AI in Clinical Documentation: How It Works, Who Uses It, and What to Expect

AI clinical documentation uses natural language processing, speech recognition, and large language models to generate medical notes from patient encounters. These tools range from ambient scribes that listen passively during visits to NLP engines that extract structured data from free-text records. The technology has matured past the proof-of-concept stage: a 2025 multicenter quality-improvement study in JAMA Network Open reported lower burnout after ambient AI scribe rollout, with burnout falling from 51.9% at baseline to 38.8% at 30 days among clinicians who completed follow-up.

What most AI documentation tools miss is the clinical reasoning layer. They transcribe and structure the conversation, but they do not help you think through the case. Glass Health pairs ambient AI scribing with real-time clinical decision support – surfacing ambient insights during the encounter and then generating differential diagnoses, assessment-and-plan recommendations, and clinical notes from the same encounter. That distinction matters for note quality, treatment planning, and patient safety.

This guide covers how AI clinical documentation works at a technical level, what the tools actually produce, why generative assessment-and-plan support changes note quality, and how to implement these tools without disrupting your practice.

Why Is AI Transforming Clinical Documentation?

Physicians already know documentation takes too long. The numbers confirm how severe the problem has become.

A landmark time-motion study in Annals of Internal Medicine found that physicians spent nearly two hours on EHR and desk work for every hour of direct patient care in ambulatory practice ( Sinsky et al., 2016). That burden is one reason documentation remains such a durable target for workflow automation.

The AMA’s physician burnout coverage continues to identify documentation and EHR burden as a major contributor to physician dissatisfaction and burnout ( AMA, 2024). The problem is structural, not anecdotal.

The downstream effects extend past burnout. Physicians who spend evenings finishing charts see fewer patients per session while working more total hours. Rushed documentation produces incomplete notes, weaker handoffs, and more follow-up ambiguity.

AI documentation tools attack this problem by generating structured note drafts from encounter data, whether that data comes from ambient audio, existing EHR records, or a combination. The clinician reviews and signs the note rather than building it from scratch. KLAS and real-world deployment studies both suggest the main value is less after-hours charting and lower edit burden, not just raw transcription speed.

The evidence that this works at scale arrived in 2025. A multicenter study published in JAMA Network Open followed 263 clinicians across six health systems after ambient AI scribe rollout. Burnout dropped from 51.9% to 38.8% within 30 days among the clinicians who completed the follow-up surveys ( JAMA Network Open, 2025).

Grand View Research estimates the global clinical documentation improvement market will reach $7.1 billion by 2030. That growth reflects both the severity of the burden and the increasing maturity of AI tools that can address it without requiring physicians to change how they practice medicine.

What Types of AI Documentation Tools Exist?

Not all AI documentation tools work the same way. The category has four distinct approaches, each with different workflow implications, accuracy profiles, and use cases.

Ambient AI Scribes

Ambient AI scribes passively listen to the clinician-patient conversation and generate structured notes in real time. The clinician does not need to dictate, press buttons, or interact with the software during the visit. The tool captures the encounter as it happens and produces a draft note by the time the visit ends.

Tools in this category include Glass Health, Freed, Abridge, DeepScribe, and Nuance DAX Copilot (Microsoft). Ambient scribes have become the fastest-growing segment of AI clinical documentation because they eliminate the most painful part of charting: the after-visit documentation session.

Glass Health differentiates from other ambient scribes by combining the documentation output with real-time encounter insights and generated clinical reasoning. During the visit, Glass can suggest history questions, physical exam maneuvers, and next steps; after the visit, it can generate a differential diagnosis and evidence-based assessment and plan for clinician review. That matters because the assessment-and-plan section is where many notes become either clinically useful or clinically thin.

NLP-Based Documentation Engines

NLP-based documentation engines analyze existing free-text clinical notes and extract structured data: diagnoses, medications, procedures, clinical findings, and severity indicators. These tools are most commonly used in hospital-based clinical documentation integrity (CDI) programs, where CDI specialists review inpatient records to ensure that notes capture the needed diagnostic specificity and illness severity.

