Heidi Health vs Marvix AI (2026 Comparison): Features, Pricing, EHR Integration & Clinical Depth

For any AI documentation tool to be truly useful in clinical practice, it needs to integrate and work seamlessly with existing Electronic Health Records (EHR) systems used by the practices.

Heidi Health - Basic Visit-Level Sync

Heidi supports integration with most EHR systems at a visit-by-visit basis, with support for pulling scheduled appointments and patient demographics from the EHR into Heidi and pushing documentation to the EHR after the consultation.

1. Appointment Sync: Heidi pulls appointment schedules and patient demographics from the EHR and displays them within its interface.
2. Documentation Push to EHR: Once the consult is complete, Heidi can push the generated documentation back into the EHR. There is section field mapping so each content block from the notes goes into the right content section within the EHR.
3. Scope of Integration: Heidi's EHR integration is limited to this intake-and-output flow. It does not support bidirectional exchange of clinical data. Prior notes, labs, imaging, medications, and historical records cannot be pulled into Heidi from the EHR to inform the current visit note.

Marvix AI - Deep 2-Way Integration

Marvix offers real-time, bi-directional EHR integration (with most popular EHRs like AthenaHealth, Advanced MD, eClinicalWorks and more) without disrupting clinic workflows. Moreover, it offers complete EHR integration in the free trial at no extra cost.

This is how Marvix integrates with your EHR:

1. Pulls demographic data and appointment schedule
2. Pulls prior patient information (prior notes, growth charts, immunizations, screening results, labs, imaging, medication histories, and intake forms, handwritten notes, scanned and faxed documents) in structured or unstructured format
3. Pushes fully editable completed notes back into the EHR
4. Maintains section mapping in the EHR in the provider's preferred format
5. Complete EHR integration in 2-4 business days

Takeaway: Heidi vs Marvix on EHR Integration

Both products integrate with the EHR, but at different depths. Heidi supports appointment sync and documentation push-back. Marvix supports bidirectional data exchange and full note mapping with most EHRs. One of the differences between both is that with Marvix, EHR setup is typically completed in 2–4 days, with support from the Marvix team. On the other hand, with Heidi, the setup and EHR integration is handled by the user through a self-serve configuration and referring to support articles.

The way a clinical note is structured determines how useful it is beyond the current visit. For specialty practices managing patients over time, note architecture matters.

Heidi Health — Traditional Consult-Centric Structure

Heidi uses a classic consultation format optimized for clarity and brevity.

Structurally, Heidi:

1. Consolidates information into familiar consult sections
2. Groups history, exam, assessment, and plan into a compact flow
3. Prioritizes readability and immediate documentation needs
4. Functions best as a snapshot of the initial visit

The structure is effective for first-visit documentation but does not expand into longitudinal scaffolding.

Marvix AI — Expanded, Specialty-Grade Structure

Marvix follows a modular, full-stack clinical note architecture designed for longitudinal care rather than a single visit.

Structurally, Marvix:

1. Separates clinical data, interpretation, and execution into distinct sections
2. Uses discrete modules for history, diagnostics, assessment, orders, functional status, and guidelines
3. Supports ongoing updates across staging, treatment, and surveillance phases

The structure is built to scale as the patient moves through diagnosis, treatment, and follow-up.

Takeaway: Heidi vs Marvix on Clinical Note Structure

Heidi uses a traditional consultation structure that captures the encounter and generates a complete note for that appointment. History, exam, assessment, and plan are consolidated into a compact, readable format.

Marvix uses a modular architecture structured for longitudinal care. It separates clinical data, diagnostics, assessment, orders, and guidelines into distinct sections that track patients across multiple visits and integrate historical context.

If your practice focuses on individual consultations with efficient first-visit documentation, Heidi's structure supports that workflow. If you're managing patients over weeks or months with evolving clinical data, Marvix's expanded framework is built to scale with that complexity.

Templates shape how documentation fits into your workflow. Static templates work the same for everyone, while customizable templates adapt to individual physician styles and specialty needs.

Heidi Health - Static Templates

Heidi uses static templates that remain the same across all visit types. It does not have templates for new, follow-up, or specialized encounters.

