AI in EHR: How Ambient Documentation and Clinical AI Are Transforming Healthcare in 2026

The AI Revolution in Healthcare Is No Longer Theoretical

In 2023, when generative AI first began capturing mainstream attention, healthcare was cautiously skeptical. Clinicians worried about accuracy. Administrators worried about liability. CIOs worried about integration. One year later, the data tells a different story: the American Medical Association found that 66% of US physicians used AI tools in clinical practice in 2024, up from 38% in 2023 — a 78% increase in adoption in a single year.

That acceleration has not slowed. Entering 2026, AI is no longer a futuristic add-on to your cloud EHR — it is becoming a core capability that vendors are embedding directly into their platforms. Epic, Oracle Health, eClinicalWorks, NextGen, and specialty vendors like AZZLY Rize and Valant are all shipping AI features in production, not just pilot programs.

The shift is being driven by a convergence of three forces: the maturation of large language models (LLMs) that can process clinical conversation with high accuracy, the physician burnout crisis that demands documentation relief, and the competitive pressure among EHR vendors to differentiate on AI capability. Understanding where AI delivers real value — and where it remains immature — is now a critical factor in selecting or switching your EHR.

Ambient Clinical Documentation: The Flagship AI Use Case

If there is a single AI application that has broken through from "promising pilot" to "widespread clinical use," it is ambient clinical documentation. The concept is straightforward: an AI-powered tool passively listens to the natural conversation between physician and patient during a clinical encounter, then automatically generates a structured clinical note — typically in SOAP (Subjective, Objective, Assessment, Plan) format — that the physician reviews, edits if needed, and signs.

The numbers are striking. Ambient documentation generated approximately $600 million in revenue in 2025, a 2.4x year-over-year increase. Nearly $1 billion has been invested in the ambient documentation space through venture capital and strategic acquisitions. And according to industry analysis, 72% of practices using AI rely on an ambient scribe tool as their primary AI application.

Adoption within the largest EHR ecosystem has been particularly rapid. By June 2025, 62.6% of Epic hospitals had deployed ambient AI documentation — up from virtually zero just two years prior. This is one of the fastest technology adoption curves in healthcare IT history, driven by the universal pain point it addresses: clinical documentation burden.

The impact on time savings is the metric that resonates most with practicing clinicians. Multiple studies and vendor reports converge on a consistent finding: two-thirds of physicians using ambient AI tools save between 1 and 4+ hours per day on documentation. NextGen Healthcare reports that its Ambient Assist product saves clinicians an average of 2.5 hours per day. That is time that can be redirected to patient care, professional development, or personal well-being.

How Ambient Documentation Actually Works

Understanding the technical workflow helps practices evaluate products and set realistic expectations. Here is the step-by-step process:

Step 1: Audio Capture

The clinician activates the ambient AI tool — usually through a smartphone app, tablet, or a button within the EHR interface. The tool begins recording the patient-physician conversation. Some products use the device's built-in microphone; others use dedicated hardware for better audio quality. Patient consent protocols vary by state, but best practice is to inform the patient and document their agreement.

Step 2: Speech-to-Text Transcription

The audio is processed through an automatic speech recognition (ASR) engine that converts the spoken conversation into text. Modern medical ASR models are trained on millions of hours of clinical audio and understand medical terminology, drug names, anatomical references, and abbreviations with high accuracy. The best models handle multiple speakers, accents, and background noise effectively — though noisy environments and heavy accents remain challenge areas.

Step 3: Clinical Note Generation

A large language model processes the transcription and generates a structured clinical note. The AI must accomplish several complex tasks: identify which content is medically relevant (filtering out small talk), organize information into the correct SOAP sections, extract specific data elements (chief complaint, medication changes, physical exam findings, diagnoses), and format the note to match the practice's documentation standards.

Step 4: Physician Review and Sign-Off

The generated note appears in the EHR for the physician to review. The clinician can edit any section, add information the AI missed, or correct inaccuracies. Once satisfied, the physician signs the note, which then becomes part of the official medical record. This human-in-the-loop step is essential — no reputable vendor suggests that AI documentation should bypass physician review.

Step 5: Coding and Billing Suggestions

Many ambient tools go beyond note generation to suggest appropriate ICD-10 diagnosis codes and CPT procedure codes based on the documented encounter. This assists coders and billers, potentially reducing under-coding (which leaves revenue on the table) and over-coding (which creates compliance risk). The coding suggestions are advisory — a certified coder or physician still makes the final determination.

