Within the past decade and a half, states have been aiming to monitor and control the rising cost of healthcare. One approach used to tackle this problem is the creation of a benchmark trend rate to which year-over-year changes in healthcare expenditure can be compared. State-level cost growth benchmark programs aim to increase accountability and control healthcare spending, but their effectiveness depends on whether the measurement framework can distinguish true cost-management performance from population mix, high-cost claimant volatility, market-wide cost variation, and methodological artifacts.
When measuring healthcare trend, results may be more meaningful when based on a stable population. The annual turnover of commercial insurance business can shift a population’s underlying risk profile and reduce year-to-year stability. It may therefore be helpful to consider the additional volatility that can arise when assessing trends at more segmented levels, such as by payer or provider.
This white paper analyzes three components of these programs that influence measurement results: the trend rate benchmark itself, the handling of high-cost claimants, and the presence of risk adjustment. If states use cost growth benchmarks for accountability, the measurement system should ideally distinguish controllable cost performance from population mix, high-cost claimant volatility, market-wide trend pressure, and other factors that are more related to market variability, where providers and payers have minimal direct influence.
The paper covers the following key discussion points.
- Healthcare expense trends: These provide an understanding of the underlying patterns and rates at which cost and utilization have changed over time.
- High-cost claimants: Outlier events, though rare, are costly occurrences that can have significant financial impact.
- Implementation of risk adjustment: The performance of provider organizations participating in accountable care organizations is adjusted to account for changes in their underlying populations in order to appropriately compare to the benchmark.