The Pro version includes an 'Oncology Initial Consultation' template which we have used for generating a note. The free version offers a set of base templates, such as:

1. H&P
2. H&P (Including Issues)
3. Issues List
4. Specialty Note
5. SOAP
6. SOAP (Including Issues)

Heidi also provides a public library of user-created templates for making notes and other kinds of documents (AVS, Referral Note etc.) which are static, prebuilt note formats contributed by clinicians across different specialties. These templates can be browsed, copied, and manually adapted, but they are not customised based on visit type, specialty context, or patient data.

A user can also ask Heidi AI to create a document by prompting instruction to the AI.

Also, Heidi does not provide note customisation within its clinical notes.

Marvix AI - Custom Specialty-Specific Templates

Marvix has specialty- and disease-specific templates that can be chosen depending on the type of visit and the patient's condition. It also has custom templates for first visits, follow-ups, annual wellness visits, and sick visits, to capture the most relevant details for each context. Marvix builds templates for each provider in the practice that match their individual documentation style.

One of Marvix's standout features is its ability to tailor notes to an individual physician's style. Using neural style transfer, Marvix learns from your previous notes to match your tone, language, and preferred format.

Beyond writing style, Marvix also customizes section formatting. For example, physicians can choose how A/P sections appear: bullet-point lists, narrative style, or problem-wise breakdowns with corresponding action plans.

Takeaway: Heidi vs Marvix on Custom Templates

Heidi relies on fixed, static templates that can be selected or copied but do not adapt to visit context, patient data, or individual physician style.

Marvix treats templates as a living part of the workflow. They can be adapted to visit type, condition, and specialty, and modified to how each physician writes by learning from prior notes.

The accuracy of an AI scribe is table stakes. What separates systems is the depth of clinical reasoning and context they preserve in the documentation.

How We Generated Notes Using Marvix and Heidi

While using Marvix, we selected the 'Oncology' template to generate a note. In Heidi, we selected the 'Oncology Initial Consultation Note' (Pro version) to create a note. Heidi offers different voice styles and we have used Goldilocks (recommended by Heidi) as it strikes a balance between brevity and thoroughness.

Comparing Marvix's note and Heidi's Oncology Initial Consultation Note:

1. Clinical Documentation Approach

This section examines how comprehensively each system captured the consultation and whether the note reflects clinical reasoning or functions primarily as documentation of what was discussed.

2. Patient History & Diagnostic Workup

This section compares how each system documented the patient's symptom timeline, imaging findings, and the diagnostic strategy for staging the disease.

3. Treatment Plan & Patient Preparation

This section examines how each system documented the proposed treatment approach, supportive care measures, and baseline functional status.

Takeaway: Heidi vs Marvix on Documentation Accuracy and Depth

Heidi produced an accurate, structured note that documents what was discussed during the visit. It captures the key clinical information in a clear format. However, it does not include oncologic depth or explicit clinical reasoning connecting findings to decisions.

Marvix documented the clinical reasoning behind decisions and provided oncology-specific detail. It captured symptom evolution over time, imaging interpretation in the context of staging, treatment sequencing rationale, performance status with clinical justification, and specific prehabilitation targets. This framework provides additional context and depth for tumor boards, multidisciplinary review, or complex referrals.

To evaluate how each AI scribe summarizes longitudinal patient history, we gave both systems the same prior notes, specialist reports, intake forms, labs, imaging, and procedure documents. We then assessed how clearly each tool summarized this information for the current visit.

Marvix AI - Patient Recap Summary

Before generating a new visit note, Marvix analyzes each historical document individually and creates a structured summary known as the Patient Recap. It extracts clinically relevant details such as diagnoses, symptom progression, prior assessments, lab trends, imaging findings, and procedure outcomes, in a concise, organized format. This enables clinicians to quickly grasp the complete longitudinal context before the visit begins.

Heidi Health - No Summary

Heidi takes a different approach. While you can manually upload historical documents, it does not produce summaries that clinicians can review prior to note generation.

Takeaway: Heidi vs Marvix on Summarizing Longitudinal Patient History

Heidi allows clinicians to upload historical documents, but it does not generate a summarized view of the patient's longitudinal history before note creation.

