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Area of Science:

  • Health Economics
  • Health Policy
  • Data Science

Background:

  • The U.S. government invests billions in testing value-based payment (VBP) models for healthcare.
  • Unintended consequences are a significant concern as VBP models scale.
  • Voluntary participation in VBP models can amplify unintended outcomes.

Purpose of the Study:

  • To propose an unsupervised outlier detection method for identifying unintended consequences in VBP models.
  • To provide a generalizable approach for detecting problematic participant behavior early.
  • To help policymakers and researchers mitigate risks associated with VBP model implementation.

Main Methods:

  • Developed a straightforward unsupervised outlier detection approach.
  • Utilized ranked percentage changes to identify participants with unusual behavior.
  • Applied the method to hypothetical data and simulations for validation.

Main Results:

  • The approach correctly identified "bad actors" in 86% of simulations by the first period and 96% by the second.
  • A trade-off exists, with 9% of honest participants mistakenly flagged as "bad actors" by the second period.
  • The method demonstrated efficiency in detecting unintended consequences.

Conclusions:

  • The proposed outlier detection method offers a practical tool for monitoring VBP models.
  • Early identification of unintended consequences can prevent issues in scaled-up programs.
  • Researchers can adapt this method to protect VBP models, even with a single problematic participant.