These are not ambient tools. They work on notes that have already been written, scanning for missing specificity, unaddressed comorbidities, or documentation gaps that could result in DRG downgrade. Vendors in this space include 3M/Solventum, Optum, and Nuance’s CDI products.

For outpatient practices, NLP-based engines are less relevant. Their value is highest in inpatient and hospital-based settings where DRG accuracy directly drives reimbursement.

Template-Based AI Assistants

Template-based AI assistants use clinician-defined note templates enhanced with auto-fill capabilities. The AI populates note sections based on EHR data, prior visit records, medication lists, and structured inputs. The clinician provides the framework; the AI fills in the predictable sections.

This approach offers more clinician control over note structure but requires more manual interaction than ambient systems. It works well for practices with highly standardized visit types – a diabetes management clinic, for example, where the note structure is nearly identical across encounters and the variable data (A1c, medication adjustments, foot exam findings) can be populated automatically.

The tradeoff is that template-based tools do not capture the open-ended clinical conversation. They miss nuance, patient-reported concerns that fall outside template fields, and the clinical reasoning that happens verbally during the visit.

Dictation with AI Enhancement

AI-enhanced dictation builds on traditional speech-to-text by adding contextual understanding. Rather than producing a raw transcript that needs reformatting, these tools interpret clinical intent and organize dictated content into note sections. If a physician dictates “lungs are clear bilaterally, no wheezing, rhonchi, or rales,” the tool places this in the respiratory section of the physical exam rather than dumping it into a transcript block.

Nuance Dragon Medical One is the most established tool in this category. The approach preserves the physician’s authorial control (you say exactly what you want in the note) while reducing the formatting labor. The limitation is that the physician still has to dictate, which takes longer than reviewing an AI-generated draft and adds cognitive load during or after the visit.

A systematic review in the Journal of Biomedical Informatics found that NLP-based documentation tools can achieve strong but variable performance in extracting clinical entities from unstructured text, with results differing by specialty and note type ( Journal of Biomedical Informatics, 2024). Ambient scribes operating on real-time audio also show meaningful performance gains, though head-to-head comparisons across vendors remain limited.

How Does AI Generate a Clinical Note?

The pipeline from spoken conversation to signed clinical note involves five stages. Understanding each one helps clinicians evaluate tool quality and recognize where errors are most likely to occur.

Stage 1: Audio Capture and Speaker Diarization

For ambient tools, the process begins with recording the clinician-patient encounter. Modern ambient scribes use multi-speaker diarization – an algorithm that distinguishes who is speaking at each point in the conversation. This matters because patient-reported symptoms belong in the Subjective section while clinician statements about physical exam findings or diagnostic reasoning belong elsewhere.

Diarization accuracy directly affects note quality. If the system attributes a patient’s description of their symptoms to the clinician, the HPI section will read incorrectly. Current diarization models handle two-speaker encounters (clinician and patient) well. Encounters with additional speakers – interpreters, family members, trainees – remain more challenging, though performance is improving.

Glass Health’s ambient capture works across office visits, telehealth sessions, follow-up appointments, and new patient evaluations. Telehealth visits often produce higher-fidelity audio because there is less background noise compared to a busy exam room.

Stage 2: Clinical NLP and Entity Extraction

The raw transcript passes through a clinical NLP layer that identifies medically relevant content. This is more than keyword spotting. The model must understand that “my sugar has been running high” means hyperglycemia, that “I stopped taking the lisinopril because it made me cough” contains both a medication discontinuation and an adverse drug reaction, and that “my mom had a heart attack at 52” is a family history element, not a current complaint.

Entity extraction pulls out structured clinical data: chief complaints, HPI elements (location, quality, severity, timing, context, modifying factors, associated signs/symptoms), review of systems findings, physical exam details, assessment reasoning, and plan components. The quality of this extraction depends on the clinical language models underlying the tool, which are trained on millions of medical encounters across specialties.

