Most AI scribes begin with the words spoken in the room. Praxis begins with the physician who practices medicine. Its Concept Processor learns from your work, and Reflective Ambient Intelligence™ brings that knowledge to the patient conversation.
Praxis begins with AI inside the clinical record. Its Concept Processor learns from your work; Reflective Ambient Intelligence™ brings that knowledge to the conversation. The connected tools then support patients, staff, follow-up and reporting. Explore the full suite below.
Praxis is a template-free electronic health record built around Concept Processing, which learns from each physician’s prior encounters. Its Reflective Ambient Intelligence™ (RAI) combines ambient encounter input with that physician-specific knowledge for clinician review. Connected features support intake, scheduling, patient communication, follow-up and analytics; the physician remains responsible for clinical decisions and the final record.
These are the core of Praxis’s template-free approach.
Praxis recalls your relevant clinical concepts, language and associated actions from prior encounters. Revise the case in your own words; your changes inform future similar visits. The system becomes more personal as you use it.
Praxis native ambient AI meets the clinician-specific Concept Processor. RAI is designed to reflect the physician’s clinical rationale, terminology and intent in a note the physician reviews and approves.
Reduce repeated questions and bring relevant information into the encounter earlier.
Condition-relevant forms can adapt to patient responses and send information back to the clinical workflow, ready for clinician review.
Patients can book online while the practice sets rules for visit types, provider availability and appointment slots. Intelligent alternatives support rescheduling.
AI-driven patient engagement keeps personalized instructions, tailored forms and secure communication connected to the patient’s record and the physician’s plan.
Turn the physician’s decision into coordinated, repeatable action.
Share physician-created clinical concepts from Praxis’s AI-based learning workflow as a starting point, then adapt it to your own medical judgment and documentation style—not a rigid, one-size-fits-all template.
Set reminders and messages for staff, patients or providers. An Agent can activate later or when a defined condition is met, and alert the team when action is still needed.
Embed discrete data within physician-authored text and query it for quality reporting, population health and research without forcing the clinical narrative into a grid of checkboxes.
Praxis’s value comes from how these capabilities work together. Ask to see one encounter carried through intake, physician-reviewed documentation, orders, follow-up and reporting.
An EHR can advertise AI because it has an ambient scribe, a chatbot, or a separate automation tool. Those tools may be genuinely useful. An added scribe can create a useful draft, but its integration with the underlying chart, orders, follow-up, and reporting varies by vendor. The physician still needs to verify clinical accuracy.
Praxis starts in a different place: its Concept Processor learns from the individual physician’s completed encounters and related work. RAI brings ambient input into that existing, clinician-specific workflow. Intake, Agents, portal communication, and Datum+ then connect the clinical plan to what happens before and after the visit.
In brief: Native AI in Praxis means the physician-specific learning engine is part of how the EHR documents and carries out care. An add-on AI scribe can speed up a note, but it does not by itself make the underlying EHR learn how you practice.
| Question to ask | Typical template-based EHR with a separate AI scribe | Praxis native AI workflow |
|---|---|---|
| What learns from the physician? | The scribe may learn a writing preference; the underlying chart may still follow predefined templates and fields. | Concept Processing adapts clinician-authored concepts and related actions across encounters. |
| What starts the note? | A form, a blank note, or a transcript-based draft, depending on the product. | The physician’s relevant prior clinical work and, with RAI, ambient context for review. |
| What happens after documentation? | Orders, messages, follow-up, and reporting may require separate steps. | Related actions, Agents, patient communication, and structured data can remain connected to the encounter. |
| Whose clinical style is preserved? | Depends on the scribe’s personalization and the EHR’s documentation constraints. | The clinician’s own language and decisions are the foundation of the knowledge base. |
EHR products differ substantially. This compares approaches, not every vendor’s implementation. The best test is to run the same encounter from intake through follow-up in each system.
Physicians do more than produce a note. They recognize a pattern, decide what is different, explain why a plan fits, order care, and arrange what happens next. The closer AI is to that complete process, the more useful it can become across repeated encounters. Its value should be judged by the work it removes while preserving clinical accuracy and physician control.
Templates helped digitize and standardize paper forms. They can still be useful for a defined task. But when rigid templates become the default way to describe every patient, a physician may spend the visit filling fields, navigating preset choices, and correcting language that does not reflect the case.
Adding an ambient scribe to that workflow may improve the first draft while leaving the underlying friction intact. The clinician can end up reviewing generated prose and reconciling it with required fields, templated text, orders, and separate follow-up steps. In some implementations, more text can mean more to verify rather than less work. That is the architectural question—not whether a vendor has added a microphone.
