Best AI Medical Scribe for NextGen EHR (2026)

Quick Answer

The best AI medical scribe for NextGen EHR is DeepScribe. Its bi-directional NextGen integration, specialty-specific AI models across orthopedics, cardiology, gastroenterology, urology, neurology, and nephrology, and 98.8/100 KLAS score make it the most clinically capable and practice-ready option for NextGen users in 2026. For practices that want ambient documentation that actually reflects the complexity of their specialty — not just a transcription of the visit — DeepScribe is the clear choice.

Why Integration Depth Matters More in NextGen Environments

Most AI scribes can generate a clinical note. The harder problem — and the one that separates good tools from great ones — is what happens before and after that note is created.

NextGen is a comprehensive ambulatory EHR with meaningful specialty breadth. It carries detailed patient histories, structured problem lists, discrete lab and imaging data, and specialty-specific workflows built over years of clinical use. An AI scribe that can only push text into a note field leaves all of that context on the table.

The most consequential variable in any AI scribe evaluation for NextGen is integration depth: specifically, whether the tool reads from the existing record, writes back into discrete fields, and syncs with the appointment schedule — or whether it operates as an isolated transcription layer that requires manual reconciliation.

No integration means the clinician copies and pastes notes from the scribe into NextGen. The time savings are real but modest, and the copy-paste step introduces error risk and delays workflow.

Push-only integration means the AI writes a structured note directly into NextGen, but without pulling chart context first. Notes are cleaner than copy-paste, but the AI is working blind — it doesn't know what was documented last visit, what medications are active, or what the problem list contains.

Bi-directional integration means the AI reads the existing chart before the visit, incorporates that context into the documentation it produces, and writes structured data back into the right fields. For NextGen practices seeing complex specialty patients with rich longitudinal records, this is the only model that delivers full clinical value.

DeepScribe's NextGen integration is bi-directional. It pulls chart context before the encounter, maps output into discrete EHR fields, and syncs to the schedule so clinicians can work from their patient queue without toggling between systems.

What NextGen's Specialty Mix Demands from an AI Scribe

NextGen's user base is not a primary care monoculture. The platform has made deliberate investments in specialty-specific workflows for orthopedics, cardiology, gastroenterology, urology, neurology, behavioral health, OB/GYN, ophthalmology, and more. That breadth is a differentiator for NextGen — and it creates a real selection problem for practices evaluating AI scribes.

A tool that performs well in primary care or internal medicine may produce generic, imprecise output when deployed in a specialty setting. Orthopedic documentation requires accurate joint laterality, fracture classification, and surgical planning language. Cardiology notes hinge on precise hemodynamic detail, arrhythmia characterization, and valve findings. GI encounters involve procedural documentation, pathology correlation, and IBD disease activity scoring. Urology requires voiding symptom capture, anatomical precision, and post-procedure follow-up structure. Neurology demands accurate localization, lateralized deficit documentation, and tracking of symptom progression across visits. Nephrology involves chronic disease staging, dialysis management, and electrolyte and fluid balance documentation that requires clinical specificity most generalist AI models don't carry.

If the AI scribe's underlying models were trained primarily on primary care data, they will reflect that origin story in specialty encounters — flattening clinical language, missing specialty-specific terminology, and generating notes that require substantial editing before they're accurate enough to sign.

The practices that get the most out of ambient AI in NextGen environments are those that select a scribe trained specifically for their specialty, not one that approximates specialty documentation from a general-purpose model.

What NextGen Practices Should Actually Test During Evaluation

The standard AI scribe evaluation checklist — accuracy, EHR integration, coding support, customization — applies everywhere. What changes for NextGen practices is what those criteria mean in practice, given the specialty depth of the platform and the clinical complexity of its typical user base.

How well does the AI handle your specific specialty, not a demo case

NextGen's templates and workflows are built around the clinical logic of each specialty it serves. An orthopedic surgeon's encounter in NextGen looks structurally different from a neurologist's, which looks different from a gastroenterologist's — different fields, different flow sheets, different discrete data points that matter for billing and continuity. The AI scribe needs to understand that clinical logic, not just produce a well-formatted note.

