AI Agent Operational Lift for Next Oncology, An Avacare Business in San Antonio, Texas

Why AI matters at this scale

Next Oncology, an Avacare business, operates a growing network of community-based cancer centers across the United States. Founded in 2018 and headquartered in San Antonio, Texas, the organization employs 201-500 people and focuses on delivering advanced oncology care—including chemotherapy, immunotherapy, and a robust clinical trials program—outside traditional academic medical centers. This decentralized model makes cutting-edge treatment more accessible, but it also creates operational complexities: managing high volumes of complex patient data, coordinating multi-site trials, and navigating burdensome payer requirements with a lean administrative team.

At this size band, AI is not a luxury but a force multiplier. Mid-market healthcare providers often lack the massive IT budgets of large health systems, yet they face identical clinical and regulatory pressures. Cloud-based, verticalized AI tools have matured to the point where a 200-500 employee organization can deploy them incrementally, without hiring a team of data scientists. For Next Oncology, AI adoption directly supports the shift toward value-based care, where reimbursement increasingly depends on outcomes and efficiency rather than volume.

Three concrete AI opportunities with ROI framing

1. Personalized treatment decision support. Oncology is drowning in data: genomic profiles, radiology images, pathology reports, and clinical notes. An AI-powered clinical decision support system can ingest these disparate sources and surface evidence-based treatment options tailored to the individual patient. For a community network, this reduces unwarranted variability and accelerates time to optimal therapy. ROI manifests through improved progression-free survival rates (attracting more referrals) and reduced costs from ineffective regimens.

2. Ambient documentation and coding optimization. Oncologists spend up to two hours per day on EHR documentation, a leading cause of burnout. Ambient AI scribes that listen to patient visits and auto-generate structured notes can reclaim that time, boosting capacity by 15-20%. Simultaneously, AI-assisted medical coding ensures accurate capture of hierarchical condition categories (HCC) and chemotherapy administration codes, directly lifting revenue by 3-5% without changing clinical volumes.

3. Intelligent clinical trial matching. Next Oncology’s clinical trials program is a key differentiator. Manually screening patients against complex trial inclusion/exclusion criteria is slow and error-prone. Natural language processing models can pre-screen eligible patients from the EMR in real time, flagging candidates for coordinators. This accelerates enrollment, reduces sponsor dropouts, and strengthens the network’s reputation as a premier trial site—driving both grant revenue and patient volume.

Deployment risks specific to this size band

Mid-market providers face unique AI risks. First, integration fragility: smaller IT teams may struggle to connect AI point solutions with existing EMRs (Epic, Cerner) and oncology-specific systems (Varian, Mosaiq), leading to workflow disruptions. Second, algorithmic bias: models trained on academic medical center data may underperform on Next Oncology’s community-based, potentially more diverse patient population, risking care disparities. Third, change management: without a dedicated informatics department, clinician buy-in can falter if AI tools add perceived friction. Mitigation requires phased rollouts, robust vendor support, and a governance committee including frontline oncologists. Starting with low-risk, high-return use cases like ambient documentation builds trust before tackling clinical decision support.