Li-Mei Chen

Case Management Department, Landseed International Hospital, Taoyuan City 32449, Taiwan

Abstract Title:

Enhancing AI Literacy among Case Managers through a Knowledge Management–Driven Training Models

Biography:

Li-Mei Chen is a Case Manager at Landseed International Hospital, Taiwan, with expertise in healthcare quality management, chronic disease care, and patient education. As a Registered Nurse, Certified Diabetes Educator, and Chronic Kidney Disease Care Educator, she is actively involved in healthcare innovation and professional education. Her interests focus on knowledge management, artificial intelligence literacy, and digital transformation in healthcare. She is committed to developing innovative approaches that enhance organizational learning, workforce capability, and patient-centered care. Name: Li-Mei Chen Email: chenlm0320@gmail.com

Research Interests:

Statement of the Problem: With the rapid advancement of digital transformation and artificial intelligence (AI) in healthcare, enhancing AI competency among case managers has become essential for improving care quality, patient education, communication, and administrative efficiency. This project adopted a Knowledge Management (KM) approach to develop an AI literacy training program that introduced generative AI tools, including ChatGPT and NotebookLM, to support clinical practice and build a smart case management workforce. Methodology: Using a KM framework, SWOT analysis and knowledge mapping were conducted to identify knowledge gaps in AI awareness, operational skills, and practical applications among case managers. A structured training program covering ChatGPT, NotebookLM, meeting summarization, presentation generation, and AI-assisted video production was implemented. Training effectiveness was evaluated using the Kirkpatrick four-level model. To strengthen AI governance and safety, an AI operational guideline was established, including risk classification, data de-identification, content review, incident reporting, and staff education. Prompt-sharing mechanisms and an AI application repository were also developed to facilitate knowledge transfer and organizational learning. Findings: Between 2025 and 2026, three AI-related training activities totaling five hours were conducted. Participation reached 90%, and overall satisfaction was 93%. From February to June 2026, 29 AI-assisted projects were completed, including 11 presentations, 8 posters, 3 meeting summaries, and 6 educational videos. NotebookLM (23 projects) and ChatGPT (16 projects) were the most frequently used tools. Post-training assessments showed that 100% of participants achieved scores above 80, demonstrating improved understanding of AI ethics, information security, data protection, and practical AI applications. Conclusion & Significance: The KM-driven training model effectively addressed AI knowledge gaps, enhanced workplace productivity, and transformed individual experiences into reusable organizational knowledge assets. This approach provides a sustainable model for promoting digital transformation and smart case management in healthcare. Keywords: Artificial Intelligence (AI), Case Management, Knowledge Management, AI Literacy, Clinical Care.