Stage 3: Note Structuring

The extracted clinical entities are organized into the appropriate note format. Glass Health supports multiple output formats:

• SOAP notes: The standard four-section format used across primary care, urgent care, and many specialties.
• H&P reports: Full history and physical notes for new patient evaluations, hospital admissions, and consultations.
• Progress notes: Focused follow-up documentation for established patients.
• Specialty-specific templates: Customized formats for visit types with unique documentation needs.

The structuring step is where the AI must make editorial decisions. Does a patient’s mention of poor sleep go in the HPI, the ROS, or the social history? Was the physician’s question about chest pain part of a cardiac ROS or a direct response to a symptom? These decisions require clinical judgment, which is why the best AI documentation tools train on physician-authored notes rather than generic language corpora.

Stage 4: The Clinical Decision Support Layer

This is where Glass Health’s architecture diverges from every other ambient scribe on the market.

Glass does not wait until the encounter is over to be useful. During the visit, it can surface ambient insights that track the chief complaint, refine the differential diagnosis, suggest history questions, and flag potential next steps as the conversation unfolds. After the visit, the same clinical context is used to generate a differential diagnosis and a structured assessment and plan for clinician review. This is not a generic autocomplete. The system considers the patient’s presentation, medications, relevant history, uploaded context, and clinical findings to generate reasoning support tied to the actual encounter.

Other ambient scribes stop at Stage 3. They produce a note that reflects what was said during the encounter. Glass produces a note that reflects what was said and provides clinical reasoning support for what should happen next. For a patient presenting with new-onset dyspnea, for example, the CDS layer would generate a differential that includes heart failure, COPD exacerbation, pulmonary embolism, and pneumonia – along with the workup and management considerations for each.

This integration matters for documentation quality because the assessment-and-plan section is the hardest part of the note to write well. A good draft helps the clinician produce a clearer treatment plan, a more coherent note, and a better handoff to the next reader.

Stage 5: Review and Finalization

The clinician reviews the AI-generated draft, makes corrections, and signs the note. This review step is not optional – it is a clinical and legal requirement. The physician is the author of record regardless of how the first draft was produced.

Well-tuned ambient scribes can materially reduce review time compared with writing from scratch. Clinicians develop a review pattern: scan the HPI for accuracy, confirm the medication list, verify the assessment matches their clinical thinking, and check the plan against what they actually ordered. The review is faster than writing because you are reading and editing rather than generating from scratch.

Glass Health’s structured output makes review more efficient because the generated assessment and plan gives the clinician a framework to react to rather than a blank section to fill in. The point is not to replace clinician judgment. The point is to give the clinician a better first draft of the reasoning and next steps so the final note and treatment plan are stronger after review.

What Does AI-Generated Documentation Look Like?

Comparing a manually written note to an AI-generated note for the same encounter shows the practical difference in documentation completeness.

Scenario: Hypertension Follow-Up Visit

A 58-year-old male presents for a 3-month follow-up of hypertension. He has been on amlodipine 10 mg daily for six months. His blood pressure today is 148/92. He reports occasional headaches and admits to inconsistent medication adherence, particularly on weekends. He has a family history of stroke (mother, age 67). His metabolic panel from last week shows a creatinine of 1.3 mg/dL (up from 1.1 six months ago).

Typical Manually Written Note (Time-Pressed Physician)

S: HTN f/u. Reports occasional HA. Admits to missing meds on weekends. BP 148/92.

O: BP 148/92. Otherwise unremarkable.

A: HTN, uncontrolled.

P: Add HCTZ 25 mg daily. Recheck BP in 4 weeks. Labs in 3 months.

This note took about 90 seconds to write. It is functional but clinically thin. It does not document the rising creatinine, the family history of stroke that elevates cardiovascular risk, the reason for choosing HCTZ, or any pertinent negatives. It does not explain why a second agent was added rather than switching agents or maximizing lifestyle intervention. Another clinician reading it would still have to reconstruct too much of the reasoning on their own.