Marvix analyzes each historical record and produces a structured Patient Recap that highlights diagnoses, symptom progression, prior assessments, labs, imaging, and procedures chronologically. This gives clinicians a clear summary of the patient's history before starting the visit note.

To evaluate how each AI scribe brings longitudinal patient history into the visit note, we gave both systems the same prior notes, specialist reports, intake forms, labs, imaging, and procedure documents.

After historical data has been ingested, we generated a new visit note in both scribes using the same present-day consult. This allows an evaluation of how each scribe carries prior clinical information into the current note.

Below, we compare the new notes generated by both AI scribes to examine: which sections are present, what historical details are carried forward, and how prior data appears in the current clinical narrative.

Difference in Composite Note Generated by Marvix and Heidi Health:

1. Note Structure & Organization

This section examines how each system structures the composite note and positions historical data within it.

2. Clinical Narrative & Documentation

3. Clinical Decision-Making & Forward Planning

This section examines how each system documents the treatment plan, functional status, and evidence-based reasoning.

Takeaway: Heidi vs Marvix on Composite Notes

Heidi produces a current visit-focused note that includes prior data within the narrative. Historical details are mentioned but not separated into distinct sections. The note is designed for immediate clinical decision-making.

Marvix constructs a longitudinal oncology record that carries prior imaging, labs, procedures, and assessments directly into the current note as separate, referenceable sections. The structure supports continuity across follow-up visits and multidisciplinary review.

Clinical documentation begins before the patient enters the room. It begins when the physician opens the chart and tries to reconstruct the patient's history from prior notes, reports, and fragmented records across the EHR.

Heidi Health - Manual Template Pre-Charting

Heidi supports pre-charting through EMR charting templates. These templates act as a checklist to organize demographics, medical history, medications, and provider notes, and require manual selection and completion. Heidi does not ingest and reconcile the full historical data into a unified view.

For post-visit dictation, Heidi offers 'Word-for-Word Dictation' and 'Smart Speech' using which information from the dictations are added into the appropriate places in the note. The key difference lies in how speech is handled.

Word-for-word dictation turns spoken words directly into text, exactly as said. Commands like "new paragraph" or "colon" are inserted just as spoken. Smart speech, on the other hand, interprets what you mean. It listens for intent and converts your narration into clear, professionally structured clinical language.

In testing we used both modes, however, dictated content in both modes was appended to the end of the note rather than placed into the relevant sections, requiring manual editing.

Marvix AI - Automated Data Pull from EHR

Pre-charting in Marvix starts ahead of the encounter. It pulls patient demographics, appointment data, schedules prior notes, historical assessments, labs, imaging, and referral documents directly from the EHR into a single, structured clinical context before the visit begins. When the chart is opened, the relevant history is already organized and ready.

Dictation in Marvix is not tied to a single moment in the workflow. Providers and their teams can dictate before, during, or after the encounter. Every dictation is stored with a timestamp and author attribution, creating a clear chronological record of clinical input.

When processed, the entire note updates, including assessment, plan, AVS, and billing codes, ensuring consistent, accurate documentation. Dictated information is placed in the appropriate sections and reflected across all generated documents. In Marvix, pre-charting and dictation work together as a unified AI documentation workflow, not as separate steps.

Takeaway: Heidi vs Marvix on Pre-Charting

Heidi supports dictation and templates, but relies on manual structure and editing. Marvix treats pre-charting and dictation as a single, end-to-end documentation workflow.

Clinical documentation often requires capturing information beyond what is verbalized during the consultation: normal exam findings, medication side effect profiles, or procedure details. Macros play a crucial part in ensuring this information is captured in the note in a seamless manner.

Heidi Health - Manual Snippet Inserts

Heidi takes a simpler approach with "Snippets." These are static, pre-saved blocks of text that can be inserted into notes by typing a shorthand (e.g., /normROS to insert a normal review of systems).

In our pancreatic cancer case, this might allow a physician to quickly drop in a generic ROS or physical exam, but Heidi won't automatically recognize that chemotherapy was prescribed or that surgery was planned. Any additional details would have to be inserted manually by recalling and typing the correct snippet shorthand.

