The New Oncology Blueprint: Integrating Clinical Intelligence for Sustainable Value

ABSTRACT

Oncology is one of the most exciting yet most challenging areas of health care. Scientific advances in immunotherapies, targeted agents, and cell and gene therapies are transforming survival, but costs are rising just as quickly. Global oncology spending is projected to exceed $400 billion by 2028, with more than half concentrated in just 5 tumor types. These dynamics present a critical challenge for payers—pharmacy benefit managers and health plans—to sustain access to innovation while protecting the long-term viability of the health care system.

Traditional tools such as step therapy, prior authorization, and formulary tiers, although important in many areas of care, struggle to keep pace with oncology, where treatment is adaptive, evidence evolves rapidly, and care decisions are highly personalized. Fragmented benefits and siloed data further obscure the total cost of care and hinder coordination across the patient journey.

This article outlines a sustainable path forward: a systems-level blueprint that places patient and system outcomes at the center, supported by clinically intelligent utilization management tools, aligned incentives across benefits, whole-patient support, and collaborative partnerships. Together, these levers create a unified framework that enhances patient experience while enabling payers and providers to focus on value, not volume.

Am J Manag Care. 2026;32(7):In Press

Takeaway Points

Oncology drug costs are projected to reach $400 billion globally by 2028, straining employers, payers, and health systems. This article proposes a 5-lever framework to unlock value in oncology drug management by moving beyond traditional tools to align cost, access, and measurable patient outcomes.

• Total cost transparency: Mandate benefit and data integration (medical, pharmacy, electronic health records) to reveal the true cost of care and manage fiduciary risk.
• Policy evolution: Implement clinically intelligent utilization management tools to reduce administrative delays and enable personalized treatment sequencing.
• Whole-patient strategy: Integrate patient risk factors (eg, social determinants of health) for proactive support that reduces expensive, avoidable acute care utilization.
• Contract and value: Engage early with biopharma and align payments with measured patient outcomes, such as treatment durability, not procedure volume.

Oncology is easily one of the most dynamic areas in medicine today. Immunotherapies, precision treatments, and cellular therapies are redefining patient journeys. In 2023, more than 2000 new oncology trials were launched, exploring new treatment approaches such as cell and gene therapies, antibody-drug conjugates, and multispecific antibodies.¹

However, this rapid scientific progress is accompanied by growing financial pressure. Global oncology spending is rising by 13% to 15% annually and is projected to exceed $400 billion by 2028.¹ A closer look at this projected spend reveals that more than half is concentrated in just 5 tumor types²: breast, non–small cell lung cancer (NSCLC), prostate, multiple myeloma, and kidney cancer, each with unique cost drivers. NSCLC spending, for instance, is propelled by targeted therapies and immunotherapy combinations, whereas high prevalence and long treatment durations drive breast cancer costs.²

In addition, the oncology drug landscape is evolving unevenly. Certain classes, such as biomarker-driven therapies, cellular therapies including chimeric antigen receptor (CAR) T-cell therapy, and checkpoint inhibitors (PD-1/PD-L1), are fueling exponential cost growth, whereas others lag in innovation. This uneven growth pattern creates an opportunity for targeted innovation to meaningfully influence these cost trajectories.

These dynamics pose a critical challenge in sustaining access to innovation while protecting the long-term viability of the health care system. Succeeding here will require bold, data-driven solutions, offering a unique opportunity for payers (pharmacy benefit managers [PBMs] and health plans) to lead
the way.

External pressures make this challenge urgent, especially because self-funded employers now face fiduciary obligations to align benefit performance with patient outcomes. Furthermore, the political environment has led to state and federal actions aimed at limiting PBM restrictions and reducing administrative barriers, such as prior authorization, to expedite patient access. This scrutiny reinforces the call for solutions that are not merely administrative but also clinically intelligent decision-support systems that reduce delays and enable proactive care management.

Before we can reimagine oncology management, we need to examine the cost containment tools we have long depended on to understand both their limitations and their potential to evolve into stronger, more effective strategies.

The Limits of Traditional Utilization Management Tools: Rethinking Oncology’s Unique Needs

For years, the industry has leaned on traditional utilization management (UM) tools such as prior authorization, step therapy, and formulary restrictions to promote cost-effective care.³ These tools apply static clinical criteria, are largely retrospective, and are designed to manage broad populations. They have been effective in supporting fiduciary oversight for chronic conditions with relatively linear treatment pathways. Oncology, however, is different. The treatment journey for a patient with cancer is often adaptive and nonlinear, driven by biomarkers, rapidly evolving evidence, and individualized care decisions. This complexity, combined with the accelerating pace of innovation, calls for UM tools that can apply agile, biomarker-based criteria to support personalized treatment, reduce delays, and enable proactive care management.

