A Turning Point for Community Oncology: Using AI to Deliver More Connected, Personalized Care

by Alti Rahman, AON chief strategy and innovation officer and COA board member

Artificial Intelligence is no longer a distant concept in healthcare. It’s actively reshaping how care is delivered, experienced and measured both in office and out of office. And nowhere is this shift more visible than in community oncology, where innovation must balance cutting-edge treatment with deeply personal, longitudinal patient relationships.

That tension between scale and personalization, efficiency and empathy, is exactly what makes this moment so pivotal.

When the oncology community gathered for this year’s COA 2026 Community Oncology Conference, themed Innovation in Practice, in April, industry leaders and speakers were not simply asking whether AI belongs in cancer care. The more urgent question was: how do we deploy it in a way that meaningfully improves the patient experience while strengthening the sustainability of community-based care?

Moving Beyond Automation to Intervention

Much of the conversation around AI in healthcare has centered on documentation – ambient listening tools that reduce administrative burden and give clinicians more time with patients. That progress matters. Time is one of the most valuable resources in oncology.

But documentation is just the starting point.

The real opportunity lies in using AI not just to record the patient journey, but to actively shape it. This means identifying risks earlier, anticipating care gaps, and guiding decisions in real time, often before a clinician or patient would otherwise recognize the need.

Some organizations are beginning to move in this direction by connecting data across the full continuum of care, capturing clinical encounters, analyzing longitudinal patient journeys, and using those insights to anticipate what comes next. It’s an evolution from reactive care to proactive intervention.

The Rise of the Intelligent Care Continuum

What is emerging is a shift from isolated tools to integrated intelligence.

Forward-looking organizations are building AI-enabled ecosystems that connect clinical encounters, operational workflows, and patient outcomes into a single, continuous feedback loop. In this model, every interaction contributes to a more complete understanding of the patient, not just in the moment, but over time.

This kind of approach is already taking shape in parts of the community oncology landscape, where integrated data strategies are being paired with analytics to support more informed decision-making, reduce administrative friction, and better align care with both outcomes and cost.

This isn’t about layering technology on top of existing processes. It’s about rethinking the care model itself.

For patients, the impact is tangible: care that feels more connected, more personalized, and more responsive to their needs.

Data Integrity Will Define Success

As AI capabilities expand, one reality becomes increasingly clear: the quality of the output is entirely dependent on the quality of the input.

In oncology, where decisions carry life-altering consequences, data integrity is not a technical detail, it’s a clinical imperative.

The industry is still grappling with fragmented data sources, inconsistent standards, and questions around reliability. Even widely used datasets can introduce risk if they are incomplete, altered without transparency, or lacking context.

That’s why some organizations are investing in more unified, internally validated data environments – bringing together real-time clinical data, treatment patterns, and outcomes to generate insights that clinicians can trust and act on with confidence.

Those who get this right won’t just adopt AI, they’ll trust it.

Building the Foundation for Value-Based Oncology

AI also has a critical role to play in the broader shift toward value-based care.

By connecting real-time clinical data with longitudinal outcomes, AI can help providers better understand what works, for whom, and under what circumstances. That insight is essential for aligning with payers, reducing unnecessary variation, and delivering more predictable, high-quality care.

Equally important, it can help remove administrative barriers that often delay treatment, creating a more seamless experience for both providers and patients. Emerging care models are beginning to reflect this shift, using data and analytics to enable more coordinated, accessible, and affordable cancer care.

The result is a model that is not only more efficient, but more humane.

A Defining Moment for Community Oncology

The conversations about innovation in community oncology happening at this year’s Community Oncology Alliance 2026 Community Oncology Conference reflected a broader inflection point for the field.

AI is no longer an innovation on the horizon. It’s becoming embedded in the daily realities of oncology practice and influencing how clinicians make decisions, how organizations operate, and how patients experience care.

The organizations that lead in this next phase will not be those that simply adopt new technologies. They will be the ones that thoughtfully integrate AI into the fabric of care delivery guided by high-quality data, aligned with clinical expertise, and always centered on the patient.

Community oncology has always been defined by its ability to deliver personalized, high-touch care close to home. With the right approach to AI, one that combines technology, data integrity, and clinical insight, it now has the opportunity to do that better than ever, at scale.

The future is not coming. It’s already here.

Coming Soon: Advancing Value-Based Community Oncology Through Integrated Data and Partnerships

The next phase is all about execution. Soon we’ll move to a fully operational, performance-validated model that has clinical decision support embedded directly into the workflow, value pools hardwired into how care is delivered, and includes payer alignment that reduces prior authorization burden. In the next piece, we’ll share how we’re implementing this model across a multi-state program, illustrating how data integrity, physician governance, and payer partnership can collectively create a more seamless experience for patients and clinicians.