The next era of cancer care

The coming proliferation of predictive data, algorithmic screening, and molecular diagnostics will create a massive new healthcare segment: The pre-symptomatic patient with an elevated or unresolved cancer risk. Leading organizations will move beyond broad age-based screening guidelines and use clinical history, inherited risk, imaging, laboratory data, and other signals to identify who requires more intensive monitoring or intervention.

Proactive individualized assessment and detection will surface needs earlier. Genetic risk assessment will increasingly extend beyond family history to polygenic scores that combine hundreds of variants to create an individualized view of susceptibility. AI-enabled imaging will review scans obtained for unrelated reasons and surface incidental nodules and other findings that traditional screening programs may miss. Direct-to-consumer channels, including whole-body MRI and broad genetic testing, will continue to generate findings that patients bring back into the traditional healthcare ecosystem.

Blood-based technologies will further accelerate the shift. Multi-cancer early detection (MCED) tests and single-tumor blood assays already reach consumers through self-pay, employer, and concierge channels, often moving ahead of clinical consensus and broad insurance coverage.² Although the ultimate role of population-wide MCED screening remains uncertain, these tests will create a growing number of patients with possible cancer signals but no known tumor origin.

The same dynamic will reshape the post-treatment period. Measurable residual disease (MRD) testing will search for microscopic evidence that cancer remains or has returned before it appears on conventional imaging. As MRD becomes clinically actionable in more solid tumors, survivorship care will evolve from periodic follow-up into more continuous molecular surveillance. Each blood draw will become another potential point of re-entry into active cancer care.

[IMAGE]

These new signals will emerge in an oncology market where traditional referral channels have already consolidated. More than 80% of US physicians are now employed by hospitals or corporate entities, and many large integrated delivery networks (IDNs) have assembled increasingly self-contained ecosystems across primary care, imaging, surgery, medical oncology, and acute care.³ In many markets, referrals are shaped not only by individual physician preference but also by enterprise scheduling systems, electronic health record (EHR) order pathways, employment relationships, internal access centers, and network integrity expectations.

The pre-diagnostic market also will create channels outside the traditional physician referral system. Employer health plans, concierge medicine groups, radiology aggregators, consumer health companies, genetics platforms, and digital screening programs will need trusted clinical partners to interpret and resolve the signals they generate. Some relationships will resemble conventional referral arrangements. Others will become formal distribution agreements in which cancer providers commit to rapid, protocolized diagnostic workups.

Payers may also become more active channel participants. Historically, health plans have been cautious about steering patients after a cancer diagnosis when clinical complexity, physician trust, and patient sensitivity are highest. Earlier in the journey, before a treatment relationship has been established, they may have greater latitude to influence screening, diagnostics, second opinions, network selection, and site of care. Rising cancer costs will strengthen their incentive to do so.

We believe the next decade will layer a new signal-driven channel system on top of an increasingly consolidated physician landscape. Traditional channels will become more organized, while competing channels emerge around the first indication of a malignancy.

By 2035, channel advantage will be defined by proximity to the first credible cancer signal, the ability to convert that signal into a timely and trusted answer, and the capacity to maintain the patient relationship across every stage of their care journey. For cancer providers, succeeding in this environment will demand three critical shifts:

• The confirmed diagnosis will become a lagging indicator of market share. By the 2030s, the risk-identification, diagnostic, and surveillance window will influence a growing share of oncology volume. Patients enrolled in multi-year monitoring for a nodule, an elevated polygenic risk score, or an annual blood-based screen will be more likely to remain within the ecosystem if treatment becomes necessary.

  Dedicated MCED clinics at leading institutions such as Dana-Farber and Duke offer an early view of this model, creating a defined destination for patients with a positive test but no known tumor origin.⁴ Cancer programs that continue to organize growth solely around the already-diagnosed patient will find more of their traditional referral base committed upstream. 

