Expert Consensus on Essential Characteristics of Oncology Value-Based Payment

This supplement was sponsored by Johnson & Johnson.

ABSTRACT

Value-based payment (VBP) models are increasingly adopted in oncology to promote high-quality, cost-effective, and patient-centered care. To identify essential characteristics of effective oncology VBP models, a modified Delphi process was conducted with 9 experts representing diverse oncology organizations. Panelists participated in 2 survey rounds and an in-person discussion, evaluating the importance and feasibility of key VBP model elements using Likert scales. Results revealed consensus on the need for patient-centered care, robust risk adjustment, and oncology-specific outcome measures. While pay-for-performance and enhanced monthly payment models were seen as feasible and widely used, they were also criticized for outdated metrics, insufficient reimbursement, and high administrative burden. High-impact models such as dual-sided risk and cost-containment approaches were viewed as promising but challenging to implement, particularly for small or rural practices. Panelists cited stakeholder misalignment, financial risk, and lack of standardized benchmarks as major barriers to effective implementation. The authors concluded that future VBP models must align incentives across stakeholders, accommodate clinical complexity, and evolve iteratively to support innovation, equity, and sustainability in oncology care.

Am J Manag Care. 2025;31(suppl 5):S71-S79

For author information and disclosures, see end of text.

Introduction

Value-based payment (VBP) is a health care reimbursement approach designed to reward providers—such as physicians, hospitals, and clinics¹—for delivering high-quality care rather than incentivizing service volume.² Its primary objective is to enhance patient outcomes while lowering health care costs by linking compensation to the value of care provided.² Examples of VBP models include bundled payment models, capitation models, and shared savings/risk
models, among others.¹

In recent years, both public and private payers have broadened their VBP initiatives beyond primary care to encompass various specialties, including oncology.² Consequently, oncology payment models have shifted from traditional fee-for-service or cost-plus reimbursement to quality- and performance-based frameworks, such as pay-for-performance programs or the Quality Oncology Practice Initiative.^2,3 More recently, newer payment approaches have emerged, including Medicare’s Enhancing Oncology Model (EOM)⁴ and comparable programs developed by private insurers like Elevance Health, Aetna, UnitedHealthcare,² and BlueCross BlueShield of Tennessee.⁵

The Centers for Medicare & Medicaid Services (CMS) aims to enroll all original Medicare beneficiaries in a high–financial risk VBP model by 2030.⁶ As of 2023, VBP models make up 61.6% of total US health care reimbursements, with 28.5% of payments tied to high–financial risk arrangements.⁶ Given that VBP is increasingly permeating our health care system, optimizing the essential elements and defining characteristics of effective VBP models is crucial to ensuring they achieve what is known as the quadruple aim: to improve population health, lower costs, enrich the patient experience, and enhance the work life of health care clinicians and staff.⁷ However, creating this efficiency remains challenging in an increasingly complicated health care landscape.

The Delphi method is a widely recognized research technique used to establish consensus in areas with significant uncertainty and limited empirical evidence.⁸ The approach incorporates key components such as anonymity, iterative rounds, and controlled feedback,⁹ which can strengthen existing evidence and may help inform health care decisions. The modified Delphi method, often used in health service research, incorporates an in-person discussion between survey rounds to allow panelists to engage directly with one another, contextualize answers, and possibly resolve differences.⁹

The aim of this study is to identify the essential characteristics of VBP models between payers and providers through a modified Delphi method with a panel of VBP experts to capture areas of consensus.

Methods

Following accepted modified Delphi practice,¹⁰ a literature review for peer-reviewed articles related to VBP in oncology was conducted in November 2024 (Technical Appendix 1 and eAppendix Figure 1 [available at ajmc.com]). From search results, key characteristics of oncology VBP were identified and formulated into a survey for VBP experts (Technical Appendix 2). The survey assessed the importance and the feasibility of these characteristics, the top payment models used by payers, and the benefits and challenges of those models. It used a 7-point Likert scale, in which 7 represented a high mark (eg, extremely important) and 1 represented a low mark (eg, not at all important), and a simple ranking method (ie, rank from most important to least important and rank from most feasible to least feasible).

