How Alternative Payment Models in Healthcare Can Impact Cost

By | November 9, 2020

As we continue to focus on value-based payment models, there is a strong belief that moving away from fee-for-service payments towards a per-patient rate for primary care providers will decrease overall costs. The counter concern is that this may also lead to reduced care because of the financial incentive to avoid services.

Primary care providers (PCPs) have the task of caring for those we serve on the front lines. As a result, there are many payment models for such care as there is a belief that PCPs can influence spending given they are often the source of utilization. Many of these models incentivize by providing additional payments that are associated with decreased utilization, as we also believe that there are services that are over-utilized, such as imaging.

A recent study by Linder et al. in Health Affairs discussed the impact of a Medicaid alternative payment model in Oregon. Payment to the community health centers occurred on a per-patient rate for traditional primary care services, including imaging, tests, and procedures. Also, they delineated certain services, such as emergency room visits and inpatient services. This payment reform is associated with a 42.5% relative reduction in price-weighted traditional primary care services, driven entirely by decreasing imaging services. Moreover, other outcomes did not change.

These findings are extremely intriguing in that they speak to the behaviors that exist. As these centers were focused on providing the best care possible and wanted to improve their financial situation, they did so by decreasing imaging services. Leading one to make the conclusion that many of these tests were unnecessary. And since outcomes remained the same, they may be correct.

The behavioral change appears tied to that of a payment dynamic. If I decrease the cost that I believe is genuinely unnecessary, then I benefit. So why do these tests occur initially? It behooves us to keep in mind that our training requires us to rule out situations, even though the likelihood is extremely low. This approach leads to testing to ensure there is “not something going on,” versus diagnosing and treating based on “what is probably going on.”  Sure, periodically, such testing will identify an unexpected finding, but this leads to higher utilization.

To successfully manage the cost and quality of diagnosis, thus the value of care, it’s essential to be aware of our behavioral dynamics as deliverers of services that result in costs to others. When we own that cost, we begin to contemplate scenarios and situations differently. As outcomes did not change, we begin to learn how to manage this polarity. The key to success will be our continued learning in discerning when interventions are genuinely not needed and incentivize in a manner that does not force eliminating necessary and critical services.