Health Care Payment Learning Action Network: Financial Benchmarking

The Health Care Payment Learning Action Network (HCPLAN) has released a white paper on performance measurement within alternative payment models (APMs).  HCPLAN work products will be considered by the Centers for Medicare and Medicaid Services (CMS)  for implementation of the Medicare Access and Children’s Health Insurance Program Reauthorization Act (MACRA) and other health care payment reform initiatives.  This white paper was developed by the Population-Based Payment Work Group and documents principles and recommendations for financial benchmarking.

In shifting away from fee-for-service payments to alternative payment models, financial benchmarking becomes a key challenge to design and implement. Many accountable care organizations have complained that both national benchmarking and benchmarking based on one’s own performance have the unintended consequences of dis-incentivizing participation of “high performers”, who already have demonstrated the capacity to deliver higher quality and lower cost care, in current alternative payment models. CMS and other payers developing and implementing such models have to achieve a balance between raising the performance of all providers, reducing regional variations in expenditures, and continuing to support innovation and improvement among those high performers. Another debated issue is whether financial benchmarks should adjusted based on patient risk characteristics, especially for safety net providers who historically have served patients without regular care or continuity of care, and often have multiple and more complex health conditions.

The white paper uses the following principles for financial benchmarking:

  • Trust among payers, providers, purchasers, and consumers is essential for managing population-based payment models over time as benchmarks are updated, rebased, and risk adjusted;
  • Financial benchmarks in population-based payment models should incentivize high-quality, efficient care, enable accountability, and establish a target that fairly rewards provider organizations;
  • Payers should transparently communicate to providers the risk-sharing parameters involved in participating in a population-based payment model, such that providers can access the information they need to fully comprehend the risks associated with participation, understanding that there is an inherent tradeoff between simplicity and precision in payment design, and that it may not be possible to precisely quantify risk ahead of time;
  • Successful approaches to financial benchmarking must simultaneously encourage participation while meeting financial, quality, and access objectives; and
  • The effect of financial benchmarks is to enable 1) efficient provider organizations to succeed; 2) struggling organizations to improve; and 3) failing organizations to fail.

And the white paper makes the following recommendations for financial benchmarking:

  • Approaches to financial benchmarking should encourage participation in the early years of the model’s progression, while driving convergence across providers at different starting points toward efficiency in the latter years;
  • The initial baseline should be based on provider-specific spending, taking into account the provider organization’s history and local market forces;
  • Updating and rebasing of the initial benchmark should not be based on provider-specific changes in spending;
  • Updating and rebasing of the initial baseline should drive convergence around local spending rates as quickly as local conditions allow, with an eventual movement to regional rates in the medium to long term;
  • There are multiple pathways to convergence but the end point is what matters;
  • Risk adjustment must strike a fine balance such that providers who serve higher-risk or disadvantaged populations are not unduly penalized and disadvantaged populations do not receive substandard care;
  • The state-of-the art of risk adjustment is likely to change over time, and it will be important to keep up with recent developments that improve the precision of risk-adjustment approaches;
  • Risk-adjustment models should minimize the connection between utilization and risk score;
  • Successful risk-adjustment models should accurately predict spending at the population and subpopulation levels, but it is not important for models to accurately predict spending at the individual level; and
  • Population-based payment models should not disrupt care for needy populations, and risk adjusting for socioeconomic status (SES) may be one way to accomplish this;  nevertheless, SES adjustments should not be a mechanism for forgiving lower care for needy populations.

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