A panel of experts in VBP was recruited based on their educational background, known or stated expertise regarding VBP and the US health care system, years of experience in the current professional field, and understanding of different features of VBP models. Panelists completed the first-round survey. Pooled survey results were summarized using numerical averages, ranking, and a visual prioritization matrix. Panelists then reviewed the summarized results in an in-person discussion moderated towards consensus. While Likert responses are ordinal, the averaging of these responses is commonly accepted in Delphi and health policy research.¹¹ After the discussion, panelists responded to the survey a second time; the new results were summarized using the same methods.

The level of consensus was assessed, areas of agreement were articulated, and key discussion points were summarized.

Results

Eight panelists representing a variety of educational backgrounds, institutions, and occupations were recruited; they served as the authors of this supplement. Panelists convened on December 18, 2024, at the Grand Hyatt Hotel in Dallas, Texas. All panelists completed both surveys and participated in the moderated discussion.

Survey Results and Subsequent Discussion

Results of survey rounds 1 and 2 and the discussion between these rounds are summarized below.

Top Benefits and Challenges of Most-Used Payment Models

Responses to questions about most-used payment models and corresponding benefits and challenges (survey questions 1-3) were consistent from the first survey round to the second. The most-used payment models for oncology were, in order, fee-for-service, pay-for-performance, Oncology Care Model (OCM)/EOM, and single-sided risk/shared savings.

The top benefits of the most-used models are listed in order of benefit in Figure 1. The highest ranked were enhanced care coordination to manage costs or improve outcomes, financial incentives for quality care or cost savings, and no restrictions on type or quantity of services offered.

CURRENT CHALLENGES WITH VALUE-BASED PAYMENT IN ONCOLOGY

The top challenges of the most-used models are listed in order in Figure 2. The highest rated was administrative complexity. While VBP models aim to improve quality and reduce costs, their success is undermined by the need to report, track metrics, and manage compliance, panelists concluded. These demands increase operational costs and divert resources from patient care.

Panelists’ second-rated challenge was financial risk or unpredictability. Current models lack sufficient mechanisms to account for patient variability, disease complexity, and social determinants of health (SDOH) in oncology, panelists noted. Panelists emphasized that existing risk adjustment methodologies fail to include sufficient clinical variables, exposing practices to financial risk when caring appropriately for highly acute patients using high-cost therapies. These models rely on outdated, oversimplified, or less relevant process and structure measures rather than meaningful clinical or patient-reported outcomes. Absent such outcomes and standardized care pathways to support them, practices serving complex or underserved populations face uncertainty about reimbursement.

Regulatory, policy, or market dynamics constituted panelists’ third-rated challenge. Frequent changes in policy contribute to compliance burdens, short-term payer incentives, and rising cost pressures, panelists agreed, complicating equitable implementation.

Although not rated among the top 3 challenges, panelists also highlighted stakeholder misalignment, stressing that true value must reflect the patient perspective.

ADDITIONAL EXTERNAL FACTORS INFLUENCING ONCOLOGY CARE

Panelists discussed how external factors create both opportunities and challenges for oncology care. As affordability and cost management remain top priorities, oncology practices face increasing pressure.

Regulatory and policy changes, (eg, pharmacy benefit manager reform, the Inflation Reduction Act, and CMS initiatives [OCM and EOM]) were seen as highly influential. Additionally, payers are adopting more aggressive utilization management policies, panelists agreed.

While innovative therapies like chimeric antigen receptor T-cell (CAR-T) therapies, bispecifics, and personalized medicine are improving outcomes, issues surrounding high up-front costs, difficult administration, and restricted coverage present major hurdles in adopting newer therapeutics. Similarly, digital tools like telehealth and artificial intelligence enhance delivery of care but demand significant resources.

Panelists noted that health care consolidation is reshaping oncology care. Smaller practices face financial strain, and hospital networks are acquiring oncology groups and driving vertical integration across care systems.

Societal trends also intensify pressure on providers, panelists agreed. An aging population, persistent disparities in access and resources, and workforce shortages (eg, oncologists, navigators, and nurses) challenge care delivery.

Economic conditions further affect affordability and adherence, panelists added. Inflation raises operational costs, and rising out-of-pocket expenses reduce patient satisfaction and treatment adherence, complicating providers’ ability to deliver consistent, high-quality care.

