Transformative EHR Training with the Power of Generative AI

As healthcare technology rapidly evolves, traditional electronic health record (EHR) training methods have become outdated, leading to inefficiencies and knowledge gaps among healthcare professionals. Healthcare organizations face challenges with lengthy onboarding processes and inconsistent learning outcomes, creating a pressing need for innovative training approaches. This study explores how generative AI tools and group-based learning strategies can transform EHR training to address these challenges.

A study was conducted over a one-year time period, evaluating the efficacy and implementation of generative AI tools and methods into the EHR training and onboarding process. The goal was to evaluate the efficacy of the training for long- and short-term outcomes.

The study engaged 133 healthcare professionals across three cohorts over a 3- to 4-week period. Two key innovations were introduced, generative AI interactive videos for pre-class preparation, allowing participants to engage with content before classroom sessions, and a novel group-based testing approach simulating chatbot interactions to foster collaborative learning. An automated assessment tool compared traditional training methods with these enhanced approaches.

Results demonstrated significant improvements in both efficiency and outcomes. AI-enhanced training reduced class time by 24.73%, addressing the critical issue of time constraints in healthcare training (thereby optimizing class time). When reviewing the 4- to 5-month mark for EHR efficacy and use, daily task efficiency notably increased, with AI-supported cohorts spending significantly less time on digital tools (166.53 minutes/day) compared to the non-AI cohort (188.89 minutes/day). Learners using AI tools also showed a 7.18% improvement in knowledge retention and practical application of skills in clinical settings compared to traditional methods at the time of class conclusion. The cohort utilizing both AI-enhanced tools and group-based testing achieved an 8.56% increase in final test scores and exhibited more consistent performance, reducing outcome variability.

These findings confirm that modernizing EHR training through AI and collaborative approaches effectively addresses current inefficiencies while enhancing learning outcomes. The study demonstrates that pre-class preparation using generative AI, combined with group-based learning methods, significantly improves knowledge retention, reduces instruction time, and increases the practical application of skills in real-world clinical settings. This approach offers a scalable and efficient solution to healthcare training, contributing to greater clinical readiness and long-term skill retention.

Looking ahead, the research proposes expanding the use of large language model (LLM) chatbots to provide 24/7 just-in-time training support. Additionally, adapting these methodologies to meet the needs of diverse healthcare roles could ensure a more flexible and comprehensive training system, ultimately transforming how healthcare professionals learn and apply essential EHR skills in their daily practice.