Many value-based payment agreements (VBAs) and programs incentivize physicians to provide care based on patients’ achievement of treatment targets, especially threshold-based metrics such as maintaining a satisfactory biomarker value. This may not ultimately be the optimal design for a population health program, as they may incentivize physicians and health systems to expend more resources on patients who require relatively less effort to bring to goal compared with patients who are more difficult to bring to goal. Modeling can be helpful to assess whether changing a threshold-based target scheme might yield improvements in health and resource allocation for chronic diseases. In the continuing drive to move the US health care system from a primarily volume-based system to a value-based system, risk-shifting in the form of VBAs only works when the delivery system is designed to achieve maximal overall health improvements.
To shift incentives in the health care sector to favor “value” over “volume,” private insurers, public payers, and purchasers apply quality measures to monitor processes and outcomes for health plan accreditation and to adjust fee-for-service payments based on performance (eg, The National Committee for Quality Assurance [NCQA] Health Plan Accreditation Program, Centers for Medicare & Medicaid Services [CMS] Medicare Stars program, and MACRA1-6). In addition, alternative payment models further target and incentivize the achievement of population health goals (eg, CMS alternative payment models).7 While advanced value-based payment models (eg, bundled payments and shared savings) are now becoming more popular, pay-for-performance is still common and often used alongside other payment methods.8,9
Using quality measures allows for standardized assessment of care across a range of patients,10 but uniform measures may not adequately capture the diversity of patient experiences with treatment or the challenges physicians face, especially as treatment becomes more personalized. The many value-based payment programs that incentivize physicians to provide care based on patients’ achievement of treatment targets,11 especially threshold-based metrics like maintaining a satisfactory biomarker value (eg, blood pressure under recommended levels), must be designed carefully, as they may incentivize physicians to expend more resources on patients who require relatively less effort to bring to goal (eg, those with marginally elevated blood pressure) compared with patients who are more difficult to bring to goal (eg, those with refractory hypertension). Whether reallocation of effort incentivized by this type of threshold achievement scheme would result in better outcomes at lower costs depends on how the health improvements for the less healthy patients compare with those of the relatively healthier patients.
The improvement in health from intensifying treatment for a relatively healthy patient may differ from the improvement in health from intensifying treatment for a relatively unhealthy patient. In some cases, it may be that the less healthy group benefits relatively more from treatment even if they do not reach the threshold. In addition, treatment intensification among those in the relatively healthier group may only improve health marginally.
Diabetes is chronic, progressive, prevalent, and costly ($327 billion in 201712), and there is a long history of value-based arrangements that include threshold-based goals.13 Good blood glucose control has been shown to improve patient outcomes, reducing rates of expensive and debilitating diabetes-related complications.14 Comprehensive Diabetes Care is part of NCQA’s HEDIS measure set, by which health plans are evaluated for accreditation, and blood glucose testing and goal attainment (ie, HbA1c poor control >9.0%, HbA1c control <8.0%, HbA1c control <7.0% for a selected population) are core components.15,16 Three features of diabetes and its treatment suggest that intensification might provide more health improvements for patients with poor blood glucose control than those with relatively good glucose control: (1) diabetes complication risks generally increase exponentially (ie, increase at an increasing rate) with rising blood glucose levels14; (2) the benefits of a 1% point reduction in HbA1c increase in absolute terms as complication risks increase14,17,18; and (3) individuals with higher HbA1c levels experience larger absolute HbA1c reductions from treatment, regardless of drug class or mode of action.19,20 Taken together, these features make it probable that individuals with higher HbA1c values will experience greater absolute benefits from treatment (even if they do not meet target HbA1c goals).
Despite widespread use of value-based payment incentives and a myriad of options for controlling blood glucose, the proportion of patients with poor blood glucose control remains high in the United States for all types of insurers. In 2017, the percent of individuals with diabetes with HbA1c>9.0% ranged across different provider categories from 22.3% for Medicare PPO to 41.2% for Commercial PPO and the percent with HbA1c>8.0% ranged from 32.8% for Medicare PPO to 51.6% for Medicaid HMO.15
Whether to expend resources and implement alternative value-based payment schemes in care to address this issue depends on the size of the potential health gains and improvements in resource allocation. Economic modeling (ie, mathematical equations that synthesize available data such as short-run clinical trial outcomes, risk equations and progression rates, and known physiological relationships into a coherent, internally consistent framework) can be used to understand the implications of alternative payment structures on health outcomes. This article presents the results of a modeling exercise in diabetes to illustrate whether changing a threshold-based target scheme might yield improvements in health and resource allocation as implications of these findings may apply to other chronic and life-threatening diseases. In particular, cancer is a disease where there is significant burden and where important economic and outcomes data will be available for modeling in the near future; the Center for Medicare and Medicaid Innovation (CMMI) is now approaching year four of their large scale and bold CMMI Oncology Care Management (CMMI OCM) program.