Best AI Receptionist for Healthcare in 2026

Most lists of the best AI receptionist for healthcare rank on the wrong things: which has the slickest demo, the biggest funding round, the longest feature list. None of that survives contact with a real phone. A healthcare AI receptionist earns its place on exactly two things, and both are invisible in a sales demo: does it write a real appointment into your EHR, against the right chart, while the caller is still on the line, and does it hold together on the twenty per cent of calls where the diary moved, the patient record is ambiguous, or the call turned clinical.

So this list starts with the criteria that actually decide it, then compares the options against them. Read the criteria first, because they are what separate an agent that resolves a call from one that merely answers it.

Before any product, fix the criteria. A healthcare AI receptionist should be judged on five things, and a demo that only shows a clean booking exercises none of them.

Real-time EHR write-back. Does the agent write a first-class appointment into your practice system, against the resolved patient, validated and reversible, while the caller is on the line? Or does it hand a summary to staff to key in? This is the single biggest divider.

A native voice pipeline. Is the voice stack built and owned, or wrapped over third-party services? An owned pipeline controls its own latency and outages; a wrapped one inherits everyone else's. Ask for the reply latency and the ninety-fifth percentile, not just an average.

Patient matching that refuses to guess. Does it resolve a returning patient by scoring candidates and escalating an ambiguous one, or does it match on a phone number and quietly create duplicates?

Clinical escalation with an SLA. When a call turns clinical, does it reach a person in seconds with the transcript and identity attached, on a boundary that is auditable, or does it try to handle what it should not?

Compliance you can read. Can you read what the vendor signs and where data lives on a page, a BAA, encryption, no training on patient data, the frameworks it covers, or only extract it during a sales cycle?

The options, compared

Platform	What it is	Best for
2care	Healthcare-native, write-back on 95+ systems	The hard calls, on any system
Assort Health	Specialty protocol platform	Large specialty groups
Sully.ai	Multi-agent healthcare suite	Several AI roles, one vendor
PolyAI	Contact-centre voice platform	Systems with a contact centre
Hyro	Enterprise conversational AI	Large, IT-led deployments
Aeva AI	Cliniko-specific receptionist	Single-site Cliniko clinics

The entries below describe each factually, and against the five criteria. Where a vendor does not publish a figure, that is noted rather than guessed.

1. 2care, the healthcare-native agent

2care is built around resolving the call into the EHR, and it publishes the engineering that does it. The voice pipeline is native, built in-house rather than wrapped over third-party services: streaming recognition returns a partial transcript in roughly 120 milliseconds, an intent layer resolves the reason, provider, urgency and identity while the caller speaks, and native text-to-speech replies inside a total budget of about 480 milliseconds, ninety-fifth percentile under 700. Turn-taking uses dual-signal endpointing that does not cut off a hesitant caller, and under load the pipeline fails over between redundant native model instances before a caller hears a gap.

On write-back, it books into 95 or more systems, Epic, athenahealth, eClinicalWorks, NextGen, Cliniko and the rest, plus any FHIR R4 endpoint, as a first-class appointment, validated and reversible, confirmed only after the record accepts it. Patient matching scores candidates and escalates an ambiguous one rather than guessing. Clinical calls route through an Escalation Engine, a person in about three seconds on an emergency, four on a clinical question, with the transcript attached. Compliance is on the page: a signed BAA, AES-256 at rest, TLS in transit, no training on patient data, GDPR and DPDP coverage with data in region. It scales to 1000 or more concurrent calls at 99.9 per cent uptime, and it is accessible to a single practice, not only a health system. On the five criteria, it is built to lead each one. See the platform and the integrations.

2. Assort Health

Assort Health is an enterprise platform built around specialty protocols, with a large protocol library and write-back it states across 20 or more EHR and PMS systems. It is aimed primarily at large specialty groups that run an enterprise procurement. Its voice pipeline internals are not publicly detailed. It is a serious option for a big specialty organisation; a single practice weighing it against a healthcare-native agent should check the same five criteria, particularly whether the mechanisms behind a hard call are published or behind a sales process.

3. Sully.ai

Sully.ai offers a suite of AI roles for healthcare, a receptionist among them, and positions on breadth, several agents sharing context, with a published list of native EHR integrations and a pilot measured in weeks. It fits a system that wants more than a receptionist from one vendor. As with any suite, the question for the receptionist specifically is the five criteria above, and whether the depth on the phone matches the breadth across roles.

4. PolyAI

PolyAI is a contact-centre voice platform applied to healthcare, strong on languages and on resolving a share of calls within a conversational layer. Its own material describes integrations in telephony and contact-centre terms rather than EHRs. That makes it a fit for a health system with an existing contact centre; a practice whose need is a booking landing in the EHR should confirm how, and whether, that write happens.