Glass-Style IM Outpatient Note Excerpt

CLINIC NOTE

Chief Complaint: Blood pressure follow-up.

History of Present Illness: Mr. [Patient] is a 58-year-old male presenting for follow-up of hypertension. He reports occasional bifrontal headaches and acknowledges inconsistent weekend adherence to amlodipine. He denies chest pain, dyspnea, edema, or palpitations. Recent labs showed creatinine 1.3 mg/dL, increased from 1.1 mg/dL six months earlier.

Review of Systems: Negative except as noted in HPI.

Physical Examination:

Vitals: BP 148/92 mmHg, HR 78, BMI 31.2.

General: Well appearing, no acute distress.

Cardiovascular: Regular rate and rhythm, no murmurs, no edema.

Pulmonary: Clear to auscultation bilaterally.

Assessment and Plan

Clinical Impression: Persistent hypertension with adherence issues and early renal risk, requiring medication adjustment and short-interval follow-up.

# Hypertension, uncontrolled

Blood pressure remains above goal despite amlodipine 10 mg daily. Weekend missed doses are likely contributing, but the creatinine trend also increases the importance of selecting a renoprotective next step.

Dx.

• Review home blood pressure log over the next 2 weeks.
• Repeat BMP after medication change to reassess creatinine and potassium.

Tx.

• Start lisinopril 10 mg daily and continue amlodipine 10 mg daily.
• Reinforce daily adherence strategy with pill organizer and phone reminder.
• Counsel on sodium reduction and home BP monitoring.

# Cardiometabolic risk

Family history of stroke and elevated BMI increase longer-term vascular risk and should be addressed in the same follow-up workflow.

Dx.

• Recheck lipid panel if not current before the next visit.

Tx.

• Discuss exercise target and dietary changes at follow-up.
• Consider statin initiation after updated lipid review and shared decision-making.

This is closer to what clinicians actually need from a strong AI documentation tool: a readable note plus a structured clinical impression, problem-based assessment, and concrete Dx/Tx next steps that the clinician can rapidly review and modify.

Why Generated Assessment and Plans Matter

The most valuable part of advanced documentation is not the summarization alone. It is the ability to turn the encounter into a better first draft of the clinician’s reasoning.

In most notes, the assessment and plan is where quality either rises or collapses. A weak note restates the complaint and lists a medication change. A strong note explains the working impression, organizes active problems, documents what still needs to be clarified, and makes the next diagnostic and treatment steps explicit. That is exactly where generated assessment-and-plan support can help.

When Glass produces a generated assessment and plan, the clinician still reviews and owns the final note. But the clinician is no longer starting from a blank page. They are reacting to a problem-based draft that can make the final treatment plan, follow-up instructions, and documentation more complete than a pure transcript summary would have produced.

Current Market: Who Competes and How They Compare

The AI clinical documentation market in 2026 includes vendors ranging from venture-backed startups to Microsoft. Here is how the major players compare across the dimensions that matter for clinical adoption.

What the Table Does Not Show

The most important distinction is not feature-list depth but what the assessment and plan section contains.

Freed and DeepScribe generate assessment and plan sections by summarizing what the clinician said during the encounter. If the clinician stated a diagnosis and treatment plan verbally, it appears in the note. If the clinician did not verbalize their reasoning, the A&P is thin or empty.

Abridge takes a similar approach but is aimed most squarely at Epic-centered health systems and large documentation rollouts that are not prioritizing native CDS in the same platform, which makes it a weaker fit for independent practices and smaller groups.

Glass Health generates the A&P using clinical decision support that references evidence-based guidelines, independent of what the clinician said during the conversation. This means the documentation includes clinical reasoning, differential considerations, and management rationale even for straightforward encounters where the physician did not think out loud. That difference directly affects treatment planning, note quality, and clinical safety. See our detailed best AI medical scribe comparison for a deeper evaluation.

How to Implement AI Documentation in Your Practice

Deploying AI documentation is less about the technology and more about workflow integration. Practices that treat it as a software install rather than a workflow change tend to underperform on adoption and satisfaction metrics. Here is a phased approach based on patterns from successful implementations.