Marvix AI - Context-Aware Clinical Macros

Marvix ensures that both the discussed and inferred elements of care make their way into the note. It supports two types of macros:

1. Verbal Macros: Triggered when the physician says a phrase like "Insert chemotherapy side effects." In the specific oncology case mentioned earlier, a phrase like this will instantly populate the note with detailed information about fatigue, nausea, low blood counts, and neuropathy related to gemcitabine/nab-paclitaxel on Marvix, saving time and ensuring consistency.
2. Inference-Based (Semantic) Macros: These are context-aware and don't require the physician to explicitly call them out. For example, when chemotherapy is prescribed, Marvix automatically adds the relevant side effect management plan. Similarly, if the Whipple procedure is discussed, Marvix populates the note with a structured explanation of the surgery, including resection details, expected hospital stay, and recovery milestones. These macros adapt to the encounter, ensuring that nothing essential slips through the cracks.

Takeaway: Heidi vs Marvix on Macros

Snippets on Heidi require manual insertion via shorthand commands while macros in Marvix trigger automatically based on the consult. Marvix eliminates the need for active recall during note generation, reducing documentation time.

Beyond clinical notes, practices need referral letters, discharge summaries, and other documents.

Marvix and Heidi both generate multiple kinds of documents such as clinical notes, medical leave letters and referral letters.

Below are the documents generated by Heidi:

1. Patient Summaries / Handover Notes
2. Discharge Summaries and Certificates
3. Nursing Notes
4. Task Lists and Follow-Up Actions

There is a library of pre-made templates that you can choose from to create any kind of document. You can also create your own templates from scratch and you can ask Heidi AI to create a document for you as well.

Below are the documents which are generated by Marvix AI besides the ones already mentioned:

1. After-Visit Summaries (AVS)
2. IME Reports
3. Pre-Operative Reports
4. Post-Operative Reports
5. Custom Documents (Marvix can be configured to produce any document in the clinician's preferred format, verbiage, layout, and style.)

Takeaway: Heidi vs Marvix on Document Generation

Both scribes offer a wide variety of documents. The difference lies in the fact that Heidi has pre-made templates for all users, while Marvix can customise every kind of document for each clinician in the practice.

If you are a specialist, you might already be working with a team to manage each patient encounter.

Heidi Health - Shared Access, Single Editor

Heidi allows teams to view sessions and create downstream documents based on a shared note, and templates can be shared across the team by admins. Team visibility, session access, and certain settings are centrally controlled by administrators.

However, in Heidi multiple users cannot edit the same note simultaneously. Changes are not reflected in real time across accounts, and edits within a note are not tagged with the user's name or timestamp. Team members cannot modify each other's primary notes directly.

Marvix AI - Real-Time Collaborative Editing

Marvix supports teamwork by letting multiple users add, edit and update information in the same note in real time. Every entry is instantly synced across accounts, with clear attribution showing who added or edited what and when. Names and timestamps are mentioned across all edits. All authorized users see the same note, ensuring shared visibility and parallel workflows.

Takeaway: Heidi vs Marvix on Multi-User Workflows

Heidi allows teams to view sessions and share templates through admin controls. However, it doesn't support real-time collaborative editing of the same note.

Marvix takes it a step further by enabling true multi-user collaboration. Multiple team members can work in the same note simultaneously, with every edit synced instantly and attributed with names and timestamps.

Medical coding determines billing accuracy and reimbursement. Both systems offer coding, but their approach, complexity and depth differ.

Heidi Health - Code Suggestions Only

Heidi surfaces relevant diagnosis and procedure codes based on what is explicitly documented in the note. It maps findings and planned investigations to suggest codes and presents them as a reference list. Heidi does not calculate E/M levels, apply MDM logic, or generate visit-level rationale or modifiers.

Marvix AI - Automated MDM-Based Coding Logic

Marvix generates ICD-10-CM and E/M codes directly from the clinical note using MDM guidelines. Each encounter is evaluated across procedure complexity, data reviewed, and risk of complications or comorbidities to determine the appropriate E/M level. The selected level is presented with clear MDM rationale to support coding review and selection.

