healthcare organizations can improve contract negotiations by proactively addressing data limitations.
• Utilizing strategic data analysis and benchmark projections enhances the reliability of PMPM and case rate setting.
• Adopting these expert recommendations leads to more robust and less risky healthcare contract negotiations.