Paying for Population Health: A View of the Opportunity and Challenges in Health Care Reform
The U.S. health care system is in the early stages of transitioning from a payment system driven by volume to one based on value. New payment models are being tested at scale by both private and public payers, and payers are learning to align their financial models with each other in order to accelerate the transformation of the system. The acceptance of the Institute for Health Care Improvement’s “Triple Aim” as the framework for defining value in the new system has led to broad diffusion of language supporting the three goals of 1) improved health of populations, 2) improved patient experience for those who need care, and 3) reduced trends in total per capita health care expenditures. This creates the possibility of a new funding stream that rewards improvements in population health and a window of opportunity to transition to a more sustainable funding model for population health. However, the complexity and relative weakness of the key building blocks of population health payment models create the threat that population health will not be integrated into the new payment system in a meaningful way.
Some of the barriers are rooted in the nature and scale of the transformation that is under way. The need for health reform is being driven to a large extent by a concern about the rate of growth of health care expenditures, and the task of actually managing the total per capita cost dimension of the Triple Aim is all-consuming. The actual evolution of the delivery system lags the rhetoric, with many players content to stay in the familiar fee-for-service system as long as possible. Even provider systems with decades of experience with capitated models for commercial insurers are finding that learning to adjust to the differences imposed by the Centers for Medicare & Medicaid Services (CMS) for the Medicare savings-sharing programs is taxing their capacity for change.
The total cost and patient experience dimensions of the Triple Aim are much more familiar and better understood; as such, there is greater focus in these areas. They have been used in payment models for decades and as a result reliable metrics have been developed and implemented at the care system level. More importantly, the accountability for outcomes is more clear-cut and a broad portfolio of interventions and care management tools is available.
Given these challenges, the tendency so far has been to focus on the simpler, more familiar clinical-services elements of population health, such as preventive care. The consequences of determinants of health models, which show that clinical care accounts for only 10 to 20 percent of health outcomes, are not widely accepted. Provider systems resist considering themselves accountable for nonclinical determinants and focus on their patients, not the population of the community. Even for those who would like to accept accountability, the portfolio of validated interventions for improving population health, e.g., reducing adult obesity, is very thin.
Another major problem is that the existing set of population health measures and datasets for process improvement, accountability, and payment are neither well developed nor implemented to provide timely data with the needed granularity. First, there is significant confusion about the distinction between measuring quality of care and measuring population health, even though they are two very different dimensions of performance. Second, the population health measures incorporated in current payment models focus on clinical preventive services. A more robust set of population health measures would track progress in upstream determinants of health, intermediate outcomes in disease burden and patient-reported quality of life, and final outcomes in quality-adjusted life expectancy.
A more fundamental problem in paying for population health is that the business case for population health is complex and requires reinvestment from shared savings in multiple sectors that accrue over long periods of time. Population health programs have traditionally been funded by grants or fee-for-service revenues. Evaluation has been based on impacts on risk factors, i.e., whether an intervention changed behavior, rather than the value it created. Payment models based on performance are relatively new and unexplored territory. The current shared-savings models, which account only for impacts on medical expenditures on an annual cycle, will not capture the benefits of many effective population health interventions. Payment models that support population health and reward a broader spectrum of efforts are still in the early stages of development. They require stronger building blocks in both measures and analytic models for projecting long-term impacts.
How can we pay for improvements in population health? This is a simple question to ask, but a difficult one to answer. However, we will not get the community health system we need until we learn how to answer it. The threat is that while we are learning how to pay for health, the payment system will reach a new equilibrium that does not include a meaningful population health component. The population health community needs to increase awareness of this window of opportunity, accelerate the development of tools that can support a variety of payment models, and encourage pilots that test these models in practice.
States participating in the State Innovation Model awards should provide fertile ground for such tests. The Funding Opportunity Announcement for the second round of the CMS Health Care Innovation Awards explicitly called for the testing of new payment models for population health. These programs are an excellent start, but the criteria used by CMS focus, understandably, on short-term returns and net savings in medical costs within 3 years. In order to create a more balanced portfolio of interventions with a wider range of time horizons, other sponsors, both public and private, will need to step up to the challenge of testing population health payment models.