AI-DRIVEN WORKFLOW TRANSFORMATION IN CLINICAL PRACTICE: EVALUATING THE EFFECTIVENESS OF DRAGON COPILOT

Abstract

Artificial intelligence (AI) in healthcare is transforming clinical workflows; Dragon Copilot by Microsoft gives an innovative making. And, you get the best of ambient listening from DAX Copilot, and real-time speech recognition from Dragon Medical One combined with generative AI to offer a voice-first workspace in one, unified clinical AI assistant. This investigation assesses whether DC is effective in improving workflow efficiency, the quality of clinical documentation, and physician satisfaction. A mixed-methods approach was adopted. Measures Quantitative data were collected from 100 clinicians in three hospitals who used Dragon Copilot over 12 weeks. The efficiency of the workflow was measured in time on task analysis and documentation rates. Structured interviews and questionnaires were drawn out of semi-structured interviews and questionnaires. Comparison between their baseline metrics and post-deployment results. Outcomes demonstrate a 38% decrease in documentation time, 29% improvement in patient interaction, and a rise in the quality of documentation. The clinicians have stated that they can now focus on thinking instead of doing, performed lower redundant tasks, and have better usability of EHR with voice command integrated. Utilize a function, ambient listening provided real-time transcription with contextual information, while generative AI assisted with summarizing consultations and drafting referral letters. The study found Dragon Copilot to have a positive impact on clinical productivity and user satisfaction while reducing documentation burden for clinicians, enabling them to focus on patient care. The research suggests that use and development of decision support tools should be extended.