5. Hyro

Hyro comes from the IT and conversational-AI world and is aimed at large, IT-led enterprise deployments. It holds an Epic marketplace presence, which is a route to purchase rather than a statement about how a call is resolved. Its voice and booking internals are not publicly detailed. For a large enterprise IT function it belongs in the evaluation; for a practice, the five criteria are the way to compare it fairly.

6. Aeva AI

Aeva AI is a receptionist built specifically for Cliniko, publishing around fifty concurrent calls and up to five actions per call, with custom local-accent voices. It fits a single-site Cliniko clinic that wants a simple, closely-tuned tool. A Cliniko practice that cares about the failure paths, matching, escalation, concurrency, compliance, should compare it against a healthcare-native agent on those specifics rather than on the happy path, since both book a simple appointment into Cliniko.

The technical numbers, side by side

Deep comparison means numbers, not adjectives. Here is what each publishes on the metrics that decide a call. Where a vendor does not publish a comparable figure, it reads not publicly detailed rather than a guess, and the enterprise platforms mostly do not publish these at all.

Metric	2care	Others (published)
Reply latency	480 ms; p95 700; p99 1,100	Not published
Turn-taking	550 ms adaptive	Not published
Barge-in yield	200 ms	Not published
Concurrent calls	1,000 or more	Aeva 50; else not published
Uptime	99.9 per cent	Not published
EHR write-back	95+ systems, FHIR R4	Assort 20+ EHR/PMS
Languages	50+, multilingual agents	PolyAI 45+; Assort 37

Competitor figures are estimated from each vendor's public materials; 2care figures are our own.

The pattern in that table is the argument. On the metrics that decide how a call feels and whether it resolves, latency, turn-taking, concurrency, write-back, 2care publishes a number and most of the field does not, and where the field does publish, on languages and concurrency, 2care is ahead. A vendor that will not state its reply latency or its turn-taking behaviour is asking a practice to take the hardest part of the product on trust, and the hardest part is exactly where a phone agent succeeds or fails.

Where 2care is right for a healthcare practice

2care is right for the practice that reads a table like the one above and cares about every row: the latency that makes a call feel human, the turn-taking that does not cut off a hesitant patient, the concurrency that answers a Monday rush, the write-back that lands in the EHR, and the compliance it can read. It is right for a single practice or a multi-site group that wants that depth without an enterprise procurement, on whatever system it already runs. A very small clinic that only ever exercises a clean booking may not need all of it; every practice whose real calls test those edges does, and that is the buyer 2care is built for.

How to choose, and what to test

Pick by matching the five criteria to what your practice actually needs, not by the demo. A large health system with an enterprise procurement and an existing contact centre will weigh the enterprise platforms; a single-site Cliniko clinic wanting the simplest tool will weigh Aeva; a practice or group that wants depth on the hard calls, on the EHR it already runs, without an enterprise procurement, is exactly where 2care fits.

Whatever the shortlist, test on the same four calls: a booking that must land in your EHR against the right chart, a hesitant caller, an ambiguous patient record, and a call that turns clinical. Those are what a demo hides and what a real phone will find. Phone access is a genuine priority for practices, an MGMA poll of practice leaders named it among the top patient-access focuses for 2026, so the phone is worth getting right.

Frequently asked questions

What is the single most important thing to evaluate?

Real-time EHR write-back. An agent that resolves a conversation but hands the booking to a queue has moved the work, not finished it. The best healthcare AI receptionist writes a first-class appointment into your record, against the right chart, while the caller is still on the line.

Why does a native voice pipeline matter?

Because a wrapped pipeline inherits the latency and outages of every third-party service in the chain, while an owned one controls them. Ask for the reply latency and the ninety-fifth percentile; 2care replies in about 480 milliseconds, p95 under 700, because it owns every stage.

How do I compare compliance fairly?

Ask to read it, not hear it. A vendor should be able to show, on a page, that it signs a BAA, encrypts data at rest and in transit, does not train on patient data, and covers the frameworks you operate under. If that takes a sales cycle to extract, that is itself a finding.

Are enterprise platforms better for a large group?

They are built for enterprise procurement and contact-centre scale, so a large IT-led organisation should evaluate them. But scale is not exclusive to them; a healthcare-native agent that handles 1000 or more concurrent calls fits a busy multi-site group and is accessible to a single practice too.

How should I actually test the shortlist?

On the hard calls, not the demo: a booking that must land in your EHR, a hesitant caller who pauses, an ambiguous patient record, and a clinical call that must reach a person in seconds. The differences between these products live there, not on the happy path.

The test that settles it

Line up your shortlist and give each the same difficult call: a returning patient with an ambiguous record, booking after hours, whose call turns clinical halfway through. Watch which one writes the appointment into your EHR against the right chart, escalates the clinical part to a person in seconds with context, and never creates a duplicate. That is the best healthcare AI receptionist for your practice, whichever name is on it, and it is the exact call worth putting in front of every product on your shortlist.

Hear 2care handle exactly that call on your own system, live, when you book a demo.