Marvix automatically identifies and applies the appropriate modifiers and procedure codes as well based on the clinical context of the visit. All generated codes remain fully editable, allowing clinicians to review, adjust, or override them as needed before final submission.

Difference in Billing Codes Generated by Marvix and Heidi Health:

To compare billing codes, we asked Heidi AI to produce them for the composite note, while in Marvix we checked the 'E/M' and 'ICD-10' boxes. Here's how the outputs differed:

Heidi Health

Heidi produced a concise, diagnosis-focused coding output. For this case, the ICD-10 codes accurately reflect the core clinical picture, including pancreatic malignancy, biliary obstruction, weight loss, and pain. Planned procedures and staging investigations are identified and mapped to the appropriate procedural codes within the NHS framework. The output presents a clear summary of documented diagnoses and next steps.

The coding output remains list-based. Codes are presented without visit-level complexity assessment, documented risk evaluation, or an explanation of how clinical reasoning supports billing decisions. There is no MDM breakdown and no linkage between longitudinal care and code selection.

Marvix AI

Marvix generated ICD-10 and E/M codes and also surfaced the reasoning behind each coding decision. Visit level selection is derived from explicit MDM components: problem complexity, data reviewed, and risk of complications. Each component is supported by written rationale tied to the patient's condition, prior workup, and planned interventions.

Add-on codes such as G2211 are included with justification based on longitudinal management and anticipated treatment course. Additional diagnosis codes capture procedural history and relevant laboratory abnormalities, reflecting a broader view of the clinical context beyond the current visit diagnosis.

Takeaway: Heidi vs Marvix on Medical Coding

Heidi captures what happened and what is planned, producing a clean and usable code set. Marvix captures how complex the encounter is, why it qualifies for a given billing level, and how the visit fits into ongoing care. The difference is in depth: Heidi provides correct labels, while Marvix provides coding grounded in documented clinical reasoning.

How a system is set up determines how quickly it fits into your workflow — and both follow different approaches.

Heidi Health - Self-Serve with Resources

Heidi onboarding is largely self-serve. Documentation style is standardised rather than custom note styles. Templates follow standardized formats with limited manual customization options.

EHR configuration is completed by the user. Setup is supported through help articles and customer support team, but configuration and troubleshooting are managed independently.

There is no structured onboarding session to tailor workflows or documentation style. Support is available through live onboarding group calls which you can book in advance and through resources.

Marvix AI - Fully Managed by Team

Onboarding with Marvix is handled end to end by the Marvix team. The process starts with reviewing the physician's existing notes. Prior notes are used to train the AI so that when the physician uses Marvix, the generated documentation matches the doctor's current verbiage, structure, formatting, and style.

Custom templates are created during onboarding. This includes visit notes, AVS documents, referral letters, and any other documentation the physician requires. Every template is built in the physician's preferred format, style, and structure.

EHR configuration (at no additional fee) is completed by the Marvix team. Setup is typically completed within 2–4 days. Providers do not need to manage integrations or technical configuration.

An onboarding call is conducted with the physician and/or with the full clinical team. The session covers workflows, features, and day-to-day usage so teams know exactly how to use the system from day one.

Ongoing support is available 24×7 and assistance is provided whenever any issues arise or workflows need adjustment.

Takeaway: Heidi vs Marvix on Onboarding

Heidi onboarding is self-serve, standardized, and configured by the user. Marvix onboarding is hands-on, physician-specific, and fully managed by the Marvix team.

Both Marvix and Heidi are built to meet the security and compliance standards required for handling protected health information.

Each platform is HIPAA compliant, maintains SOC 2 Type II certification, encrypts data in transit and at rest, and undergoes regular security audits. Both state that patient data is not used for model training or secondary commercial purposes, and both provide strict access controls and secure infrastructure to safeguard clinical information.

Both Marvix and Heidi offer free trials with access to their full feature sets, allowing clinicians to evaluate the product in real workflows before committing.

Heidi offers a 14-day free trial while Marvix offers a 30-day free trial with full feature set access and integration with existing EHR.

To learn more about Marvix pricing, click here. To learn more about Heidi pricing, click here.