• Referral strategy will evolve into channel architecture. Individual physician relationships will remain important, but they will become part of a broader set of designed pathways spanning primary care, imaging, genetics, employers, payers, and consumer platforms. Leading cancer enterprises will define how external signals enter the system, who assumes responsibility for resolving them, how quickly a patient receives an answer, and where the downstream care is directed. The defining competitive advantage will be closed-loop diagnostics, not the strength of referral strategy.

• Channel strategies will diverge by provider type. IDNs will begin with a structural advantage through owned primary care, imaging, specialty networks, and longitudinal patient relationships. But they must overcome internal silos to identify, coordinate, and retain this emerging pre-cancer patient cohort. 
  
  Academic medical centers (AMCs) and freestanding cancer hospitals without captive upstream channels will face the greatest risk of disintermediation. They will need to build channel partnerships while leaning into their ability to resolve ambiguous signals and manage complex hereditary risk. To that end, we are already witnessing an unprecedented wave of cross-tier alignment—with AMCs forging regional alliances and joint ventures like UCSF Health and John Muir Health, Moffitt Cancer Center and Memorial Healthcare, and Michigan Medicine and Trinity Health.

  Scaled oncology platforms will be able to leverage their operational agility and standardized pathways across hundreds of community sites, positioning themselves as the turnkey, low-cost delivery partners for payers, national employers, and consumer health companies managing early cancer interception programs. 

In the 2030s, competitive advantage will not belong to the institutions that try to own every asset but to the enterprises that orchestrate the most connected ecosystems across payers, employers, community oncologists, and consumer health platforms.

The next decade of oncology will be defined by speed of discovery and clinical translation. AI models are increasingly integrated across the drug-development lifecycle—predicting biomolecular interactions, narrowing drug targets, and supporting molecule design. Simultaneously, digital platforms are accelerating parts of clinical development and regulatory review.^5,6 AI platforms will not replace the clinical validation required to prove safety and efficacy. But they could meaningfully shorten the path from discovery to approval by accelerating preclinical work, improving patient identification, and enabling more adaptive trial design.

By 2035, the frontier of cancer science is likely to produce a much broader therapeutic arsenal: 

• Antibody-drug conjugates (ADCs) and next-generation targeted pills will displace portions of traditional chemotherapy
• Bispecific and multi-specific antibodies will make immune redirection more scalable and relevant in solid tumors
• Cell therapies will extend beyond their foothold in liquid tumors
• Radiopharmaceuticals will become a more established pillar of precision treatment. 

Collectively, oncology will be reshaped by a larger, faster-moving, and increasingly combination-driven portfolio of these therapies—reaching broader patient populations and earlier stages of disease.

The historical pattern of innovation beginning in a small number of AMCs and diffusing slowly into community practice will compress as protocols mature, toxicity management improves, and therapies become easier to manufacture and administer. Even so, each modality will bring distinct requirements, from biomarker testing and tissue handling to pharmacy operations, isotope logistics, adverse-event monitoring, and rescue capability. The ability to translate innovation into routine care will therefore become a defining source of advantage—favoring organizations that can bring new therapies to patients quickly, safely, reliably, and at scale.

Innovation adoption will need to occur across a redesigned chassis, designed around a bimodal cancer network. Despite early-2020s predictions that oncology would fully migrate to the outpatient setting, billions of dollars have been committed over the past 5 years to specialized inpatient cancer hospitals. This acute, high-complexity capability remains a durable requirement.⁷ 

Moving into the 2030s, leading enterprises will run a dual architecture, with distributed sites for protocolized care, tethered to advanced hubs for early-phase trials, intensive cellular therapies, and complex surgical oncology. Because the supply of subspecialized oncologists cannot meet demand, expertise will be embedded in the network through AI-enabled decision support, remote molecular tumor boards, and virtual consults.