The disconnect between traditional UM and the realities of oncology care results in coverage decisions that lack essential clinical context. This not only delays access but also fuels provider frustration, erodes trust, and draws increasing political and regulatory scrutiny.⁴

But this mismatch also presents an opportunity to reimagine UM through modern infrastructure powered by explainable artificial intelligence (AI) and machine learning, or next-generation UM.⁵ A redesigned UM model, grounded in real-time data and clinical transparency, can reduce administrative burden, improve outcomes, and align cost with care more effectively.

Although rethinking UM is essential, it is only one part of the equation. To fully unlock value in oncology, we must also confront a deeper structural issue: fragmentation across data systems and benefit designs.

Breaking the Barrier: Addressing Fragmentation to Unlock Oncology Value

A deeper look at projected oncology spending reveals 2 primary forces driving costs at the macro level: innovation-driven costs and system inefficiency–driven costs.

• Innovation-driven costs are the intended result of scientific progress: expensive breakthrough treatments, expanded indications, biomarker-driven populations, and a limited competitive pipeline of biosimilars.⁶ These dynamics fundamentally reshape treatment pathways, challenge existing benefit strategies, and necessitate a new approach to managing the claim cost mix and overall spend.
• System inefficiency–driven costs, by contrast, are the unintended result of how we manage innovation: misaligned incentives between third-party administrators and the plan’s fiduciary needs, siloed medical/pharmacy benefits, and fragmented data systems that cannot “talk” to each other to support coordinated care.

The most persistent system inefficiency is the misalignment of benefit structures. Oncology drugs span both medical and pharmacy benefits, yet diagnostic testing, infusions, oral therapies, and supportive medications are often managed separately. This fragmentation fuels misaligned incentives, drives waste, disrupts care coordination, and delays access while also limiting the ability to manage total cost of care. Fragmentation in care delivery has been shown to increase avoidable costs and worsen outcomes.⁷ A similar dynamic occurs when misaligned benefit structures disrupt coordination in oncology.

Equally concerning is the fragmentation of data. When clinical and claims information is siloed across payers, providers, and PBMs, it compromises the fidelity of trend analysis and obscures the true cost of care, which can have unintended consequences on data-driven decision-making. In short, fragmentation is a foundational issue that must be addressed. Unlocking oncology value will require some degree of benefit integration and the creation of unified data platforms that enable a clearer, more coordinated view of care.⁸

Addressing fragmentation and modernizing UM are critical first steps to unlocking oncology value. Creating lasting impact will require a broad strategy rooted in coordination, collaborative partnerships, and patient-centered care.

Driving Innovation: The Blueprint for Transforming Oncology Care

A sustainable path forward for oncology requires a systems-level blueprint that places patient and system outcomes at the center, supported by clinically intelligent UM tools, aligned incentives across benefits, stronger accountability, and payment models that tie reimbursement to demonstrated clinical benefit.

This blueprint is based on 5 levers, culminating in a unified value-based ecosystem (Figure⁹).

1. Data Integration

At the core of transforming oncology care is the need to bridge the most challenging information gaps that limit clinical and financial decision-making. Integrated platforms, despite requiring significant structural commitment, will create a unified view of the patient, where comprehensive medical, pharmacy, electronic health record (EHR), and real-world evidence data form the foundation for real-time analytics. These insights can uncover hidden cost drivers, strengthen treatment decisions, and support smarter care delivery. With real-time analytics, payers and providers can identify emerging trends, spot avoidable health care resource utilization earlier, and enable more coordinated, proactive management.

2. Next-Generation UM Tools (Clinically Intelligent UM)

As discussed earlier, traditional UM is rigid and population based by design. It assumes that patients with a given diagnosis follow the same pathway until proved otherwise. This is a clear mismatch with oncology, where treatment is adaptive, nonlinear, and highly personalized.

To better support this complexity, UM must evolve into a real-time clinical support model that uses appropriately governed algorithms and real-world data to guide decisions earlier in the treatment journey. Such tools could help answer complex questions in modern oncology, including the following:

• Duration management: identifying candidates for time-limited therapy (eg, fixed-duration venetoclax) instead of indefinite approaches such as chronic Bruton tyrosine kinase (BTK) inhibition
• Early signal detection: combining EHR and claims data to flag relapse, toxicity, or nonresponse sooner and support real-time prior authorization
• AI-supported sequencing: anticipating optimal treatment order, such as shifting from BTK inhibitor therapy to CAR T-cell therapy in chronic lymphocytic leukemia
• Site-of-care and benefit alignment: supporting transitions from hospital to home infusion and harmonizing medical and pharmacy criteria

The goal of next-generation UM is to strengthen clinical context, preserve judgment, and reduce the administrative steps that delay care (Table). When UM reduces unnecessary steps and avoids inappropriate regimens, patients start therapy sooner, providers experience less administrative burden, and payers avoid downstream complications that drive up the total cost of care.