The main difference comes down to approach.

Heidi follows a self-serve model where you configure the system yourself using standardized templates. It focuses on documenting individual encounters.

Marvix is built on a customized service model. Templates are tailored to each physician's style, EHR integration is bidirectional and fully managed, and the system is designed for longitudinal care.

Heidi works well if you want manual control over how your notes are structured. Marvix works well if you're managing complex, multi-visit cases and want your documentation tool to handle the heavy lifting.

Marvix is designed for specialty care with modular note structures, automatic Patient Recaps from historical data, and multi-visit continuity. Heidi uses traditional consult formats optimized for single encounters.

Specialty practices managing patients across multiple visits with complex diagnostic workups, treatment sequencing, and longitudinal tracking typically require the structured historical integration and ongoing care framework that Marvix provides.

Both integrate with EHR systems at different levels.

Heidi pulls appointment schedules and demographics, then pushes completed notes back. It does not pull prior notes, labs, imaging, or medication histories.

Marvix provides bidirectional integration with systems like AthenaHealth, eClinicalWorks, and Advanced MD, pulling full historical data including prior notes, labs, imaging, and medications. Setup is completed by the Marvix team in 2–4 business days at no additional cost.

Heidi generates structured notes documenting what was discussed during the visit using traditional history, exam, assessment, and plan sections.

Marvix generates composite notes that pull prior imaging, labs, procedures, and assessments from historical visits into the current note, with separated sections for historical context, current findings, and forward planning.

Heidi notes focus on accurate encounter documentation. Marvix notes are structured for longitudinal care and multidisciplinary review.

When evaluating AI scribes, physicians often compare several workflow areas.

Compare EHR integration (visit-level vs bidirectional), note structure (consult format vs longitudinal architecture), template customization (standardized vs physician-tailored), historical data handling (narrative vs Patient Recap summaries), pre-charting (manual checklists vs automatic EHR pulls), macros (manual snippets vs context-aware triggers), collaboration (view-only vs real-time editing), coding (ICD-10 suggestions vs E/M with MDM rationale), onboarding (self-serve vs fully managed), and trial duration (14 vs 30 days with EHR integration).

The right choice often depends on the complexity of the practice's documentation needs and how the team manages clinical workflows.

Both reduce documentation time through different mechanisms.

Heidi generates notes from consultation audio with adjustable detail levels and manual snippet insertion.

Marvix uses automated workflows including context-aware macros, pre-charting that organizes historical data before visits, dictation that updates all documents simultaneously, and real-time collaborative editing.

Both tools aim to reduce time spent on charting, though they approach the workflow slightly differently.

Heidi uses EMR charting templates as manual checklists for organizing demographics, medical history, and medications. Users complete them manually without automatic historical data consolidation.

Marvix automatically pulls demographics, appointment schedules, prior notes, assessments, labs, imaging, and referral documents from the EHR into a structured view before the visit. Historical data is organized and ready when the chart is opened.

Both approaches help clinicians prepare for consultations, though the workflows differ.

Heidi allows teams to view sessions and share templates through admin controls. Multiple users cannot edit the same note simultaneously, and edits are not tagged with names or timestamps.

Marvix enables real-time collaborative editing where multiple users can edit the same note with instant syncing. All edits include user attribution and timestamps.

Both tools support team documentation, with different approaches to collaboration.

Heidi surfaces ICD-10 diagnosis and procedure codes based on documented findings as a reference list. It does not calculate E/M levels or apply MDM logic.

Marvix generates ICD-10 and E/M codes using MDM guidelines, evaluating problem complexity, data reviewed, and risk to determine E/M levels. It includes written rationale and modifiers like G2211 with justification.

All codes are editable in both systems.

Heidi suits practices with straightforward encounters, single-visit documentation needs, and a preference for self-configuration.

Marvix suits specialty practices managing longitudinal care, requiring custom templates, automated pre-charting, real-time collaboration, and managed onboarding.

Both are HIPAA compliant and SOC 2 Type II certified. Trial periods are 14 days for Heidi and 30 days with full EHR integration for Marvix.

The best choice depends on the documentation complexity and workflow needs of the practice.