Market Leaders and Competitive Landscape

The ambient documentation market has consolidated around three dominant players, with a long tail of emerging competitors:

Nuance DAX Copilot (Microsoft) — 33% Market Share

Microsoft's acquisition of Nuance Communications for $19.7 billion in 2022 was a bet on AI-powered clinical documentation — and it is paying off. DAX Copilot is deployed across 77% of US hospitals in some capacity, making it the most widely available ambient AI platform. Its tight integration with Microsoft's Azure AI infrastructure and its growing embedding within Epic, Oracle Health, and other EHR platforms give it a distribution advantage that is difficult for competitors to match. DAX Copilot processes encounters across primary care, specialty care, and emergency medicine. Vendors like AZZLY Rize have integrated DAX Copilot for behavioral health documentation workflows.

Abridge — 30% Market Share

Abridge has emerged as the primary challenger, securing major enterprise contracts. Its most notable deployment is with Kaiser Permanente, which is rolling out Abridge across 40 hospitals and hundreds of outpatient facilities. Abridge differentiates with a citation-based approach — its AI notes include clickable references back to the specific moments in the conversation that support each documentation element, giving physicians a clear audit trail and increasing trust in the output. Abridge also has deep Epic integration and has secured partnerships with several academic medical centers.

Ambience Healthcare — 13% Market Share

Ambience positions itself as a full-stack clinical AI platform, going beyond documentation to include real-time coding suggestions, referral letter generation, and clinical quality measure tracking. It has gained traction in multi-specialty group practices and health systems that want a broader AI toolkit. Ambience has raised significant venture capital and is expanding its EHR integration partnerships.

Other Notable Players

• Suki AI — Voice-powered AI assistant that works across multiple EHR platforms. Focuses on note generation and coding assistance.
• DeepScribe — AI medical scribe targeting specialty practices, with particular strength in surgical specialties.
• Nabla — European-origin ambient documentation tool gaining traction in US markets, known for strong multilingual support.
• Heidi Health — Growing player in the primary care space, popular with independent practices for its simplicity and pricing.

Beyond Documentation: AI Applications Across the EHR

While ambient documentation dominates headlines, AI is being embedded into EHR systems across multiple clinical and administrative workflows. Here are the areas seeing real-world deployment:

Clinical Decision Support (CDS)

AI-powered CDS goes beyond the traditional rule-based alerts that clinicians have learned to ignore. Modern CDS uses machine learning models trained on millions of patient records to surface contextual, patient-specific insights at the point of care. Examples include flagging potential drug interactions that rule-based systems miss, identifying patients at elevated risk for sepsis or deterioration based on subtle vital sign trends, and recommending evidence-based treatment protocols tailored to the patient's specific comorbidity profile.

Predictive Analytics

EHR-integrated predictive models analyze historical patient data to forecast clinical events before they occur. Hospital readmission risk scores, no-show prediction for scheduling optimization, and population health stratification (identifying which patients in a panel are most likely to need intervention) are all in production use. These models help practices move from reactive to proactive care — a shift that both improves outcomes and aligns with value-based payment models.

Automated Coding and Revenue Cycle Intelligence

AI is addressing revenue leakage by analyzing clinical documentation and suggesting appropriate billing codes with higher accuracy than manual coding alone. Some platforms identify under-coded encounters (where the documentation supports a higher-complexity code than what was selected), potentially recovering 5-15% in missed revenue. On the compliance side, AI can flag encounters that appear over-coded relative to the documentation, reducing audit risk.

Patient Communication and Engagement

AI is transforming how practices communicate with patients. eClinicalWorks launched healow Genie, a patient-facing AI assistant that can answer questions about visit summaries, medications, lab results, and preventive care recommendations in plain language — drawing from the patient's actual EHR data. Similar AI chat interfaces are emerging across vendors, reducing the volume of routine phone calls to the front desk and enabling patients to get answers to basic questions 24/7.

Inbox and Message Management

The EHR inbox has become a significant contributor to physician after-hours workload. AI tools now draft responses to patient portal messages, summarize incoming referral documents, prioritize lab results by urgency, and triage incoming messages by clinical acuity. Epic's AI-powered inbox management features, for example, can draft suggested replies to patient messages that the physician reviews and sends — reducing the time spent on each message from minutes to seconds.

The Physician Burnout Connection: Why AI Documentation Matters

To understand why ambient AI documentation is being adopted at unprecedented speed, you have to understand the severity of the problem it solves.

The EHR usability landscape is grim. The System Usability Scale (SUS) is the industry-standard measure of software usability, with scores above 68 considered "acceptable." EHR systems collectively score a 45.9 out of 100 — an "F" grade. This makes EHRs some of the least user-friendly software in any professional domain.

The time burden quantifies why. Research consistently shows that physicians spend an average of 5.9 hours per day directly interacting with their EHR — charting, ordering, reviewing results, responding to messages. On top of that, clinicians spend an additional 1.4 hours per day on EHR tasks after clinic hours, a phenomenon the medical community calls "pajama time" because physicians are completing notes from home in the evening. For family medicine physicians specifically, the after-hours burden averages 86 minutes nightly.