Importantly, as cancer science becomes more complex, the patient experience must become demonstrably simpler. Historic reliance on reputation will fail to overcome fragmented coordination or provider-centric operations. To secure loyalty in the 2030s, the cancer experience must improve how patients access the system and how that system supports them when they leave the building. 

Programs that can quickly schedule patients with the right provider at the right time will build a durable access advantage. Those that extend infusion, imaging, and radiation hours will unlock demand that traditional schedules leave unserved. The metric of success will shift from traditional throughput measures (e.g., "days to first consult") to whether the patient received “the appointment that they wanted” (as Memorial Sloan Kettering now asks). 

Second, as next-generation therapies shift care from the clinic to the home, the most critical moments in the cancer journey will occur between clinical visits. A patient receiving a bispecific antibody (BsAb), ADC, or oral targeted therapy may spend only a fraction of their month in the clinic, while managing fatigue, immune toxicity, and anxiety at home. 

The most successful programs will deploy remote symptom monitoring, proactive digital triage, and dedicated oncology urgent care. By explicitly formalizing this between-visit programming, providers will reduce avoidable emergency department visits, make complex therapeutic regimens more manageable for patients, and deliver higher-value outcomes.

The practical implication is that growth will require building systems that can rapidly absorb clinical innovation, intelligently route based on complexity, and serve patients on their terms. For cancer providers, this new growth formula will demand four operational mandates:

• Adoption speed will be a new competitive currency. The pipeline of complex therapeutics will demand a disciplined process for scanning the horizon, assessing site readiness, and standardizing clinical and financial pathways. The historical model of relying on isolated physician enthusiasm to champion new treatments will be too slow and risky. 
  
  Cancer center leaders must build the infrastructure to safely and rapidly deploy unfamiliar therapies and manage unique toxicities[AF2]  across multiple, diverse sites of care. Some organizations will choose to minimize this complexity risk through strategic partnerships. We are already seeing the emergence of outsourced operational models, ranging from cell therapy “in a box" platforms and multi-institution consortiums to turnkey early-phase research networks, such as START, that provide infrastructure for Phase 1 trials.
• Cancer networks will need to design around complexity, not just geography. A successful bimodal network will require clear, enforceable guidelines for what care can safely remain in the community, what requires a virtual subspecialty consult, and what must be escalated to an advanced hub. This balance of keeping tertiary sites reserved for high-acuity interventions while safely maximizing the capabilities of distributed sites will become a competitive advantage. 
• Patient preference will redefine access. Historically, cancer centers measured oncology access as a throughput puzzle with rigid 8-to-5 weekday schedule constraints. The successful platforms of the 2030s will deconstruct these constraints and compete aggressively on schedules that accommodate caregivers and patients, such as evening imaging and weekend infusion. MD Anderson has already moved in this direction, adding once-a-month Saturday clinics, infusion, imaging, and surgery options for patients. 
  
  Market share will shift to organizations that prioritize getting patients the appointments they need for their circumstances, rather than defaulting to the first available oncologist. Leading programs will also give patients more agency through digital care plans, preference-capture tools, self-scheduling tools, and clear visibility into areas of shared decision-making.
• “Between-visit” interventions will become standard of care. As next-generation therapies shift the burden of toxicity management from the infusion chair to the home, the most critical moments of the cancer episode will occur outside the clinic walls. 
  
  Market leaders will redesign workflows to manage this experience—drawing from the extensive, established track record of scaled oncology platforms. They will invest in virtual care coordination, electronic patient-reported outcomes, proactive symptom triage, and dedicated oncology urgent care. In the process, programs will protect their economics, secure payer advantages, and reduce avoidable emergency visits.

Clinical innovation of the 2030s will be remarkably expensive and will collide with an ecosystem focused on affordability. Policymakers and payers are aggressively targeting US cancer spend, which is forecasted to reach $246 billion annually by 2030. The Inflation Reduction Act’s expansion into Part B drug negotiation and Part D redesign will compress pharmaceutical net pricing and shift financial liabilities, incentivizing payers to manage high-cost oral oncolytics more strictly through prior authorization and preferred specialty pharmacies. 

