Overview

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Accurate clinical documentation depends on more than generating a draft—it requires careful verification before anything enters the health record. This course focuses on reviewing and finalizing Dragon Copilot outputs responsibly within clinical documentation and records workflows. You'll apply ambient capture workflows to generate encounter note drafts, analyze them against source conversations, and evaluate note quality for completeness, clinical relevance, and workflow fit, all before EHR finalization. The course closes with a hands-on project where you'll verify and finalize a complete Dragon Copilot documentation package, simulating a real records management workflow. By the end of this course, you'll be able to configure ambient capture workflows, verify AI-generated draft accuracy, and evaluate note quality before finalizing documentation in the EHR. Designed for health administration and clinical operations staff responsible for reviewing, routing, or managing AI-generated documentation. Prior experience with Dragon Copilot and clinical documentation workflows is assumed.

Syllabus

Verify Copilot Outputs: Execute Ambient Capture Workflows
- This module covers how to configure and execute Dragon Copilot ambient capture workflows for defined outpatient visit types—from initiating the capture session through generating a complete draft encounter note. Learners will be able to set up Dragon Copilot for a scheduled outpatient visit and produce a draft note ready for verification. This skill is essential for clinical operations staff who manage AI-generated documentation pipelines at scale.
Verify Copilot Outputs: Verify Drafts Against Source Conversations
- This module develops the analytical skill of verifying Dragon Copilot-generated draft sections—history, assessment, and plan—against the source clinician-patient conversation to confirm accuracy before EHR finalization. Learners will be able to systematically compare draft content to known conversation content and identify discrepancies requiring correction. This verification capability is critical for preventing documentation errors from becoming part of the permanent patient record.
Verify Copilot Outputs: Evaluate Note Quality Across Scenarios
- This module builds the evaluative judgment to assess Dragon Copilot note quality across three dimensions—completeness, clinical relevance, and workflow fit—as notes recur across multiple documentation scenarios over time. Learners will be able to apply a quality evaluation framework to identify patterns, rate overall note quality, and provide actionable feedback to improve templates and workflows. This capability enables health administration professionals to move beyond individual note review toward systematic quality management of AI-generated documentation.
Verify and Finalize a Dragon Copilot Documentation Package
- In this project, you verify and finalize a complete Dragon Copilot documentation package, executing the full workflow from ambient capture configuration through source-conversation verification to quality evaluation for a multi-note batch representing a realistic half-day clinic session, including three encounters (configuration for all three; verification for two). You'll produce a finalized documentation package that demonstrates your ability to configure, verify, and evaluate AI-generated clinical notes at operational volume. This deliverable mirrors the daily work of clinical documentation staff who manage Dragon Copilot output quality in real healthcare settings.

Taught by

Microsoft

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