Re: How Evolving United States Payment Models Influence Primary Care and its Impact on the Quadruple Aim: The Need for Health Equity

To the Editor: Park et al¹ make an important contribution in their review of what is known about the impact of different payment models on the core functions of primary care and their corresponding impact across the Quadruple Aim. Our comments focus on health equity in terms of the aims of payment models, risk adjustment, and creation of continuous learning payment systems. Our commentary is intended as a friendly amendment to this excellent article.

The authors adopt the Quadruple Aim to align payment models with intended results. However, equity is not among these 4 aims. The authors use the fourth aim to refer to “physician well-being” although others use it to reflect health equity.² We suspect that Barbara Starfield would have advocated for making equity an explicit aim. She recognized primary care as a potent tool for achieving health equity,³ and she criticized the World Health Organization for removing equity from its public health monitoring and policy agenda.⁴

We propose equity as a key health care aim for primary care payment models. This could be accomplished by making equity a fifth aim. However, “The Quintuple Aim” is less focused. Alternatively, aims could be consolidated into The Dual Aim: improved health care value and equity. Value implies a focus on desired processes including first Contact, Continuity, Comprehensiveness and Coordination (4C's) and on meaningful patient/family health outcomes relative to cost. The original “Triple Aim” (Better Health, Better Care, and Lower Cost) is subsumed under value. The wellbeing of the physician/team could be explicitly called out under “Better Health.” Equity implies fairness in processes, outcomes, and relative costs. We appreciate that choice of aims is not the authors' focus. Yet, equity is inextricably linked to payment thus meriting its own aim.

The authors astutely note the importance of risk-adjustment payments. Risk adjustment promotes equitable payment among clinicians that care for patients with varying levels of need. When thoughtfully designed to promote equity, risk adjustment aligns population needs with the resources to address those needs. This requires consideration of health complexity and social complexity including the socioeconomic status of the patient/family and their community.⁵ Risk adjustments are critical to creating equitable capitation payments and to incentivizing equity in meaningful outcomes, (eg, reduction in obesity, smoking cessation, and avoidable hospitalizations etc.) that require greater resources.

We strongly concur with the authors regarding the need for rapid dissemination and research. No doubt, there is a critical need for practice-based-research network infrastructure, widespread practice experimentation, and rapid cycle funding necessary to guide design, refinements in payment models that include health care value and equity as outcomes. We favor comprehensive payment models that includes a bit of pay-for-performance with adequate adjustment for social risk. However, we acknowledge uncertainty regarding how different models will fare vis-a-vis “The Dual Aim.”

This leads to our last, aspirational suggestion. We need to build adaptive feedback loops that continuously adjust payment model features, levels of payments, and risk adjustments to optimize achievement of value and equity. Primary care payments models are part of a larger, complex adaptive system that can be best evaluated by perturbing it through continuous targeted changes analogous to plan-do-study-act cycles. This will require big data sets that include all payer claims, community health data, and electronic health record data that include social and behavioral risk, which enable assessment of equity. The Institute of Medicine report, Measuring Vital Signs, offers a reasonable starting place for meaningful core health metrics.⁶ Through use of big data analytics such as machine learning and simulations, it may ultimately become possible to create continuous learning systems that adjust models to optimize primary care value and equity.