This documentation burden is a direct driver of physician burnout. The Medscape Physician Burnout and Depression Report consistently finds that bureaucratic tasks — of which EHR documentation is the largest — are the number-one cited cause of burnout across specialties. Burnout, in turn, drives physician turnover, which costs healthcare organizations an estimated $500,000 to $1 million per physician departure (including recruitment, onboarding, and lost revenue during vacancy).

AI ambient documentation directly targets the root cause. If a physician saves 2.5 hours per day on documentation — the figure NextGen reports for its Ambient Assist users — that translates to either seeing more patients (revenue increase), finishing work on time (burnout reduction), or both. This is why adoption is outpacing virtually every other healthcare IT innovation: the value proposition is immediately tangible.

Which EHR Vendors Offer AI Features

The competitive landscape has shifted dramatically. Virtually every major EHR vendor now offers — or is actively developing — AI capabilities. Here is what the leading platforms deliver as of early 2026:

Epic Systems

Epic has moved aggressively into AI. Its ambient documentation integration (primarily through DAX Copilot partnership) is deployed across the majority of its hospital base — 62.6% of Epic hospitals by June 2025. Beyond ambient, Epic offers AI-powered inbox management (draft responses to patient messages), predictive deterioration alerts, and a growing suite of generative AI tools for clinical summarization. Epic's scale advantage means its AI models are trained on one of the largest clinical datasets in the world.

Oracle Health (Cerner)

Oracle launched its Clinical AI Agent — a generative AI assistant embedded within the Oracle Health EHR platform covering 30+ medical specialties. The Clinical AI Agent assists with documentation, clinical summarization, evidence-based recommendations, and order entry workflows. Oracle is leveraging its cloud infrastructure (OCI) and its acquisition of Cerner to build an integrated AI platform that connects clinical, operational, and financial data.

eClinicalWorks

eClinicalWorks has been one of the most prolific vendors in AI feature development, with 125+ AI-powered features across its platform. Notable innovations include ChatGPT integration for clinical summarization and patient communication, and healow Genie — a patient-facing AI assistant that answers patient questions using their own EHR data. eClinicalWorks serves 150,000+ providers and is rapidly deploying AI across its ambulatory user base.

NextGen Healthcare

NextGen's Ambient Assist is its flagship AI product, and the results are compelling: clinicians report saving an average of 2.5 hours per day on documentation. NextGen has also integrated AI into its revenue cycle management tools and patient engagement platform. As a vendor focused on ambulatory and specialty practices, NextGen's AI features are particularly well-tuned for the outpatient workflow where documentation burden is highest.

AZZLY Rize

AZZLY Rize, a cloud-native EHR purpose-built for behavioral health and substance abuse treatment, has integrated Microsoft DAX Copilot into its documentation workflow. This brings ambient AI documentation to a specialty vertical — behavioral health — where session note documentation is particularly time-consuming due to the narrative nature of therapy encounters. The integration means behavioral health clinicians can focus on the therapeutic relationship during sessions rather than on documenting what was said.

Valant

Valant, another behavioral health-focused EHR, offers AI-assisted documentation features designed for the unique documentation requirements of psychiatry, psychology, and counseling. Valant's AI tools assist with progress note generation, treatment plan updates, and outcome measure tracking — workflows that are specific to behavioral health and distinct from general medical documentation.

Adoption Challenges and Barriers

Despite the momentum, AI adoption in healthcare faces real obstacles. Understanding these challenges is essential for any practice evaluating AI-powered EHR features.

Cost and ROI Uncertainty

AI features add cost — either through higher EHR subscription tiers, per-provider add-on fees, or separate product licenses. For a small practice already stretched by EHR costs, adding $100-$300 per provider per month for ambient documentation represents a meaningful budget line. The ROI is real (time savings, additional patient volume capacity, reduced burnout-driven turnover), but it requires upfront investment before benefits materialize.

Accuracy and Hallucination Risk

Large language models can "hallucinate" — generating plausible-sounding but factually incorrect information. In a clinical documentation context, this could mean inserting a medication the patient isn't taking, omitting a critical symptom, or attributing a finding to the wrong body system. While the incidence of clinically significant errors is low in mature ambient products, it is not zero. Every AI-generated note must be reviewed by the signing physician, and practices need to build this review step into their workflow — not shortcut it.

Medical Liability Questions

If an AI-generated note contains an error that contributes to a misdiagnosis or adverse event, who is liable — the physician who signed the note, the EHR vendor, or the AI company? The legal framework is still evolving. Most malpractice carriers currently hold the signing physician responsible for the accuracy of the medical record, regardless of how the note was drafted. This means physicians cannot abdicate review responsibility to the AI. Practices should consult their malpractice carrier about their specific coverage position on AI-assisted documentation.

