AI Moves the Specialty Pharmacy Into the Clinic

Early AI-powered clinic pharmacies cut delays and paperwork, giving providers new ways to deliver specialty drugs beyond PBM-heavy models

5 Jan 2026

AI-powered specialty pharmacy services are beginning to move directly into US medical practices, reshaping how some of the most complex and costly drugs reach patients.

The model, still at an early stage, places pharmacy teams and software inside clinics rather than relying on external specialty pharmacies. Supporters argue this closer integration can reduce delays in starting treatment for conditions such as cancer, autoimmune disorders and rare diseases, where timing and coordination matter.

Interest in the approach has grown after a recent high-profile funding round that signalled investor confidence in in-clinic pharmacy platforms. Backers including venture capital group NEA are betting that tighter links between physicians, pharmacists and automated systems can address longstanding inefficiencies in specialty drug delivery.

For decades, access to specialty medicines has largely been controlled by pharmacy benefit managers, which oversee benefits checks, prior authorisations and distribution through centralised workflows. Critics say this structure often slows care and adds administrative burden for clinicians. The clinic-based model reverses that process, embedding pharmacy staff alongside care teams while AI systems manage routine administrative tasks.

Providers testing these systems report faster approvals, with fewer manual steps and less back-and-forth with insurers. Clinics also gain better visibility into whether patients begin treatment and adhere to it. When therapy plans change, adjustments can be made more quickly within the practice.

The shift does not yet amount to a wholesale disruption of the specialty pharmacy market. Large groups such as CVS Health continue to dominate, and PBMs remain deeply embedded in drug pricing and access. But industry observers see in-clinic pharmacies as part of a broader decentralisation of healthcare operations, gaining relevance as regulators scrutinise costs, transparency and patient access.

Practical benefits are driving adoption among providers, including reduced paperwork and more time for patient care. However, scaling the model brings challenges. State licensing requirements, data privacy rules and oversight of automated decision-making systems add complexity.

Supporters say these hurdles are manageable in a sector already shaped by regulation. For now, AI-enabled in-clinic pharmacies resemble a blueprint rather than a finished system. Wider adoption would test whether specialty drug delivery can move closer to where care is actually delivered.