Phase 1: Pilot (Weeks 1-4)

Select 3-5 clinicians across different visit types – a mix of new patient evaluations, chronic disease follow-ups, and acute visits. Run the AI tool alongside existing workflows for the first week, then transition to AI-primary documentation for the remaining three weeks.

Measure three things during pilot: (1) time per note, including review and editing; (2) correction burden, meaning the percentage of AI-generated content that clinicians change before signing; and (3) clinician satisfaction on a simple 5-point scale. Do not over-interpret small-sample workflow differences during pilot – the sample size is too small.

Glass Health’s free tier supports an initial pilot without financial commitment, with limited ambient scribing and limited clinical decision support so clinicians can evaluate workflow fit before moving to paid tiers. Start a free pilot.

Phase 2: Calibration (Weeks 5-12)

Adjust templates, note format preferences, and specialty configurations based on pilot feedback. This is where most practices discover that their existing note templates were designed for manual entry and do not optimize for AI-generated content. A SOAP note template that made sense when physicians were typing from scratch may need restructuring when the AI is generating the first draft.

Establish QA protocols: random chart audits (5% sample is sufficient), denial rate monitoring, and an escalation path for AI output that clinicians flag as incorrect or incomplete. Assign a clinical champion – a physician who is the point person for feedback, questions, and workflow adjustments.

Phase 3: Expansion (Weeks 13-20)

Roll out to the full clinical team. Stagger by specialty or pod to maintain support capacity. Track adoption metrics (percentage of encounters using AI documentation), documentation quality scores (from your QA audits), and clinician satisfaction with review speed and note usefulness.

Expect a temporary dip in efficiency during the first week of expansion as new users develop their review workflow. This normalizes quickly.

Phase 4: Optimization (Ongoing)

Use chart-audit findings and clinician input to refine AI performance over time. Monitor for accuracy drift – the tendency for clinicians to become less careful in review as they develop trust in the tool. Periodic chart audits maintain quality.

The Documentation-Reasoning Integration: Why Scribing Alone Is Not Enough

Every ambient AI scribe on the market solves the same problem: converting the clinician-patient conversation into a structured note. That is a real and valuable capability. But documentation is not the only thing that happens during a clinical encounter. Clinical reasoning happens too. And the two are connected in ways that current scribing-only tools ignore.

When a physician sees a patient with new-onset atrial fibrillation, the documentation task is recording the encounter. The reasoning task is working through the CHA2DS2-VASc score, deciding between rate and rhythm control, choosing an anticoagulant, and identifying reversible contributing factors like thyroid disease or alcohol use. These are separate cognitive processes, but they produce output that belongs in the same note.

Scribing-only tools capture the first process. Glass Health supports both.

This matters for three practical reasons.

Completeness. A physician who manages atrial fibrillation during a 15-minute visit may not verbalize every element of their reasoning. They may calculate CHA2DS2-VASc mentally, check the medication list for interactions in the EHR, and order labs without narrating each step. A scribing-only tool captures what was said; Glass’s CDS layer generates documentation of the clinical reasoning regardless of whether it was verbalized.

Safety. The differential diagnosis is a cognitive second opinion. For a patient presenting with chest pain, the CDS engine generates a differential that includes ACS, PE, pericarditis, musculoskeletal pain, and GERD – surfacing diagnoses that the clinician may have already considered but ensuring none are overlooked. This is clinical decision support in its original meaning: helping clinicians think through cases, not just documenting what they already decided.

Education. Residents and early-career physicians use the CDS-generated assessment and plan as a learning scaffold. Seeing a structured differential and evidence-based management plan for each encounter accelerates clinical reasoning development. The documentation becomes a teaching tool, not just a transcript artifact.

This is where Glass Health is differentiated. Freed, Abridge, and DeepScribe produce notes. Glass Health produces notes and adds a clinical reasoning layer in the same workflow.