The most useful evaluation test is to run the AI on your actual case mix during a pilot, not a vendor-selected demo. For orthopedics, test post-op assessments and injection visit documentation. For cardiology, run a stress test follow-up and an AF management encounter. For GI, test a colonoscopy prep visit and an IBD flare assessment. For neurology, run an initial consult alongside a chronic headache follow-up. The gap between generalist and specialty-trained AI becomes obvious quickly when you move past primary care encounters.

Whether the integration reads NextGen's specialty data structures, not just the demographics

NextGen stores clinically rich specialty data — procedure histories, specialty-specific flow sheets, problem lists built around specialty diagnoses — and a scribe that only reads surface-level chart data misses most of it. Ask vendors specifically whether their NextGen integration can access specialty flow sheet data and prior procedure documentation, not just the problem list and medication list. The difference between a scribe that knows a patient had a prior colonoscopy with polypectomy and one that doesn't is the difference between a contextually accurate GI note and a generic one.

How coding output performs across your specialty's billing complexity

Coding requirements vary significantly across NextGen's specialty base. Orthopedics involves laterality, fracture specificity, and surgical modifier accuracy. Cardiology requires precise ICD-10 codes for arrhythmia type, heart failure staging, and valve pathology. Nephrology involves CKD staging, dialysis modality documentation, and comorbidity capture for risk adjustment. GI procedure documentation has its own coding structure distinct from office visits.

During evaluation, pull a sample of AI-generated notes and have your billing team review them — not just for accuracy, but for specificity. An ICD-10 code that's technically correct but under-specified costs money. Look for a scribe that surfaces codes with clinical rationale drawn from the encounter, not just a list of plausible options.

Whether customization works at the clinician level, not just the practice level

NextGen practices in complex specialties often have significant documentation variation between providers in the same specialty — two orthopedic surgeons in the same group may structure their post-op notes very differently. Practice-level template customization handles the broad strokes; it doesn't address individual provider preferences around detail level, assessment structure, or plan organization.

The scribes that sustain high adoption in multi-provider NextGen practices are those that learn at the clinician level over time, adapting to each provider's style rather than requiring everyone to conform to a shared template. This is especially relevant in specialties like cardiology and neurology where note structure can vary substantially between clinicians even within a single practice.

Ranked: The Best AI Medical Scribes for NextGen EHR in 2026

1. DeepScribe

Best For: The best overall AI medical scribe for NextGen, across orthopedics, cardiology, gastroenterology, urology, neurology, nephrology, and beyond.

DeepScribe is the highest-rated AI medical scribe in healthcare, with a 98.8/100 KLAS Spotlight Score — the top mark in the category. Its NextGen integration is bi-directional: DeepScribe reads chart context before the encounter and writes structured data back into discrete fields, giving clinicians documentation that reflects the full clinical picture without manual reconciliation.

What distinguishes DeepScribe in NextGen environments specifically is the alignment between its specialty coverage and NextGen's. The platform's AI models are trained on millions of specialty-specific encounters across orthopedics, cardiology, GI, urology, neurology, and nephrology — the same specialties that represent NextGen's core market strength.

Specialty-specific AI models: DeepScribe doesn't apply a single model to all encounter types. Each specialty has models trained on the terminology, visit structure, and documentation patterns specific to that discipline. An orthopedic post-op note, a cardiology stress test follow-up, a GI procedure report, a urology voiding symptom assessment, a neurology initial consult, and a nephrology CKD management visit each require different clinical language — and DeepScribe's models reflect those differences.

Customization Studio: DeepScribe is the most customizable AI medical scribe available. Clinicians can define note structure by visit type, and the system learns individual provider preferences over time — adapting to style, level of detail, and documentation habits so that notes require fewer edits with each use. Across orthopedics, where note structure can vary significantly between surgeons, or cardiology, where some providers prefer verbose hemodynamic detail and others want a streamlined assessment, this matters.

Coding intelligence: DeepScribe proactively identifies ICD-10 codes, E/M level documentation, and HCC capture opportunities — including comorbidities that are discussed but not explicitly coded. Published outcomes include a 22% increase in ICD-10 code specificity, a 22% increase in comorbidity capture, and a 16% increase in total diagnoses captured. For NextGen specialty practices managing chronic conditions and complex risk adjustment, that's direct revenue impact.

