Electronic health record training effectiveness remains a critical challenge in healthcare settings, particularly in specialized units like cardiac catheterization laboratories where documentation accuracy directly impacts patient care and financial outcomes. This study presents a novel three-stage evaluation framework assessing the effectiveness of electronic health record training in a major academic medical center’s cardiac catheterization laboratories. Our framework integrated technical performance evaluation, workflow analysis, and impact assessment through mixed-methods research to evaluate AI-enhanced EHR training solutions. Technical evaluation analyzed existing electronic health record system training infrastructure. Workflow examination combined ethnographic observations of 20 cardiac catheterization laboratory staff over five days with detailed surveys assessing training adequacy. Impact measurement evaluated key performance indicators and financial metrics across >300 cases. Key findings revealed that 70% of staff reported inadequate preparation despite 80% having over five years of experience with electronic health records. Workflow analysis identified substantial environmental factors impacting documentation accuracy. Impact assessment demonstrated potential revenue implications of $30 M due to documentation delays, with 27% of cases exceeding target turnaround times. Our findings demonstrate the potential of AI-enhanced training solutions through personalized learning pathways and real-time documentation assistance while emphasizing the importance of transparency, ethical considerations, and appropriate implementation guardrails. The evaluation framework provides a structured approach for assessing and implementing Artificial Intelligence-assisted training interventions in specialized clinical settings where documentation accuracy is crucial for patient care and operational efficiency.