CHALLENGES IN TREATING SMALL OR RURAL POPULATIONS

Small and rural oncology practices face unique challenges in adopting VBP models, panelists noted. These practices often lack the resources, infrastructure, and patient volumes needed to meet performance benchmarks and remain financially sustainable.

The up-front costs of transitioning from fee-for-service to VBP (eg, for training, technology, and staffing) are often unaffordable for smaller practices, panelists added. Limited data-sharing capabilities and poor care coordination further contribute to fragmented care.

During the discussion, panelists observed that programs like social work, care navigation, and patient education are essential for improving outcomes but are often underfunded or under-reimbursed, making them difficult to sustain in resource-limited settings. Staffing shortages—particularly in key roles such as care navigators, social workers, and oncologists—compound these challenges.

Additionally, some providers resist adopting new models or technologies due to uncertainty, mistrust, or fear of financial risk, further limiting participation in VBP initiatives. Overall, structural and financial barriers make it significantly harder for small and rural practices to implement VBP effectively, panelists concluded.

Assessment of Feasibility of Current Value-Based Payment Models

The survey assessed the impact and feasibility of several VBP models (questions 4 and 8). Findings from rounds 1 and 2 are summarized in eAppendix Table 1 and illustrated in eAppendix Figure 2 as prioritization matrices.

PAY-FOR-PERFORMANCE MODELS

Panelists agreed that pay-for-performance models are widely used outside of rural practices due to their ease of implementation and their focus on quality care and provider accountability. They recognized benefits such as incentivizing quality improvement, using structured metrics to track performance, and encouraging investments in data analytics and care coordination. However, many noted that these models need refinement. Current versions often rely on outdated or simplistic metrics and demand significant administrative effort; further, they have had a limited impact on improving oncology outcomes or lowering costs. Panelists also observed that the model’s complexity makes it difficult for smaller or underserved practices to participate, potentially increasing inequities. They recommended updates to better reflect current clinical practice, reduce reporting burdens, and incorporate gold carding mechanisms, which exempt providers who have demonstrated adherence to appropriate clinical criteria via exceptional prior authorization (PA) approval rates from future PA requirements for those services.

MONTHLY ENHANCED ONCOLOGY SERVICES PAYMENT MODELS

Panelists noted that monthly enhanced oncology services payment models are used in several practices and support care coordination in line with VBP goals, although current payments often fall short of covering full care costs. They highlighted the models’ value in addressing gaps left by traditional reimbursement, particularly for nonbillable but essential services like patient education, care navigation, and psychosocial support. The models also encourage investment in care coordination roles and promote a more comprehensive approach to care.

However, panelists identified key shortcomings: per-member-per-month payments under programs like OCM and EOM are too low to cover costs, especially for high-need oncology patients; tracking and justifying payment use are administratively burdensome; and payments typically apply only to select patient populations, limiting broader impact.

Panelists recommended increasing payment levels to reflect actual care coordination costs, simplifying reporting requirements to reduce administrative strain, and expanding eligibility to reach a wider patient population.

SINGLE-SIDED/DUAL-SIDED RISK MODELS

Panelists agreed that single-sided and dual-sided risk models are essential components of VBP but are in limited use. Single-sided, or upside-only, risk arrangements present uncertainty to the provider only with respect to future financial gain. Dual sided–risk arrangements pose additional downside risk, which is uncertainty about future financial loss.¹² Single-sided risk is more appealing and feasible as a low-stakes entry point, while dual-sided risk offers greater impact on cost control and accountability but carries higher financial risk, prompting caution.

Both models incentivize quality improvement and cost reduction and encourage provider accountability for outcomes and resource use. Panelists viewed single-sided models as a transitional step from fee-for-service to dual-sided risk.

However, panelists noted challenges: without accurate risk adjustment, practices serving complex patients may be unfairly penalized; both models involve high administrative burdens, including tracking metrics, reporting outcomes, and managing reconciliation; and they may discourage care for high-risk patients.

Panelists recommended simplifying reporting and reconciliation processes and adopting advanced risk stratification methods to account for complex patients and ensure fair, achievable benchmarks.