Integration Complexity

Not all AI tools integrate seamlessly with all EHR platforms. A standalone ambient documentation tool that doesn't write directly into the EHR creates a fragmented workflow — the physician still needs to copy/paste or manually transfer the note. The best implementations feature tight, bidirectional integration: the AI tool reads the patient context from the EHR (appointment type, chief complaint, medication list) and writes the completed note directly back into the correct chart section. When evaluating products, prioritize native or deeply integrated solutions over bolt-on tools.

The Metro vs. Rural Adoption Gap

AI adoption is not uniform across geography. Data shows a significant gap: 64.7% of metropolitan practices have adopted AI tools compared to 54.3% of rural practices. This gap is driven by multiple factors — lower broadband availability (ambient tools require reliable internet for cloud processing), smaller practice sizes with less capital for technology investment, fewer vendor sales resources focused on rural markets, and a more conservative technology adoption culture. Closing this gap is important for health equity, as rural patients deserve the same quality and efficiency benefits that AI delivers in urban settings.

How to Evaluate AI Features When Selecting an EHR

AI capabilities are becoming a legitimate differentiator in the EHR selection process. Here is a practical framework for evaluating AI features during your vendor evaluation:

1. Ask About Native vs. Third-Party AI

Is the AI built into the EHR platform, or is it a third-party partnership? Both can work, but native integrations typically offer tighter workflow embedding, better data context (the AI has access to the full patient chart), and simpler procurement. Third-party tools offer more flexibility to switch AI vendors independently of your EHR, but may require separate contracts, logins, and support channels.

2. Evaluate Specialty-Specific Training

A general-purpose ambient AI model may struggle with the terminology, documentation patterns, and note structures specific to your specialty. If you run a behavioral health practice, dermatology clinic, or surgical center, ask whether the AI model has been trained on encounters from your specialty. Vendors like AZZLY Rize and Valant focus their AI training specifically on behavioral health workflows, which produces better results for that specialty than a generic model would.

3. Demand Accuracy Metrics

Ask the vendor for published or internal accuracy data. Relevant metrics include: word error rate (WER) for speech recognition, clinical accuracy rate for generated notes (percentage of notes requiring zero or minimal edits), and specific performance data for your specialty or patient population. Be skeptical of vendors who provide only anecdotal testimonials without quantitative metrics.

4. Understand the Data Privacy Model

AI tools that process patient conversations handle some of the most sensitive data in healthcare. Key questions: Where is the audio processed and stored? Is the audio deleted after note generation, or retained? Is patient data used to train the AI model (and can you opt out)? Does the vendor sign a BAA? Is the AI processing HIPAA-compliant end-to-end? Cloud-based AI processing is standard, but the vendor's data handling practices matter enormously.

5. Test with Your Actual Workflows

Do not evaluate AI features through a canned demo alone. Insist on a pilot with your actual clinicians, your actual patient encounters (with appropriate consent), and your actual EHR configuration. A product that performs beautifully in a controlled demo may struggle with your clinic's background noise, your patient population's language diversity, or your documentation style preferences. A 2-4 week pilot with 3-5 clinicians gives you real-world data to inform your decision.

6. Calculate the Real ROI

Build a business case with real numbers. If the AI tool costs $200/provider/month and saves 2 hours/day, calculate what those 2 hours are worth. If the physician can see 2-3 additional patients per day, multiply by the average revenue per visit. If the time savings reduces overtime or eliminates the need for a human scribe ($36,000-$50,000/year), factor that in. If the tool reduces physician turnover by improving satisfaction, the $500K+ cost of replacing a physician dwarfs the AI subscription cost.

Frequently Asked Questions

What Comes Next

We are still in the early chapters of AI's transformation of healthcare IT. The ambient documentation wave is the opening act — highly visible, immediately valuable, and setting the stage for deeper AI integration across clinical workflows.

In the near term (2026-2027), expect to see: AI-powered prior authorization automation that eliminates one of the most universally despised administrative tasks; AI clinical summarization that produces patient handoff summaries, referral letters, and care transition documents automatically; multimodal AI that integrates clinical imagery (dermatology photos, radiology scans) with documentation and decision support; and increasingly sophisticated predictive models embedded directly into EHR workflows rather than siloed in separate analytics platforms.

The longer-term trajectory points toward an AI co-pilot model where the EHR evolves from a passive record-keeping system to an active clinical partner — surfacing relevant information, anticipating the physician's next action, and handling routine cognitive tasks so clinicians can focus on the complex judgment calls that require human expertise.

For practices selecting or switching EHRs today, the strategic implication is clear: AI capability is no longer a nice-to-have — it is a core evaluation criterion. The vendor you choose now will determine how quickly and effectively you can leverage AI tools that are rapidly moving from competitive advantage to table stakes. Factor AI into your EHR selection process, evaluate it rigorously during your implementation planning, and ensure your deployment model supports the cloud infrastructure that AI requires.