Concurrently, Medicare Advantage now covers more than half of eligible Medicare beneficiaries—a dynamic our colleagues at HealthScape Advisors carefully track. As a result, providers will face unprecedented utilization management and site-of-care steering. Combined with the Centers for Medicare & Medicaid Services’ (CMS's) push toward site-neutral payments and steep proposed cuts to 340B-acquired drugs, the traditional hospital-based premium margin will face structural erosion.⁸

Over the next decade, value-based oncology will likely have moved beyond a limited demonstration model, though it is unlikely to replace fee-for-service payment for cancer care. CMS’s Enhancing Oncology Model offers an early blueprint, in which practices receive care management payments, assume downside risk for 6-month episodes, and are accountable for quality and total cost. In its first reconciliation period, 79% of participants generated savings relative to historical spending, and more than half earned the highest quality bonus.⁹ The model is scheduled to run through June 2030.

The private market is moving in parallel. Starling Oncology (formerly The Oncology Institute) is expanding delegated capitated arrangements and reported 2.1 million lives under value-based contracts in mid-2026. Meanwhile, Thyme Care offers partnerships in which it assumes downside risk and shares savings with oncology practices.¹⁰ By the mid-2030s, we expect a hybrid model in which fee-for-service remains central to the business, while care-management payments, shared savings, downside risk, and selective delegation expand in select markets and plans.

[Add call-out box to HC360 piece: 
Healthcare’s financing evolution: Today’s novel models are learning labs
A single paradigm shift will not define the future of healthcare financing. But significant early trends are in motion as providers, payers, employers, and others experiment together.]
 

As the margins on infused drugs narrow, the oncology profit pool will distribute across more of the care continuum. Sustaining the economic engine will require capturing value beyond the infusion suite through specialty pharmacy, advanced diagnostics, radiopharmaceuticals, MRD-led surveillance, and complex-care premiums. 

Sophisticated capital has already preempted this shift, with major drug wholesalers like Cardinal, Cencora, and McKesson deploying billions of dollars in recent years to consolidate independent oncology networks and secure outsized control over these revenue streams.

[IMAGE]

To defend and rebuild margin in the 2030s, cancer enterprises must execute three strategic mandates:

• Broaden participation in the oncology margin portfolio. Infused therapy will remain economically critical but will become less dependable as the sole margin strategy. Providers will need to participate in the growing economics around specialty pharmacy and oral oncolytics, advanced diagnostics, clinical trials, theranostics, and molecular surveillance. This participation could take different forms, including ownership and partnership. 
  
  Not every portfolio category will be highly profitable on its own. Some will drive patient acquisition, therapy selection, and preservation of the longitudinal patient relationship. Some others will enable downstream value. 
• Hospital-based programs must develop a non-premium parallel chassis. Hospital-based programs should defend premium economics for care that truly requires premium infrastructure, including complex surgery, immune-effector therapy, high-risk infusion, theranostics, early-phase trials, and inpatient rescue. Routine protocolized oncology will face growing site-neutral and payer pressure. 
  
  Rather than defend all services in the hospital outpatient setting, health systems should prepare lower-cost ambulatory options within their networks or through partnerships. The strategic objective is not to abandon 340B or provider-based advantage prematurely but to remain competitive if those advantages narrow and simultaneously preserve premium reimbursement where it is clinically defensible.
• Build the capabilities to succeed in a more accountable market. Fee-for-service will remain the economic engine of oncology. But as payers increasingly emphasize total cost, cancer providers will need stronger capabilities in historically underfunded areas, such as navigation, symptom monitoring, treatment adherence, pathway management, early palliative care, and episodic-level analytics. 
  
  Investing in these capabilities can create strategic flexibility by improving quality, experience, and continuity within the existing model—while positioning organizations to participate if more accountable payment models enter the market.