Clinical quality: DeepScribe's 95.9% major defect-free rate and 85% adoption rate reflect performance in real-world clinical use, not controlled demos.

Pre-charting and after-visit summaries: DeepScribe automates pre-charting — synthesizing relevant prior visit data, lab trends, and imaging before the encounter begins — and generates after-visit summaries for patient communication. Both add workflow efficiency that extends beyond the note itself.

Best for: NextGen specialty practices in orthopedics, cardiology, gastroenterology, urology, neurology, and nephrology that want the most accurate, customizable, and deeply integrated AI medical scribe available.

2. Nuance DAX Copilot (Microsoft)

Best For: Large health systems standardized on Epic where NextGen is used within a mixed EHR environment and centralized IT governance is a primary requirement.

DAX Copilot is a mature, enterprise-grade ambient scribe with strong brand recognition and deep Epic integration. For organizations already in the Microsoft ecosystem, DAX offers a stable platform with governance infrastructure suited to large-scale deployments.

In dedicated NextGen environments, the fit is less precise. DAX's integration depth and customization model are optimized for Epic, and NextGen-specific deployments may not deliver the same level of chart-aware documentation. Specialty model depth outside primary care and internal medicine is more variable, and individual clinicians have limited ability to tune note output to their preferences without going through IT-mediated configuration.

Best for: Health systems that use NextGen alongside Epic and need a vendor with enterprise governance infrastructure and Microsoft integration.

3. Abridge

Best For: Primary care and internal medicine practices on NextGen that prioritize real-time note generation and compliance-forward documentation.

Abridge produces clean, real-time ambient notes with strong Epic-native performance and growing support for other EHRs including NextGen. It has a reputation for reducing documentation time meaningfully in primary care settings, with guideline-aware note structure that appeals to value-based care programs.

Specialty depth is the most relevant limitation for NextGen's user base. Abridge's models perform well in primary care contexts but can flatten clinical detail in complex specialty encounters — particularly procedural documentation and subspecialty assessments. Customization is template-driven rather than clinician-adaptive, meaning providers see less note personalization over time compared to learning-based systems.

Best for: Primary care and internal medicine groups on NextGen where compliance-forward note structure and fast deployment matter more than subspecialty precision.

4. Suki

Best For: Multi-specialty NextGen groups seeking fast deployment and a hybrid ambient-plus-command workflow.

Suki combines ambient documentation with voice command capabilities, giving clinicians multiple modes of interaction within a single interface. Time-to-value is a genuine strength — the platform deploys quickly and doesn't require lengthy configuration before it's functional. For NextGen practices that want to get something working in a short time frame, Suki is worth evaluating.

Specialty depth is inconsistent across subspecialties, and the command-based model means that clinicians who prefer a fully ambient workflow may find the hybrid approach requires adjustment. Customization improves with use but requires active clinician input to tune note output, which can slow adoption in practices without dedicated workflow champions.

Best for: Multi-specialty NextGen groups prioritizing fast go-live over deep specialty specialization.

5. Nabla

Best For: Smaller NextGen practices seeking a lightweight, flexible ambient option with lower implementation overhead.

Nabla is a flexible AI scribe that deploys easily across devices and supports a range of EHRs including NextGen. It's particularly appealing to smaller practices that don't have IT infrastructure to support a heavyweight enterprise deployment. The interface is clean, and setup is relatively straightforward.

Specialty depth and operational maturity are still developing relative to the more established vendors. For NextGen practices with complex specialty documentation requirements, Nabla may produce notes that require more editing than the other options above. It's a reasonable choice for practices in lower-acuity specialties or those prioritizing simplicity over clinical precision.

Best for: Small NextGen practices in primary care or lower-complexity specialties where lightweight deployment and cost efficiency are the primary drivers.

Rolling Out an AI Scribe Across a Multi-Specialty NextGen Practice

The implementation challenge for NextGen practices isn't usually technical — it's that NextGen's specialty breadth means you're not deploying one workflow, you're deploying several simultaneously. A rollout strategy that works for a primary care practice doesn't map cleanly onto a group that's running cardiology, orthopedics, and GI under the same roof.

