Beyond Dictation: How AI Ambient Listening is Reshaping Clinical Notes

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This article is by Evgeny Koloda, Senior IT Director at Better Health Group.

Introduction
 AI-powered ambient listening tools are transforming how healthcare providers document patient visits. Instead of typing or dictating notes manually, providers can now rely on AI systems that passively listen, transcribe, and structure conversations into progress notes. The promise: reduce administrative burden, reclaim physician time, and improve note quality. But what does implementation look like in practice?

Drawing from my direct work with eClinicalWorks (eCW), NextGen Mobile, and Microsoft Dragon, this article breaks down the results, challenges, and trade-offs of deploying ambient AI in clinical settings.

Why Ambient Listening?
 Medical documentation is a major source of burnout. Providers often spend hours after clinic completing notes, reducing both productivity and satisfaction. AI ambient listening offers a compelling alternative: hands-free note generation that captures the natural flow of the patient encounter.

These tools aim to:

• Minimize screen time during consultations
  

• Increase face-to-face engagement with patients
  

• Accelerate documentation workflows
  

• Support structured data capture for EHRs
  

Tools Reviewed

1. eCW Scribe (eClinicalWorks)
    Integrated directly with the eCW EMR, this tool uses a combination of voice recognition and ambient AI to draft progress notes. It’s designed to fit within eCW’s native workflow but has limitations in customization and note formatting. It performs best in settings with consistent provider workflows and predictable terminology. Notably, eCW recently acquired a third-party AI solution called Suno to strengthen its ambient listening capabilities.
   

2. NextGen Mobile with Ambient Assist
    Offers mobile-first AI documentation, pulling data from both voice and typed inputs. The system integrates directly with NextGen EMR, and excels in combining mobile usability with AI transcription. It supports real-time editing, which can improve user confidence and workflow efficiency.
   

3. Microsoft Dragon Ambient eXperience (DAX)
    Dragon’s solution focuses on high-accuracy medical transcription, paired with clinical NLP to auto-generate SOAP notes. It has strong enterprise appeal and integration options but comes with higher cost and complexity. DAX is particularly suited to high-volume practices with IT support for system optimization.
   

Many providers still prefer using a physical Dragon microphone for dictation due to familiarity and tactile control. While newer ambient tools utilize computer microphones or even mobile phone apps to capture conversations natively within EMR applications, adoption remains limited among clinicians deeply accustomed to the legacy Dragon hardware.

Implementation Lessons

1. Integration Isn’t Plug-and-Play
    Despite marketing claims, integrating ambient listening into live clinical workflows requires customization. EHR compatibility, network infrastructure, and user training all impact success. Clinics often underestimate the time needed for technical setup and process redesign.
   

2. Accuracy Varies by Context
    Tools perform well in quiet, one-on-one consults. In chaotic, multi-voice environments (urgent care, group visits), accuracy drops. Pediatric settings add extra complexity due to overlapping speech and emotional tone. Accents and regional terminology can also challenge recognition algorithms.
   

3. Provider Buy-In is Make or Break
    The best tools still fail if providers don’t trust or adopt them. Early adopters often need multiple demos, hands-on testing, and reassurance about data privacy before fully committing. Trust is built through iterative improvement and responsive support.
   

4. Note Quality Isn’t Fully Automated
    Generated notes still require review and editing. Common issues include:
   

• Misinterpreted medical terms (especially rare conditions or medications)
  

• Over-generalized summaries lacking nuance
  

• Omissions in ROS or PE sections that could impact downstream coding
  

Pros and Benefits

• Time Savings: Up to 30% reduction in time spent per patient note
  

• Improved Patient Engagement: Providers spend less time typing and more time interacting
  

• Consistency in Documentation: Especially useful for template-driven notes
  

• Reduced After-Hours Work: Providers reclaim personal time once used for documentation
  

• Training Support: Standardized notes help onboard new clinicians more effectively
  

Challenges and Trade-Offs

• Cost: Licensing and implementation costs can be high (especially for DAX)
  

• Noise Capture: While all reviewed tools are HIPAA-compliant, AI solutions constantly listen during encounters, sometimes capturing non-relevant or highly personal conversation details.
  

• Excessive Documentation: In value-based care models where appointments can run 45–60 minutes, progress notes may balloon with patient lifestyle discussions or tangential topics, requiring more time for editing and structuring.
  

• Technical Dependence: System downtimes or transcription delays can disrupt clinic flow
  

• Editing Workload: Some users find note review adds a different kind of administrative burden
  

• Workflow Disruption: Initial adoption often slows providers down before benefits are realized
  

Measuring Success
 Success metrics vary but may include:

• Reduction in time spent per note
  

• Increase in number of same-day signed notes
  

• Provider satisfaction scores (pre vs. post-implementation)
  

• Documentation audit results for compliance and accuracy
  

Clinics implementing AI ambient tools should track these metrics consistently and use them to guide refinement.

Final Thoughts
 Ambient AI is not a magic bullet, but it is a powerful tool—when implemented with clear expectations and proper support. For clinics willing to invest in change management, training, and iteration, the benefits are tangible. Providers can spend more time with patients, reduce after-hours burden, and achieve more consistent documentation.

However, the gap between vendor promises and real-world delivery still exists. Success depends on selecting the right tool for your setting, supporting it with robust onboarding, and continuously refining workflows based on user feedback.