Are you still charting like it’s 2010? What’s changed with ambient AI

A lot has changed in healthcare since 2010, but for many clinicians, charting still feels stuck in the past. 

Even in 2026, documentation often means jumping between tabs, typing during patient visits, and finishing notes after hours. That burden doesn’t just affect clinician well-being. It also impacts data accuracy, quality of care, and performance in value-based care models, where complete and timely documentation matters more than ever. 

That’s why ambient AI has gained so much traction. By capturing the patient-clinician conversation in real time and generating intelligent clinical notes automatically, ambient technology reduces the administrative load and gives clinicians time back. 

But as adoption grows, it becomes clear that documentation alone is not the full answer. To support modern charting workflows and the demands of VBC, AI needs context. 

What you’ll learn

• Why clinical documentation still feels like a burden despite modern healthcare documentation software 
• What ambient AI does well and why clinicians have embraced it
• Where some solutions fall short, especially in value-based care settings
• Why patient context is essential for complete and accurate documentation
• How Navina combines ambient AI with longitudinal data in a clinician-first copilot
  

The old way of charting: stuck in 2010

By 2010, the U.S. healthcare system was well on its way toward adopting electronic health record (EHR) systems in place of paper charts. The Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 had been passed the previous year, and medical documentation was being modernized. 

But while the hope was that healthcare documentation software would streamline clinical workflows, many clinicians experienced the opposite. New documentation requirements increased the amount of time spent charting, contributing to growing administrative burden and widespread physician burnout. Studies show that many clinicians now spend more time on administrative tasks than on direct patient care. 

Today’s technology has created new opportunities to modernize charting through AI-assisted clinical documentation workflows. Yet for many clinicians, the day-to-day reality of charting is still remarkably similar to what it was in the early 2010s: fragmented, time-consuming, and heavy on clicks.

Before seeing a patient, providers spend valuable time navigating the EHR to piece together relevant history from labs, notes, imaging, and referrals. During the visit, attention is frequently split between the patient and the screen to ensure everything is documented. After the visit, providers must reconcile information to produce notes that are complete, compliant, and accurate. 

The rise of ambient AI in clinical documentation

That reality began changing in the 2020s with the emergence of ambient AI in healthcare. Instead of requiring clinicians to type or dictate notes manually, ambient AI tools use natural language processing and automatic speech recognition to listen to the patient-clinician conversation during a visit and generate clinical documentation in real time. 

For clinicians, the benefit is immediate. Notes are created as the visit happens, reducing typing time and limiting the time needed to finish documentation after hours. By removing most of the manual work of charting, ambient AI lets clinicians stay focused on the patient rather than the screen. 

As a result, ambient AI has become one of the most widely-adopted forms of AI-assisted clinical documentation, modernizing charting workflows that once added frustration and burnout.

What ambient AI does well

Ambient AI is designed to remove the mechanics of documentation from the clinical visit. When it works well, it handles note creation in the background so clinicians can focus on the visit itself. 

In practice, ambient AI tools are especially effective at: 

• Creating clinical notes automatically by capturing the visit in real time
• Completing documentation during the visit, rather than after the fact, helping reduce “pajama time”
• Reducing errors and omissions that can occur when notes are finalized later
• Freeing up clinician time so providers can focus more on delivering care than on charting
• Allowing patients to receive more personal attention, improving both care quality and the patient experience
• Reducing documentation-related costs for healthcare organizations while supporting clinician retention

These strengths make ambient AI a powerful tool for modern charting workflows, but documentation alone doesn’t address the full clinical picture.‍

Beyond documentation: why context matters for ambient AI

Documentation and understanding aren’t the same thing. Ambient AI accurately captures the visit conversation. But without connecting the dots from a patient’s medical history, it can miss the broader context that shapes diagnosis and care. 

That context lies outside the visit itself, in prior diagnoses, medications, labs, hospitalizations, referrals, and unresolved care gaps. While clinicians can access this information, doing so requires time-consuming chart review, often still happening after hours. This is especially challenging when the data is fragmented across systems. 

In value-based care settings, these gaps matter even more. Lack of context leads to missed conditions and inconsistent documentation. It also has downstream impacts on risk adjustment, quality reporting, and reimbursement. Ambient AI can still be valuable, but on its own, it leaves clinicians responsible for putting together the bigger picture.

‍The limitations of ambient-only solutions

Ambient AI tools that lack deep integration with patient records come with clear limitations, including:

• Notes that reflect the visit conversation but not the patient’s broader medical history
• Limited ability to surface insights that support care decisions and diagnoses
• Continued reliance on clinicians to manually interpret and connect information
• The need for additional review and editing to ensure documentation is complete and accurate 

When clinicians rely too heavily on ambient-only tools, important clinical signals can remain buried in the chart because they weren’t discussed explicitly during the visit. For organizations focused on quality, compliance, and value-based care performance, these limitations can add up. 

How Navina integrates ambient AI Into a broader picture of patient health

Navina takes a different approach by embedding ambient AI within a comprehensive clinician copilot built to solve the administrative burdens of value-based care.

By integrating ambient AI with disparate clinical data sources, Navina’s AI engine can provide deeper insights that meaningfully improve the quality of care patients receive. Navina doesn’t just record what happens during a visit; its clinician copilot helps interpret the key takeaways in context.

This broader approach to clinical intelligence and physician workflow automation makes clinical notes both more accurate and more complete, reducing the time clinicians need to spend reviewing and revising documentation. It also surfaces actionable insights, such as suspected diagnoses and care gaps right at the point of care, helping clinicians focus on the information that matters most without unnecessary noise and clicks. 

The results for physicians

By integrating comprehensive clinical intelligence with ambient AI, Navina builds on the benefits of scribes and enhances them. The results include:

• Less time spent on documentation, as more complete clinical notes are generated automatically
• Better clinician preparation for each visit, with relevant insights available before and during visits
• More clinician time and attention devoted to patients, due to reduced screen time during visits
• Lower burnout and higher job satisfaction, thanks to a lightened administrative burden 
• Reduced documentation-related costs for healthcare organizations, along with improved clinician retention
• More accurate risk adjustment documentation and proper reimbursement, as Navina’s AI-powered copilot integrates and analyzes information from disparate sources to suggest likely diagnoses for HCC coding

Conclusion: stop charting like it’s 2010

In 2010, long hours charting, including pajama time and the resulting clinician burnout, were largely unavoidable in the U.S. healthcare system. That’s no longer the case. With ambient AI now handling much of the heavy lifting of documentation, healthcare organizations have access to tools that can meaningfully lighten clinicians’ administrative burden. 

But as impactful as those benefits are, supporting clinicians today requires more than transcription alone. By integrating ambient AI seamlessly with disparate sources of information, including from EHR, HIE, and claims data, Navina offers a comprehensive clinician copilot built on rich clinical intelligence. This approach extends the benefits of ambient documentation by helping clinicians work with more complete, actionable information at the point of care.

The result is less administrative strain, more efficient workflows, and better support for high-quality care and organizational financial resiliency. And that’s good news for clinicians, patients, and healthcare organizations alike.

How can a comprehensive copilot take your organization beyond the benefits of basic ambient AI scribes? Discover Navina’s AI-powered platform to find out.