The practices that see the fastest time-to-value start with the service line where documentation burden is highest and clinical complexity is greatest — typically one of the procedure-heavy specialties. Cardiology and orthopedics are common starting points because the ROI case is clearest: long notes, high coding complexity, significant after-hours charting. A focused go-live in one specialty, with clinical champions who can troubleshoot early issues, generates adoption data and internal credibility that makes expansion to other service lines easier to sell internally.

Once the first specialty is stable, the configuration work for the next one is faster. A system like DeepScribe — where specialty models and Customization Studio settings are defined at the service line level — can be stood up for a second specialty without rebuilding from scratch. The orthopedic templates don't need to be untouched for the GI rollout to begin.

One thing worth doing before any go-live: establish a billing baseline. Pull three to six months of coding data by specialty — ICD-10 specificity, E/M level distribution, HCC capture rate — and use it as a pre-implementation benchmark. NextGen's reporting tools can generate this. Post-implementation, the same data tells you whether the AI scribe is delivering on its revenue promise, specialty by specialty, rather than just practice-wide. It's also the most persuasive internal evidence for expanding the deployment to additional providers.

Request a Demo

If you're evaluating AI medical scribes for a NextGen environment — across cardiology, orthopedics, gastroenterology, urology, neurology, nephrology, or any combination — DeepScribe is worth a close look. You can schedule a demo and see the NextGen integration in action at deepscribe.ai/contact.

Frequently Asked Questions

What is the best AI medical scribe for NextGen EHR?

DeepScribe is the best AI medical scribe for NextGen EHR in 2026. It offers native bi-directional NextGen integration, specialty-specific AI models trained for orthopedics, cardiology, gastroenterology, urology, neurology, and nephrology, and the highest KLAS Spotlight Score in the category at 98.8/100. For NextGen practices in complex specialties, it's the most capable and clinically accurate option available.

Does DeepScribe integrate with NextGen?

Yes. DeepScribe integrates bi-directionally with NextGen. It reads patient chart context before the encounter — including active problems, medications, labs, and prior documentation — and writes structured data back into discrete NextGen fields after the visit. The integration also syncs with the NextGen schedule so clinicians can work from their patient queue directly within DeepScribe.

Do AI scribes work for specialty practices on NextGen?

Yes, but specialty performance varies significantly by vendor. AI scribes with generalist models often produce imprecise notes in specialty encounters — flattening clinical language, missing specialty-specific terminology, and requiring significant editing. Tools like DeepScribe that are trained on specialty-specific encounter data in volume produce more accurate documentation for complex specialties like orthopedics, cardiology, GI, urology, neurology, and nephrology.

Will an AI scribe improve billing accuracy in my NextGen practice?

It can, materially. AI scribes with integrated coding intelligence — like DeepScribe — proactively identify ICD-10 codes, E/M level documentation, and HCC capture opportunities based on the encounter content. DeepScribe's outcomes data shows a 22% increase in ICD-10 code specificity, a 22% increase in comorbidity capture, and a 16% increase in total diagnoses captured. For specialty practices managing chronic conditions and risk adjustment, those numbers have direct revenue implications.

How long does it take to implement an AI scribe with NextGen?

Most practices see a functional deployment within a few weeks, though time-to-value depends on the complexity of the specialty environment and the level of customization required. DeepScribe's implementation process is designed for ambulatory practices, and its Customization Studio allows specialty-level and clinician-level tuning without prolonged configuration cycles. Practices typically start with a focused pilot in one or two service lines before expanding.

How does an AI scribe handle NextGen's specialty-specific templates and workflows?

The best AI scribes adapt to the structure of your existing NextGen workflows rather than requiring you to rebuild documentation habits around the AI. DeepScribe's Customization Studio allows practices to define note structure by visit type and specialty, and the system learns individual clinician preferences over time — so output improves with use rather than requiring ongoing manual template management.

Is patient data secure when using an AI scribe with NextGen?

Yes, provided you select a vendor with appropriate safeguards in place. Confirm that any AI scribe you evaluate is HIPAA-compliant, carries a Business Associate Agreement, and provides documentation of SOC 2 certification and encryption standards for data in transit and at rest. DeepScribe meets all of these requirements and maintains enterprise-grade data governance and PHI controls suitable for multi-specialty ambulatory deployments.