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Microsoft

AI-Driven Clinical Documentation

Microsoft via Coursera

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Overview

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Clinical documentation takes time that could be spent on patient care. This course introduces Microsoft 365 Copilot for Healthcare and Dragon Copilot, two AI tools designed to reduce documentation burden and improve chart completeness in real clinical workflows. You'll build hands-on skills: generating encounter note drafts from patient-clinician conversations, reviewing AI-generated content for accuracy, and editing notes to meet established documentation standards. You'll also learn to reconcile Dragon Copilot outputs within EMR workflows, ensuring AI-generated content meets field requirements, traceability standards, and downstream usability needs. By the end of this course, you'll be able to generate AI-assisted encounter note drafts, edit them to meet documentation standards, and integrate Copilot outputs into EMR workflows with accuracy and traceability. Designed for health administration professionals and allied health staff who document patient encounters or support clinical note workflows. Basic familiarity with clinical documentation concepts and Microsoft 365 applications is assumed.

Syllabus

  • M365 Copilot: How Generative AI Works
    • This module clarifies where Copilot lives and how it accesses the information you need. You will learn the difference between "Public AI" and "Work AI," specifically how Copilot securely references your Outlook and OneDrive data to provide professional context. Learners will leave knowing how to verify that Copilot is referencing the correct organizational data and how to navigate the M365 ecosystem with confidence.
  • M365 Copilot: Copilot's Productivity Role
    • This module bridges the conceptual understanding of generative AI to the practical reality of Microsoft 365 Copilot as a productivity tool. Learners will explore real business use cases, understand how Copilot creates measurable value, and practice articulating that value in professional terms.
  • Draft Notes: How AI Documentation Works in Clinical Settings
    • This module introduces how Dragon Copilot and Microsoft 365 Copilot for Healthcare convert patient-clinician conversations into AI-generated clinical encounter note drafts, explaining the core mechanisms, key benefits, and non-negotiable human review requirements of ambient AI documentation in clinical settings.
  • Copilot Integration: Copilot Across Your Daily Toolkit
    • This module provides learners with a practical orientation to Copilot across the Microsoft 365 apps they use every day. Learners will see how Copilot surfaces in Word, Teams, and other core tools, and understand the workflow pattern that makes Copilot integration effective rather than disruptive, and practice executing hand-offs that move information seamlessly from one stage of work to the next.
  • Copilot Integration: Evaluating Tasks for Copilot vs. Human-Led Effort
    • Not every task belongs to Copilot. This module equips learners with a structured evaluation framework to make confident, defensible decisions about which tasks to delegate to AI and which require human leadership, turning ad hoc AI use into a professional strategy.
  • Copilot for Healthcare: Capture Narratives and Edit AI-Generated Notes
    • This module covers how to use Microsoft 365 Copilot for Healthcare's ambient listening capability (Microsoft Dragon Copilot) to capture patient narratives and produce draft clinical note sections, and how to edit those outputs into complete, finalized SOAP notes and HPIs ready for clinician sign-off.
  • Copilot for Healthcare: Documentation Standards and Clinical Validation
    • This module builds the skills to review AI-generated clinical documentation against coding, billing, and medical necessity requirements, and to apply a structured validation framework confirming that AI-generated summaries accurately reflect clinical intent without introducing patient safety concerns.
  • Generate Clinical Encounter Note Drafts with Dragon Copilot
    • This module covers how to apply Dragon Copilot's ambient documentation features in a routine outpatient documentation context from initiating a capture session through generating a complete clinical encounter note draft and reviewing it for completeness before passing it to the clinician for sign-off.
  • Analyze Dragon Copilot Note Sections Before Clinician Sign-Off
    • This module develops the analytical skill of systematically reviewing Dragon Copilot-generated note sections for three specific quality issues—omissions, attribution errors, and irrelevant transcript content—and producing an annotated review that supports informed clinician sign-off.
  • Evaluate Dragon Copilot Configuration Choices for Clinical Workflows
    • This module builds the evaluative skill of assessing Dragon Copilot configuration options—including note template selection, specialty-specific structure settings, and documentation burden parameters—to determine which configuration choices best fit an established clinical workflow and support consistent draft quality.
  • Strengthening AI Notes: Apply Documentation Standards
    • This module covers how to apply clinical documentation standards to edit Dragon Copilot-generated note sections—specifically, histories, assessments, and plans— completeness and encounter relevance, so the note is ready for record finalization.
  • Strengthening AI Notes: Analyze Clinical Encounter Note Quality Discrepancies
    • This module develops a more sophisticated quality analysis skill—tracing the documentation chain from the source conversation through the generated draft to the final chart entry and identifying where discrepancies entered and what they mean for record quality and process improvement.
  • Strengthening AI Clinical Encounter Notes: Evaluate for Legal, Operational, and Care Usability
    • This module builds the evaluative skill of reviewing finalized AI-assisted clinical encounter notes against three usability dimensions—legal defensibility, operational usability, and continuity-of-care appropriateness—within routine documentation review processes.
  • EMR Reconciliation: Transfer and Reconcile Dragon Copilot Content
    • This module covers how to transfer Dragon Copilot-generated clinical encounter note content into an EMR workflow, review it within the chart structure, and complete a reconciliation process that verifies accuracy before the chart is completed.
  • EMR Reconciliation: Analyze EMR Workflow Constraints
    • This module develops the analytical skill of identifying and documenting EMR workflow constraints—system-level, structural, and process-level limitations—that affect how Dragon Copilot and Microsoft 365 Copilot content can be reliably incorporated into charting and follow-up activities.
  • GenAI Literacy: AI Tools in Clinical Documentation
    • This module deepens learners' understanding of how generative AI underlies Dragon Copilot and Microsoft 365 Copilot for Healthcare, and how that understanding strengthens responsible, effective use across clinical documentation, note review, and EMR workflows. Learners explore what large language models do when generating clinical note drafts, why confident-sounding AI outputs still require human review, and how to apply AI literacy to work more precisely and safely in a documentation quality role.
  • Project Module: Apply AI Documentation Skills in a Clinical Scenario
    • In this project, learners integrate all ambient AI documentation workflows, clinical note capture and editing, Dragon Copilot draft analysis, documentation standards, and EMR reconciliation skills to complete a full AI documentation review cycle for a simulated outpatient encounter. Working through provided materials, they review a Dragon Copilot draft clinical note, edit it to documentation standards, trace discrepancies through the documentation chain, and evaluate the scenario for EMR record integrity, producing a finalization-ready documentation package with a structured readiness report. This project mirrors the end-to-end workflow a health administration professional would follow when supporting AI-assisted documentation in a real clinical setting.

Taught by

Microsoft

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