The AI drafts from the conversation. The EHR’s forms and task sequence may still be predetermined. The physician must determine what belongs in the note, what was omitted, and how the plan becomes orders and follow-up.
Concept Processing recalls how this physician has handled a related case. RAI is designed to bring ambient input into that personalized context. The physician adapts and approves the encounter, while connected tools can carry the plan forward.
Research on scribed notes has identified note bloat and a substantial role for template design; studies of ambient AI have also found benefits that vary among users. Neither finding proves that every template-based EHR becomes worse with a scribe. Scribed-note study · Ambient-scribe study
A template starts with someone else’s idea of the visit. Concept Processing starts with yours. Praxis learns the concepts, language, and related actions you use in practice, then offers the closest relevant prior work when you see a new patient. You can change it; the system incorporates those changes for future encounters.
Bring forward the clinician’s relevant prior approach instead of rebuilding the note from blank fields.
Revise what is different today. The note remains specific to this patient and subject to your review.
Praxis remembers the revisions and related actions, so future similar encounters can require less repetitive work.
Many ambient scribes turn speech into a note draft. Praxis RAI is designed to bring ambient listening together with the physician’s Concept Processor knowledge base, so the documentation is informed by how that clinician has documented, assessed, and carried out care before.
What matters clinically is often the reasoning behind the words. RAI aims to preserve that rationale, the physician’s voice, and the link between assessment and plan. The physician reviews and approves the final record. No AI draft should be assumed accurate without review.
Ambient capture helps reduce manual entry during the patient conversation.
Concept Processing supplies physician-specific prior knowledge and relevant patterns for the current visit.
The clinician checks the record, corrects it where needed, and decides what becomes part of the chart.
Self-scheduling and tailored intake help collect relevant information in advance.
Concept Processing and RAI support personalized, physician-reviewed documentation.
Orders, instructions, portal communication, and Agents support next steps.
Knowledge Exchanger, Datum+, and DataMiner support shared expertise and reporting.
Imagine a patient returning for a chronic condition with a new concern. This illustrative workflow shows what to ask Praxis to demonstrate; availability and automation depend on the practice’s configuration and the clinician’s decisions.
Condition-specific intake gathers relevant patient responses before the visit.
Concept Processing recalls the physician’s related work; RAI brings the conversation into that context for review.
The physician adjusts the note and plan. Related orders, instructions and Agents can carry chosen next steps forward.
Follow-up messages and Datum+ data support continuity, queries and reporting.
Use the same realistic encounter with each vendor. Ask them to show the whole workflow live, including what the AI gets wrong and how you correct it.
Show what changes on the second and third similar encounter, not just the first note.
Show whether it acts inside the clinical record or hands off a draft to another workflow.
Revise the assessment and show how the note, associated actions and future suggestions respond.
Show review, correction and approval steps for ambient content and any suggested action.
Show orders, patient instructions, reminders, portal communication and quality data.
Compare total documentation and staff time, correction work, note quality and follow-up reliability in your own practice.
“AI” covers several distinct approaches. Boolean logic means true-or-false conditions such as AND, OR, and NOT. It can be useful for defined rules, but a fixed rule does not learn from a physician. Praxis describes Concept Processing as a proprietary concept-learning system; its public explanations do not identify Boolean logic as its core mechanism.
| Approach | What it does | Where it fits in care |
|---|---|---|
| Boolean logic and rules | Combines true-or-false conditions such as AND, OR, and NOT to follow explicit rules. The rules do not learn by themselves. | Scheduling constraints, eligibility checks, and defined clinical reminders. |
| Machine learning | Finds patterns from examples. Performance depends on the data, task, and validation. | Prediction and classification; it should not be treated as clinical judgment. |
| Generative and ambient AI | Processes speech or text and may produce a fluent note draft. | Reducing transcription work, with clinician review for omissions or unsupported statements. |
| Praxis Concept Processing | Organizes and reuses the individual clinician’s concepts and related work, adapting as the clinician edits. Praxis describes this as its own learning architecture; do not equate it with every generic machine-learning method. | Personalized documentation and repeatable physician-directed workflow. |
| Praxis RAI | Combines ambient input with the physician-specific Concept Processing context. | Documentation designed to reflect the clinician’s established language and reasoning, with physician approval. |
An appointment rule can apply a practice’s explicit constraints consistently. A machine-learning model can estimate a pattern from examples. An ambient language model can turn speech into a readable draft. These are different capabilities, and none should be mistaken for a clinician’s diagnosis or medical judgment.
Praxis calls Concept Processing an AI-based, clinician-specific learning engine. Its public technical papers describe concept reuse and adaptation from the physician’s own work; they should not be reduced to “Boolean logic,” nor assumed to use the same approach as every modern generative model. RAI adds ambient understanding to that established physician context. The practical value is the connection among the clinical thought, the chart and the tasks that follow.
