Steve examines how artificial intelligence (AI) is reshaping the role of patient support hubs in the specialty drug ecosystem. As policy, payer, and gross-to-net pressures mount, he argues that the hub model must evolve from a service function into a revenue-protection strategy—one that uses “embedded AI” to enhance, not replace, human judgment.
To learn more, download ConnectiveRx’s free eBook: 8 Questions Patient Access Leaders Should Ask About AI—But Aren’t.
Read on for Steve’s insights.
Hubs, AI, and the New Era of Revenue Protection in Patient Access
By Steve Randall, Chief Technology Architect, ConnectiveRx
Hubs as Revenue Protection
The economics of access have shifted. The Inflation Reduction Act (IRA), aggressive PBM practices, and gross-to-net pressures have squeezed margins and made every prescription count. According to IQVIA, only about 30% of new specialty prescriptions get filled, with most lost to coverage gaps, prior authorizations, or distribution issues. Each lost prescription isn’t just a missed opportunity for patient care—it’s lost income and an opening for competitors.
In this environment, hub services are essential for guiding providers and patients through payer requirements, reducing coverage denials, and preventing therapy abandonment. Brands that move quickly to support patients and prescribers reinforce confidence and secure revenue that might otherwise slip away.
Embedded AI, not AI instead of people
The question isn’t whether hubs need AI. It’s how to embed it in ways that protect access, not undermine it. It’s tempting to view AI as a tool for cost savings, but the real test isn’t leveraging all it can do—it’s knowing when and how it should be used. AI excels at workflow acceleration: document management, call summarization, anomaly detection, and surfacing trends in affordability and benefits. Used this way, AI improves program quality and shortens cycle times.
However, automating every touchpoint can flatten a personal, trust-based prescription journey into a transactional one. Mature teams easily distinguish cost-focused automation from an outcomes-focused enhancement. The right approach is embedding AI technology in ways that it becomes part of the fabric of the access program. It should live in places where the tools are educated and shaped by brand values, brand style, and know-how of the care experience to boost outcomes.
Success metrics for modern hubs must be clinical and behavioral—higher adherence, more successful prior authorizations, lower pharmacy abandonment—not just operational savings. The best programs build in moments for people to jump in at the right time: when something about a patient’s case doesn’t add up, when side effects appear, or when the data suggests someone’s losing patience or is about to give up. In the proper framework, AI could likely handle the majority of standard tasks so support teams can focus on the nuanced work that truly requires a human touch.
Data first, then models—never the other way around
A persistent misconception is that AI inherently brings accuracy. In reality, accuracy is a function of data quality, context, and supervision. If models aren’t learning from robust, governed program data—and if outcomes aren’t fed back into those models—“AI” is just point-in-time automation.
Architecture matters. Rather than tossing generic prompts at a general model and hoping for the best, leading teams constrain models with the right building blocks—approved APIs, brand assets, workflow rules, and compliance constraints—so outputs reflect how their organization builds and runs patient support. This “model-with-context” approach turns AI into an industrial-grade accelerator, not just a clever demo.
The payoff is continuous value delivery: today it’s next-best actions for case managers, automated call summaries, and intelligent workflow triggers; tomorrow it’s higher-confidence eligibility checks, proactive outreach, and predictive insights to drive better decision making. Over time, the learning loop compounds—models get smarter, interventions get timelier, and human specialists spend time where it truly moves the needle.
Governance and Accountability
Given the sensitivity and competitive value of patient and program data, leaders must press vendors and internal teams on governance specifics. Mature partners can show, not merely tell, how they prevent leakage and avoid commingling sensitive inputs with public systems. High-performing programs keep humans in the loop for clinically complex or emotionally sensitive scenarios. In an environment as regulated and human as patient access, governance and discretion protect both patients and brands.
Planning for 2026 and Beyond
Looking ahead, patient access leaders face a radically different set of planning assumptions. Economic constraints, R&D shifts, and environmental volatility mean past playbooks can no longer be relied upon. The answer lies in preserving the non-negotiable hub services, smart use of technology, and aligning hub strategy to brand revenue goals. Mission-critical hub services—benefit verification, prior authorization support, copay assistance, and patient onboarding—must be protected, even if other functions are pared back. Labor-intensive processes can increasingly be automated, but only where it makes sense for outcomes.
As specialty drug access grows more complex and competitive pressures mount, patient support leaders face a new imperative: deploying technology not just for efficiency, but for outcomes. To navigate these changes, leaders must ask the right questions about AI, governance, and human intervention.
For practical insights, download 8 Questions Patient Access Leaders Should Ask About AI—But Aren’t. You can see Steve on November 21st at the 2025 Patient Support Services Congress, where he will be presenting "Beyond Cost Savings–Evaluating Ai for Patient Outcomes in Hub Services."
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