Implementation of a digital scribe to improve face to face interactions and reduce charting time

Abstract

Excessive documentation time in electronic health records (EHRs) has led to provider burnout, decreased efficiency, and reduced patient interaction, particularly in psychiatric care. This quality improvement project aimed to implement and evaluate an artificial intelligence (AI) digital scribe in a rural private psychiatric practice to reduce documentation time while maintaining accuracy. A scoping literature review was conducted to assess AI-assisted documentation methods, followed by a six-week implementation of the AI scribe in a single- provider clinic. The Plan-Do-Study-Act (PDSA) model guided implementation, with pre- and post-intervention documentation times recorded for analysis. Results demonstrated a significant reduction in documentation time, with AI-assisted methods reducing documentation time by 72%, surpassing the initial goal of a 20% reduction. The provider experienced daily time savings of approximately 2.5 hours, allowing for improved patient interaction and workflow efficiency. Additionally, the AI scribe improved documentation quality and structure, ensuring accurate patient records while maintaining provider oversight. While AI-assisted documentation has demonstrated benefits, human oversight remains necessary to mitigate risks such as transcription errors and automation bias. The findings suggest that AI scribes can significantly enhance clinical efficiency, reduce provider burden, and improve patient care, particularly in high-demand, underserved settings. Future studies should explore broader applications of AI scribes in diverse healthcare environments.