Ask vendors to identify which functions follow fixed rules, which learn from your past work, which generate new text, and how the physician corrects and approves each result. This distinction is more useful than a single “AI-powered” label.
How Praxis “mirrors the human brain”: the phrase describes its design philosophy. Clinicians recognize a familiar pattern, recall a related case, notice what is different, and adapt. Concept Processing is built around that cycle. It is an analogy, not a claim that software possesses human understanding or independently practices medicine.
The digital record of care created and used within a clinician’s practice or healthcare organization. It supports documenting and managing the patient’s care there.
A broader view designed to support information sharing and continuity across organizations and care settings, in addition to the work within one practice.
The terms are often used interchangeably. The federal health IT office distinguishes them by the broader reach of the EHR. Read its EMR-versus-EHR explanation →
Why Praxis prefers “EMR”: Praxis is a fully certified EHR under the ONC Health IT certification program, with patient access, Direct messaging, laboratory connections and information exchange. Yet we prefer EMR because the physician’s clinical record—and the physician’s own reasoning—should be the design center. Physicians deserve an EMR built to serve their clinical work—not one that makes their judgment and time serve the priorities of payers, administrators or other stakeholders. The broader EHR capabilities remain part of Praxis. View current Praxis certification details →
Praxis technical papers explain its own design. Independent studies examine ambient scribe benefits and documentation risks in other products. They provide context for evaluating clinical AI; they are not independent validation of Praxis RAI or a head-to-head comparison.
The federal health IT office explains the distinction between a record within a practice and a broader record designed for sharing across care settings.
A study of human-scribed notes found that template choices and individual workflows can affect note length and content. It does not evaluate Praxis RAI.
Praxis’s detailed comparison of clinician-specific Concept Processing, templates, expert systems, speech recognition, and dictation.
Dr. Richard Low’s explanation of the clinical reasoning and documentation philosophy behind the Concept Processor.
How Praxis proposes to capture structured clinical information while allowing clinicians to document in their own words.
A prospective pilot found reductions in documentation and EHR time, with substantial differences among individual users.
A 2025 comparison found strengths in organization and thoroughness but also documented hallucinations, illustrating why drafts require review.
A voluntary framework for evaluating trustworthiness and managing risks throughout an AI system’s use.
Praxis papers state Praxis’s design and viewpoint. External research cited here studies ambient AI generally or other products; none establishes a quantified outcome or zero-error guarantee for Praxis RAI.
Its Concept Processor learns from each clinician’s documentation and related actions inside the clinical workflow. RAI connects that foundation to ambient input; Agents, intake, scheduling, portal communication, and Datum+ extend intelligence and automation across the practice. These features do not all use the same AI technique.
No. Both use ambient input, but RAI is designed to connect the encounter with the clinician’s own Concept Processor knowledge base. The physician reviews and approves the final chart.
An add-on scribe may draft notes efficiently, yet the EHR underneath may still depend on predefined forms and separate actions. In Praxis, clinician-specific Concept Processing is part of the documentation and associated workflow itself. Compare the entire encounter, including orders and follow-up, to see the practical difference.
It can add reconciliation work if generated prose must be checked against rigid fields, copied text, and separate orders or follow-up tasks. It can also save time when integration is well designed. Judge the complete workflow and note quality in a live demonstration rather than assuming either result.
An EMR is the clinical record used within a practice; an EHR also supports information exchange across care settings. Praxis is a fully certified EHR under the ONC Health IT certification program, but we call it Praxis EMR to emphasize a record built around the physician’s work and clinical judgment. See Praxis certification details.
Praxis uses a template-free clinical documentation approach. It can reuse clinician-authored concepts and adapt them to a new encounter without making a rigid form the starting point.
No. Boolean logic executes explicit true-or-false conditions. Machine learning estimates patterns from examples. Concept Processing is Praxis’s proprietary clinician-specific learning architecture, and Praxis’s published descriptions do not say its core operates through Boolean logic. Different features can use different techniques.
Praxis’s published descriptions do not identify Boolean logic as the core mechanism of Concept Processing. Boolean conditions can support defined rules in software, but Concept Processing is described as learning and reusing a physician’s own clinical concepts and work.
No. Clinical decisions and final documentation remain the physician’s responsibility. Praxis is designed to support that work and reduce repeated administrative steps.
Use the same patient scenario in both demonstrations. Compare whether the system adapts to your words and decisions, what happens after the note, and how the clinician checks the resulting record.
Bring a real encounter and your toughest workflow questions. See Concept Processing, Reflective Ambient Intelligence, and the connected Praxis tools in a live demonstration.
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