For most health systems, the answer is no. Intelligent documentation, clinical decision support, and connected workflow tools are already live across forward-thinking organizations. But the people expected to use them were trained for a different era of medicine. The technology arrives before the workforce does, and the gap between the two falls on health systems to close, one onboarding cycle at a time.
Noorda-COM decided not to inherit that gap. It decided to close it before graduation. Download the full Noorda-COM case study (PDF)
For decades, medical education has prepared physicians for the clinical realities of practice: knowledge, diagnostic reasoning, procedural skill. It did not prepare them for the systems they would work inside.
Noorda-COM's leadership recognized the gap before most institutions acted on it, and asked a different question. Not how to add an AI tool to the curriculum, but how to build AI fluency into the learning experience itself.
That distinction matters. AI literacy is not a technical skill to bolt on after graduation. It is a clinical competency. It shapes how physicians think about documentation, how they understand downstream consequences, and how they operate inside connected healthcare systems. The goal was never to graduate students who had used an AI tool. The goal was to graduate physicians who understand how intelligence functions within care.
Matic is an AI-native clinical intelligence company. Matic Intelligence orchestrates specialized agents into one coordinated system across the care-to-claim lifecycle, connecting documentation, patient context, coding, and clinical validation into a continuous workflow embedded inside the EHR clinicians already use.
Scribematic, Matic's ambient clinical documentation capability, captures real clinical conversations and produces accurate, structured, EHR-ready notes in real time. It is specialty-aware, workflow-adaptive, and built to support everything that happens after the note is written.
The fit with Noorda-COM was not a coincidence. Scribematic was built by a physician who trained at Noorda-COM, shaped by direct experience with the documentation burden students face. That origin gave Noorda-COM confidence the platform would serve its educational purpose, not just its operational one.
As Michael Rhodes, Associate Dean for Graduate Medical Education and Continuing Medical Education and CMO and DIO at Noorda-COM, put it:
"This partnership reflects our commitment to prepare students for the medicine they will actually practice. Our graduates will work across a wide range of practice settings where AI is increasingly part of everyday workflows. Matic supports learning by modeling high-quality documentation, reinforcing clinical reasoning, and fitting naturally into real clinical environments, without interfering with patient care."
Noorda-COM and Matic built a program designed to be practical, scalable, and durable, serving both students in clinical training and the preceptors who mentor them. It is built across three integrated layers.
Students get access at the point of care. Matic's ambient documentation capability has been extended to third-year and fourth-year students at the start of their clinical rotations, where they begin documenting patient encounters as part of everyday practice. They build fluency through direct use, not demonstration.
The platform is integrated into the curriculum. Matic is built into coursework through co-developed assignments that connect classroom instruction to real clinical workflows, with quarterly working sessions that deepen the integration as the program matures.
Enablement is structured for everyone. Students, faculty, and preceptors each receive dedicated onboarding. Adoption is designed, tracked, and reinforced across every cohort.
The result is a learning environment where AI fluency is built the same way clinical reasoning is built: through practice, repetition, and structured feedback. And because students and preceptors work through the system together, the learning moves in both directions. Students arrive in clinical settings already fluent. Preceptors gain exposure to modern AI-enabled workflows through the students they train.
The documentation encounter is where clinical intent is established and downstream outcomes begin. The note a physician writes shapes how the visit is coded, what is reimbursed, and the financial and operational health of the organization delivering care.
Noorda-COM students learn that chain of consequence through experience, not lecture. They graduate with a working model of how AI fits into a connected system across the full care-to-claim lifecycle: understanding the patient, capturing care, capturing revenue, and proving every decision. Documentation quality is not a clerical concern. It is a system-level problem, and fixing it at the training level strengthens the entire chain.
AI readiness is becoming a hiring problem. Health systems deploying AI will increasingly sort candidates by workflow fluency. Noorda-COM graduates will have it. Most will not.
That reframes the whole conversation. This is not only a story about education. It is a competitive advantage for every organization that hires physicians, and a compounding expectation gap for every health system that waits.
For organizations evaluating Matic, the partnership signals something beyond product capability. Matic builds with customers, not for them. The co-build model is not a services offering. It is how the platform achieves adoption that lasts, in medical education and in every enterprise deployment.
Every graduating class becomes a distribution channel. Every rotation becomes a knowledge transfer event. Matic is not just partnering with a medical school. It is building physician-level familiarity with the platform into the workforce pipeline at the point of formation.
Read the full story. The complete Noorda-COM case study covers the program architecture, the preceptor model, and the strategic takeaways for health systems, provider groups, and EHR platforms. Download the full Noorda-COM case study (PDF)