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Modeling financial outcomes and quantifying risk in episode-based payment models.

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  • 1Department of Urology, University of North Carolina, 2105 Physician's Office Building, 170 Manning Dr, CB 7235, Chapel Hill, NC 27599-7235.

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Health systems can now quantify financial risks of value-based payment models using a new simulation method. This approach analyzes episode-based payments and clinical cost drivers to inform stakeholders during the transition to value-based care.

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Area of Science:

  • Health economics
  • Health services research
  • Medical informatics

Background:

  • Healthcare systems face challenges in assessing the financial impact of value-based alternative payment models.
  • A systematic mechanism is needed to evaluate financial implications, including risk and uncertainty, of transitioning from fee-for-service to episode-based payments.
  • Understanding the influence of clinical cost drivers on financial outcomes is crucial for successful value-based care implementation.

Purpose of the Study:

  • To develop a method for prospectively quantifying the financial implications of value-based alternative payments.
  • To assess the financial risks and uncertainties associated with transitioning to episode-based payment models.
  • To analyze the impact of modifying episode-specific clinical cost drivers on financial outcomes.

Main Methods:

  • A financial simulation model was developed using empirical data from a prostatectomy (prostate cancer surgery) episode-based payment model.
  • Monte Carlo simulation methods were employed to predict financial outcomes under various clinical and payment scenarios.
  • Patient-level cost, reimbursement, and clinical data from 157 patients were input to quantify expected financial outcomes and risks for stakeholders.

Main Results:

  • The simulation revealed a range of expected financial outcomes for payers, hospitals, and providers under an episode-based payment model, varying with parameters like episode price and risk-sharing.
  • Modifying clinical cost drivers significantly impacts financial outcomes.
  • High uncertainty in financial predictions was noted, partly due to the limited number of episodes in the pilot study.

Conclusions:

  • Financial parameters and clinical cost drivers are significant determinants of expected financial outcomes for stakeholders in value-based payment models.
  • The developed simulation method provides a practical tool for evaluating financial implications and facilitating stakeholder engagement in value-based payment transitions.
  • Further refinement and application of the model with larger datasets are warranted to reduce uncertainty and enhance predictive accuracy.