How to evaluate an AI-native EHR: a feature checklist

With the evolving landscape of AI in healthcare systems, we are seeing a sharp rise in AI EHRs. We have a separate post on what it means to be an AI-native EHR and how that differs from being AI-powered or AI-enabled.

An AI-native EHR is one where AI agents are first-class users of the system. They can take actions across the EHR using the same underlying infrastructure as humans, with appropriate access controls and a full audit trail.

Essential features every EHR must have

These eight come before any AI feature. An EHR needs them to run a practice.

1. Customizable forms: Intake, assessments, and consents you build and change yourself, on the day you need the change.
2. Dot phrases in charts: Smart phrases that expand inside the note, shared across the practice so everyone documents the same way.
3. Insurance billing: Eligibility, claim submission, clearinghouse, and denial work, all of it launched from the chart itself.
4. Payments posted: Remittances posted against the right invoice automatically, so patient balances stay current without a person keying them in.
5. E-prescribing: Controlled substances included, with PDMP checks and per-provider favorites.
6. Role based access control: Access control by role, location, record type, and individual field.
7. Async messaging: Internal messaging and patient communication attached to the patient’s chart.
8. Automations.

Essential AI features for EHRs

Four AI features carry most of the clinical and administrative load. Each one works on the record itself, so its output lands in the chart or on the claim.

AI Search

Ask a question about a patient in plain English and get an answer back in seconds. The search covers the chart, notes, labs, and documents, and every answer cites the record it came from, so you can jump straight to the source.

AI Data Analyst

Ask a question about your organization in plain English and the analyst queries your live data, then returns tables, charts, and forecasts. No SQL, and no waiting in an analyst queue. The components it produces drag into a dashboard builder, which is where custom dashboards come from.

AI Scribe

Most scribes convert speech to text. The scribe should also listen for what is missing.

Working inside the record means the scribe can read the whole chart while it drafts the note, so it can nudge the provider on what they are missing while the patient is still in the room: a medication worth discussing, an annual screening that is overdue, a follow-up question the last visit left open. The output is a signed-ready SOAP note with ICD-10 and CPT codes attached.

AI RCM

Speech to text on its own does little for revenue. The claims lifecycle is where that shows up.

AI RCM runs eligibility, scrubs the claim against payer rules, submits it, tracks status, and posts payments, then drafts appeals for a biller to review. Coding sits at the front of that work: the system suggests ICD and CPT codes from the note and helps providers and billers maximize the documentation that supports them, so gaps surface while the note is still open.

Other AI features worth checking

1. Visit templates and forms created with AI. Describe the visit type in plain English and the template is built in your EHR, with discrete fields you can report on later.
2. Automations created with AI. Describe the rule in plain English and the automation is created with its triggers and filters already set.
3. Custom dashboards. Charts built from your live data, covering the trends that are native to your own practice.

Avon covers every feature on both lists.