JMIR Publications

ABSTRACT

Background:

Hospitals worldwide need to upskill their workforce in advanced AI technologies, yet published guidance on how to design and deliver such training—particularly in agent-level tools like Retrieval-Augmented Generation (RAG) and the Model Context Protocol (MCP)—remains virtually absent.

Objective:

To describe the design, implementation, and lessons learned from an 8-week, 56-hour intensive generative AI training program for multidisciplinary healthcare professionals, drawing on both quantitative outcome data and participants’ own reflections on their learning experience.

Methods:

The program was delivered on-site at Asan Medical Center with simultaneous online broadcast to two regional affiliate hospitals. The curriculum was built around the premise that MCP and AI agents would become the foundation of healthcare AI utilization, allocating 37% of instructional time to MCP and 71% overall to hands-on practice. Participants progressed from foundational concepts through RAG and MCP to team-based capstone projects, supported by funded AI tool subscriptions, a dedicated internal cloud platform, and 3–6 hours of weekly mentoring per team. A pre-post survey (pre: n = 83; post: n = 64) evaluated outcomes across Kirkpatrick Levels 1–3, complemented by thematic analysis of open-ended reflections on self-perceived growth.

Results:

The technologies that received the greatest curricular investment produced the largest self-efficacy gains (MCP: d = 1.57; overall effect: r = .574), and participants most frequently cited MCP and RAG when describing how abstract concepts “became concrete and actionable.” Non-IT professionals—clinicians, health information managers, and researchers—showed consistently larger gains than IT specialists; several reported coding for the first time through vibe coding, challenging the assumption that advanced AI training requires technical backgrounds. Despite significant overall gains, a knowledge-practice gap persisted: job-specific competency remained below the scale midpoint, though participants spontaneously reported generating workplace application ideas. Curriculum pacing was rated lowest despite high overall satisfaction (4.03/5.0), signaling that even 56 hours may progress too quickly for mixed-expertise cohorts. Capstone projects with dedicated mentoring received the highest satisfaction ratings; 11 of 12 teams presented functional prototypes, one received the Minister of Health and Welfare Award, and two have since been deployed in hospital operations.

Conclusions:

This program demonstrates that transforming a multidisciplinary hospital workforce into AI-capable professionals is feasible through intensive, hands-on training centered on agent-level technologies, and that capstone projects with dedicated mentoring can serve as a pipeline from classroom learning to institutional AI adoption. The knowledge-practice gap highlights the need for post-training support structures to translate self-efficacy gains into sustained workplace practice.