COST-CONTAINMENT/LONGITUDINAL RISK MODELS

Panelists agreed that while cost-containment/longitudinal risk models offer potential impact, they are difficult to implement and overly cost-focused, and they often fail to satisfy all stakeholders. The models’ strength lies in supporting higher up-front costs for long-term savings and emphasizing total cost of care. However, panelists noted key challenges. Accurately stratifying risk and setting benchmarks across diverse patient populations are complex, the focus on cost may compromise care or hinder adoption of innovative treatments, and long-term savings offer limited appeal to payers. They recommended refining the models to balance short-term payer goals with long-term, value-based cost optimization.

PROSPECTIVE PAYMENT SYSTEM/CAPITATED MODELS

Panelists agreed that while capitated models provide predictable income and flexibility for practices, they are difficult to implement in oncology due to patient complexity and cost variability. These models are not widely used in oncology but are growing in primary care, where providers may subcontract with specialists to manage costs.

Benefits include steady revenue streams amid fee-for-service unpredictability, incentives to manage resources efficiently, and freedom to innovate without being tied to service-based benchmarks. However, panelists cited key concerns: inadequate risk stratification, insufficient coverage for high-cost or complex patients, and risk of undertreatment to protect margins. Fixed payments may not keep pace with rising oncology treatment costs, limiting the adoption of innovative therapies. Smaller practices may also lack the resources to manage financial risk.

Panelists recommended improving capitated models by adjusting rates to reflect inflation and treatment advances, incorporating robust risk stratification, and adding safeguards to prevent care underuse. They also suggested offering infrastructure support and risk-pooling options to make these models more feasible for smaller practices while maintaining focus on quality outcomes.

COST-BENCHMARKING MODELS

Panelists agreed that while cost-benchmarking models can promote efficiency and accountability, they raise concerns around feasibility, fairness, and administrative burden, and they are not widely adopted in practice.

Benefits include establishing cost baselines, creating financial accountability, encouraging cost-conscious care, and setting consistent performance standards. However, panelists cited key drawbacks: current risk adjustment methods fail to account for patient variability, treatment complexity, and disease differences; benchmarks may unfairly penalize smaller or under-resourced practices; and high-cost innovations may be discouraged. Additionally, the data demands for developing and tracking benchmarks are burdensome.

Panelists recommended improving the model by incorporating patient acuity, treatment complexity, and geographic variation; using disease- and patient-specific benchmarks; pairing cost metrics with quality and outcomes to protect care standards; and ensuring transparency in how benchmarks are developed and applied.

Evaluation of Value-Based Payment Characteristics

In both survey rounds and in the discussion, panelists evaluated the importance and feasibility of VBP characteristics related to patient satisfaction and health equity, use of resources, and optimization of costs and quality of care.

PATIENT SATISFACTION/EXPERIENCE AND HEALTH EQUITY

The survey assessed the importance and feasibility of VBP characteristics related to patient satisfaction and health equity (questions 5 and 9). Findings from rounds 1 and 2 are summarized in eAppendix Table 2 and shown as prioritization matrices in eAppendix Figure 3.

Patient-centered care is the foundation of VBP, panelists emphasized, with patient goal prioritization significantly influencing outcomes and satisfaction. Shared decision-making was seen as essential for empowering patients, improving adherence, and enhancing quality of life. Panelists noted that variability in oncology—across disease states, treatments, and patient needs—makes effective risk adjustment and benchmarking challenging. Addressing disparities in access and outcomes was viewed as critical for equitable care, especially in underserved populations, highlighting the need to integrate SDOH into payment models. Symptom/pain management supports adherence and outcomes but requires resources like care coordinators. Panelists valued electronic patient-reported outcomes (ePROs) for tailoring care, although implementation can be difficult in small or rural practices. ePROs were seen as 1 element of patient-centered care, not a stand-alone goal.

RESOURCE USE AND COST OPTIMIZATION

The importance and feasibility of VBP characteristics related to resource use and cost optimization were assessed in survey questions 6 and 10. Findings from both survey rounds are summarized in eAppendix Table 3 and eAppendix Figure 4.

Panelists agreed that reducing acute care episodes supports cost containment, improves quality of life, and aligns with patient-centered goals. Overall health care spending was viewed as aligning more with payer priorities, potentially overshadowing quality and equity. Benchmark pricing was considered useful for setting financial targets, but it requires accurate risk adjustment to avoid penalizing practices for complex patients. Panelists emphasized distinguishing between short- and long-term costs, especially for high-cost treatments like immunotherapy and CAR-T therapy, which require up-front investment for long-term benefit, even if the treatment (eg, CAR-T therapy) has no subsequent treatment cost.