This operational reckoning is driven, in part, by the cancer care supply-and-demand reality: The oncology workforce math no longer computes. American Society of Clinical Oncology’s (ASCO’s) recent data reveals an alarming decline in the density of oncologists relative to an aging population, noting that 68% of older Americans live in counties where oncologist coverage is at risk.¹¹ 

Simply recruiting their way out of a capacity constraint will no longer be a viable strategy for cancer programs. Adding to this challenge, the oncology team’s work is compounding, with demands from oral therapy adherence, immune toxicity surveillance, MRD monitoring, symptom triage, and financial navigation. 

[IMAGE]

A central response to mounting workforce and economic pressure will be embedding AI into the daily operations of cancer care. Early deployments have largely focused on administrative burden, including tumor registry abstraction, chart summarization, prior authorization, coding, and scheduling. Those areas will remain important, but the next phase will be more consequential as AI augments clinical judgment while coordinating an increasingly complex cancer journey.

On the clinical side, AI is already emerging as a peer reviewer in radiology and pathology. Large-scale studies are demonstrating the potential of AI-supported interpretation in areas such as mammography and lung-nodule detection.¹² By the 2030s, multimodal models will increasingly synthesize imaging, pathology, molecular findings, treatment history, and rapidly changing evidence. They can then identify testing gaps, surface relevant trials, anticipate toxicity, and inform treatment choices. 

The aim is not to replace oncologist judgment but to help clinicians interpret more information, apply new science more consistently, and spend less time assembling the information required to make a decision.This is a call-out, not a heading. Bold may be sufficient, but feel free to apply another design treatment.

AI may be equally transformative as the orchestration layer surrounding the patient. It can help patients and caregivers understand the care plan, sequence appointments, manage oral therapies, report symptoms, and know when and where to seek help. It can also incorporate individual preferences into scheduling and site-of-care decisions, giving patients greater visibility and agency while escalating clinically or emotionally consequential issues to the care team. 

As Douglas Flora, MD, emphasized in dedicating his incoming Association of Cancer Care Centers presidency to AI in oncology, this technology will comprehensively reshape the cancer care ecosystem.¹³ 

None of these capabilities will create capacity automatically. Their value will depend on thoughtful workflow redesign, integration with existing systems, clear escalation rules, governance, and adoption discipline. The defining measure of success will be whether AI removes work, closes coordination gaps, and creates more time for meaningful human care, rather than adding another layer of friction.

Moving into the 2030s, the defining operational question will be how cancer programs can embrace a new operating discipline while expanding human capacity. Three mandates will be key:

• Operating discipline will become inseparable from the clinical value proposition. In the 2030s, a program's clinical reputation will be deeply intertwined with its operational execution. Patients will judge programs by how smoothly they coordinate care and how rapidly they resolve symptoms. Payers will judge value on pathway adherence and avoidable care utilization. Physicians will judge whether the operating model allows them to practice cancer medicine rather than fight administrative friction.

• Care redesign must target top-of-license preservation. Programs can no longer afford to have subspecialized providers performing routine tasks. Cancer centers must deploy digital tools to offload documentation, clinical data retrieval, and routine follow-ups, shifting nonclinical work to technology or allied team members so providers can focus on disease management. 

  Redesign cannot stop at administrative relief—it will need to integrate AI as a clinical co-pilot, equipping providers with decision support for complex treatment planning, real-time trial matching, and many other domains that elevate the accuracy and confidence of human clinical judgment.

• Efficiency must protect human moments by design. The counterintuitive truth of the AI-enabled care model is that the technology is not about removing the human element from oncology but fiercely defending it. By stripping away administrative tasks, cancer programs can protect clinicians' time for the moments of consequence—delivering a diagnosis, navigating complex shared decision-making, managing toxicity de-escalation, and guiding deeply personal goals-of-care discussions.