3. Whole-Person Support

Oncology patients often face interconnected barriers such as financial strain, transportation difficulties, symptom burden, and emotional distress. These factors compromise treatment adherence and outcomes. Whole-person support must therefore be intentional and data driven. Advanced UM tools and predictive risk models can proactively identify patients at risk of treatment disruption, adverse effect escalation, or toxicity. By replacing broad care management with precision-targeted interventions that address specific drivers, payers can improve clinical stability, enhance patient experience, and reduce avoidable acute care utilization. When integrated with social determinants of health data, this support becomes even more predictive and equitable, enabling earlier identification of patients at risk for deterioration or disengagement.

4. Collaborative Partnerships

The goal of collaborative partnerships is to align stakeholders around shared objectives such as reducing low-value care, improving evidence-based regimen selection, and increasing visibility into total cost of care. In a landscape where 71% of innovation originates from emerging biopharma,² payers have an opportunity to shift from reacting at launch to engaging prelaunch. Early collaboration is especially critical because many smaller biopharma companies lack the infrastructure for robust market access strategies. By engaging earlier, payers can help shape access frameworks and pricing expectations that balance innovation with sustainability for their members.

Collaborative partnerships can also extend to payers and providers. When pivotal trials leave practical questions unanswered—such as optimal therapy duration, treatment sequencing logic, or discontinuation thresholds, all with cost implications—these partners can generate answers through shared real-world evidence. This turns variability into evidence and evidence into alignment. Data sharing should also advance equity, ensuring that innovations reach patients without systemic disparity.

5. Value-Based Care

The systemwide payoff when the first 4 levers align is value-based care. Data integration, intelligent UM, whole-patient support, and proactive collaboration create the foundation for a learning system where value can finally be measured and rewarded. As Wait et al emphasize, sustainable oncology care depends on reducing inefficiencies, focusing on interventions that deliver patient-relevant outcomes, and aligning payment with measurable benefit.⁸ By integrating data and decision-making, payers can tie reimbursement directly to validated end points such as response durability, toxicity reduction, or minimal residual disease status.¹⁰

Reframing the Narrative

Oncology drug spending is rising at an unprecedented 13% to 15% compound annual growth rate,¹ driven by novel therapies, expanded indications, and limited biosimilar competition. Siloed benefit and data systems create misaligned incentives that hinder effective management of oncology care, a field whose complexities demand coordinated, patient-centric solutions.

Unlocking sustainable value requires a bold path forward: Integrate data to build next-generation UM tools that power whole-person support, strengthen credibility through collaborative partnerships, and ultimately create a unified value-based care ecosystem. By embracing this clinically intelligent blueprint, we can redefine oncology care, moving the focus from volume to measurable, patient-relevant outcomes, ensuring that patients and providers thrive, and enabling payers and employers to be confident they are paying for sustainable value rather than volume, unlocking the future of oncology care.

Acknowledgments

The author thanks Giselle Ebot Lacey, MSF, FPQP, for providing editorial review to improve the clarity, accessibility, and overall readability of the manuscript.

ChatGPT was used to assist with readability. All content was drafted, reviewed, and verified by the author.

Author Affiliation: MVT Health Consulting.

Source of Funding: None.

Author Disclosures: Dr Tar is the founder and principal consultant of MVT Health Consulting, an independent practice providing strategic advisory services to health care organizations. Through MVT Health Consulting, she provides consulting services to Advantage Point Solutions. Dr Tar previously served as a member of the Expion Health Pharmacy and Therapeutics Committee. Dr Tar has received honoraria from the Academy of Managed Care Pharmacy, Advanced Topics for Oncology Pharmacy Professionals, Pharmacy Times^®Continuing Education, and The American Journal of Managed Care, and has served on advisory boards for Vertex, Autolus, and Bayer.

Authorship Information: Concept and design; drafting of the manuscript; and critical revision of the manuscript for important intellectual content.

Address Correspondence to: Vivian T. E. Tar, PharmD, MBA, MVT Health Consulting. Email: VivianTar@mvthealthconsulting.com.

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