QUALITY OF CARE OPTIMIZATION

The importance and feasibility of VBP characteristics related to quality-of-care optimization were assessed in survey questions 7 and 11. Results from rounds 1 and 2 are summarized in eAppendix Table 4 and shown as prioritization matrices in eAppendix Figure 5.

Panelists agreed that clinical outcomes are the core goal of VBP, reflecting care quality and effectiveness. Gold carding was seen to reduce administrative burden for high-performing providers and support autonomy. Process measures help assess care delivery but may shift focus away from outcomes if overused. Structure measures ensure baseline standards, like staffing or infrastructure, but were viewed as less effective for driving quality, often functioning as checkbox requirements rather than indicators of care quality.

Analysis: Priority of Value-Based Payment Characteristics

Based upon the results of the second round of the survey, VBP characteristics were grouped based upon priority (Table 1). Among top priorities were patient-centered care, goal pursuit, and shared decision-making. Lowest priority was a narrowed focus upon included disease states.

Analysis: Level of Consensus and Key Areas of Agreement

In the second responses to the survey and subsequent analysis, panelists aligned philosophically, arguing that effective VBP models must satisfy certain fundamental requirements (Figure 3).These included a focus on patient-centered care and robust risk adjustment.

Additionally, panelists concluded that successful VBP models in oncology should incorporate a set of key elements (Figure 4).Among these were being specific to oncology and prioritizing patient outcomes.

Discussion

This modified Delphi process revealed that each VBP stakeholder has legitimate priorities and constraints that are often misaligned in VBP models implemented to date. Effective collaboration and model refinement are critical to balancing these competing priorities and overcoming these challenges. Priorities, constraints, and areas of compromise discussed by the panelists are summarized in Table 2.

Due to competing priorities, providers, payers, and other stakeholders believe they are competing against each other rather than striving toward the same goal, the panelists agreed. The most significant gap that panelists identified exists between providers and payers. Current risk-adjustment models are inadequate, panelists agreed, leading to unfair comparisons across providers treating different patient populations. Moreover, they continued, providers express a high willingness to take on execution risk—meaning practice risk associated with ensuring the delivery of high-quality care—but they have explicit concerns about prevalence risk—meaning the inability to control when highly acute and expensive patients present to the practice—which currently leads to inadequate reimbursement.

Finally, panelists made a concrete call to action to develop new VBP models in oncology by drawing on a first principle–driven oncology payment approach to collaborate with health plans. There was an acknowledgement of the complexities of modeling cancer overall, with high degrees of variation in clinical goals between cancer subtypes. This suggests the need to build an iterative model starting with 1 cancer subtype (eg, multiple myeloma, estrogen receptor–positive/progesterone receptor–positive breast cancer, or PD-1/PD-L1–high non–small cell lung cancer) in a research arrangement between an innovative payer and an oncology practice or practice network. Panelists noted that such an arrangement could foster trust between payers and providers through novel data-sharing arrangements to inform meaningful outcomes measurement and clinically based risk adjustment. An initial hold-harmless agreement in which practices have upside-only payment during the initial performance periods could instill confidence in the practice to take on increasing financial risk. Payers must consider the broader picture of long-term oncology care to avoid penalizing provider efforts, the panelists noted. They agreed that the path forward requires compromise, innovation, and alignment of incentives to make value-based oncology care truly sustainable and equitable.

Strategic Priorities for Future VBP Models

Panelists’ strategic priorities for future VBP models are summarized in Figure 5. Prioritizing multistakeholder value and patient outcomes is key, as is accountability for outcome measures.

Study Limitations

Study limitations included a small sample of panelists and an imbalance of providers versus payers. Moreover, patients were not included in the panel. The survey was relatively long, which may have impacted panelist responses, particularly to the final questions. Finally, after responding to the first round of the survey, panelists voiced disagreement about the definitions of certain terms used therein, which may have skewed numerical responses to the first round. As definitions were clarified during the discussion, the disagreement is unlikely to have impacted responses to the second round.

Although the panel was small, there was 100% participation for both rounds and discussion between rounds. Moreover, although panelists represented relatively large community practices, they voiced clear concerns about the impacts of VBPs on smaller practices.

Conclusions

This modified Delphi process sought to identify the essential characteristics of VBP models between payers and providers. Although the 8 VBP experts aligned philosophically and agreed that effective VBP models in oncology must satisfy certain requirements and ought to incorporate key elements, they noted challenges in current VBP models. These included misalignment between payers and providers on value definitions, with payers often prioritizing short-term cost savings over long-term outcomes, promoting competitive market dynamics, and rewarding high-value providers. The lack of standardized benchmarks also complicates fair risk adjustment and outcomes measurement, they argued. Moreover, small and rural practices face significant barriers due to limited resources, staffing shortages, and high up-front costs to transition from fee-for-service models.

The panelists also acknowledged the impact of external factors. Oncology care is changing, they noted, due to regulatory changes, aggressive payer policies, and innovative therapies (CAR-T therapy, personalized medicine), which may introduce high one-time costs and operational complexities. Moreover, an aging population, disparities in care, and workforce shortages add pressure to oncology practices.

Finally, the panelists noted, current payment models each carry benefits and drawbacks. Pay-for-performance, enhanced monthly payments, and capitated models each have benefits but fall short in addressing patient variability, administrative burdens, and equitable care. By contrast, cost-containment and single-/dual-sided risk models offer potential but are limited by challenges in risk stratification and feasibility for smaller practices.

Overall, the panelists acknowledged the shortcomings of current VBP models and offered productive suggestions—including prioritizing multistakeholder value and patient outcomes—to shape the future of VBP agreements.

Authorship Affiliation: Navista/Cardinal Health (MF), Dublin, OH; The US Oncology Network/McKesson (RH), The Woodlands, TX; OneOncology (AJL), Nashville, TN; Florida Cancer Specialists, LLC (KM), Fort Myers, FL; Oncology Consultants, PA (ST), Houston, TX; Thyme Care (LW), Nashville, TN; Harvard Medical School (MLW), Boston, MA; Tuple Health (ATY), Washington, DC.

Source of Funding: This supplement was sponsored by Johnson & Johnson.

Author Disclosures: Ms Henschel discloses employment by The US Oncology Network, as well as attendance at several VBC-based conferences including the Community Oncology Alliance Annual Meeting, the Community Oncology Alliance Payer Exchange Summit, and the Association for Value-Based Cancer Care Summit. Mr Lyss reports consultancies or paid advisory boards for Bristol Myers Squibb and Gilead Sciences, Inc. He also reports employment by and ownership of stock in OneOncology. Ms Mehring reports employment at Florida Cancer Specialists, LLC. Dr Wilfong reports employment at Thyme Care.

Mr Fazio, Ms Trivedi, Dr Witkowski, and Dr Yue have no relevant commercial financial relationships or affiliations to disclose.

This supplement was developed based on the findings of a Johnson & Johnson–sponsored roundtable event. Mr Fazio, Mr Lyss, Ms Mehring, Ms Trivedi, Dr Wilfong, Dr Witkowski, and Dr Yue were compensated for their time and participation in the roundtable discussions but did not receive honoraria for their contributions to the development, writing, or preparation of this article.

Authorship Information: Concept and design (MF, RH, KM, LW, MLW); acquisition of data (MF, RH, ATY); analysis and interpretation of data (AJL, KM, LW, MLW); drafting of the manuscript (MF, RH, KM, LW, ATY); critical revision of the manuscript for important intellectual content (AJL, AY, KM, ST, LW, MLW, ATY); provision of study materials or patients (LW); administrative, technical, or logistic support (ST, LW); supervision (LW).

Acknowledgments: The authors would like to acknowledge Samuel Stolpe, PharmD, MPH; Tao Ran, PhD; Zhongyun Zhao, PhD; and Jamie Powers for their contributions to the concept development and methodology of this study. Under the direction of the authors, medical writing support and editorial assistance were provided by Erin Garrow, PhD, of MJH Life Sciences^®.

Address Correspondence to: Lalan Wilfong. Address: 5521 Pebblebrook, Dallas, TX 75229. Email: Lalan@thymecare.com

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