Skriber

Ambient capture and note generation are the entire product with no services business underneath. The human prompt engineering team is an onboarding function that configures the model for each customer, not a delivery workforce producing the output, which distinguishes this from the human scribe vendors graded B in this category.

Conventional draft and review with a real time element: the note builds during the encounter and is presented ready to sign for clinician review rather than filed automatically, and transfer into the record is a deliberate clinician action. Held at B because no acceptance rate, edit burden figure or confidence threshold is published, which matters more given the independent review noting incomplete diagnosis capture.

No accuracy figure, model card, named models or evaluation methodology located, and two claims that cannot be tested at all. Skriber describes itself as the number one AI medical scribe and states that its system delivers the most accurate billing codes of any AI scribe on the market. Neither is measurable, neither cites a comparison, and both sit alongside the other unfalsifiable superlatives this index tracks. A vendor competing specifically on coding accuracy should publish a coding accuracy result.

Nothing identifies any party in the chain: no model or model family, no foundation model provider, no hosting arrangement and no sub processor list was located in two passes. The distinctive feature here is a human access surface that is not review, and it is a variant of the human tier finding worth recording separately because a buyer would not think to ask about it. Onboarding is bespoke and staffed.

The vendor states that prompt engineers work with each new customer to configure note output, that every new physician works directly with an engineer to match their personal documentation style, and that difficult specialty templates escalate to a named clinician at the company who adjusts them. The stated objective is to hear the physician's voice in order to replicate their style of writing.

Replicating a physician's writing style requires examples of that writing, and tuning note output requires seeing notes, so the natural reading is that named individuals at the vendor see real clinical documentation during setup. There are clean alternatives that would achieve the same result, including synthetic samples or the physician's own prior notes with identifiers removed, and nothing published says which is used. Ask what material engineers see during onboarding, under what logging and consent, and for a sub processor list.

No study, controlled evaluation, accuracy benchmark or named institutional customer located. What does exist is genuine third party review presence across several independent software directories and app stores, which is worth more than a curated testimonial page because the vendor does not control it.

The feedback there is mixed rather than uniformly positive, including a reviewer rating the product eight out of ten with adjustments still needed for their practice, and at least one user reporting that spoken diagnoses were not fully captured. Mixed independent reviews are a more useful signal than a perfect one, and the negative note about incomplete capture is the specific thing to test in a trial.

Health privacy compliance is asserted and the product is described as ready for the European regulation, but no retention schedule for audio or transcripts, no de identification practice and no statement on whether customer content trains models was located.

The specific question flagged in the earlier assessment is now confirmed as a real feature rather than an inference, and it deserves developing because it is unusual in this category.

Onboarding is human and bespoke. The vendor states that a team of prompt engineers works with each new customer to configure note output, that every new doctor works directly with an engineer to match their personal documentation style, and that where an engineer cannot configure a specialty template the case escalates to a named clinician at the company who adjusts it himself. The vendor describes the objective as hearing the physician's voice in order to mimic and replicate their style of writing.

That is a considered approach to a real problem and it creates an access surface most peers do not have. Replicating a physician's writing style requires examples of that writing, and tuning note output requires seeing notes. So the question is direct: during onboarding, do named individuals at the vendor read real patient encounters, and if so under what controls, with what logging, and with what consent from the patients whose encounters they are.

There are clean answers available. Synthetic or de identified samples, or the physician's own prior notes with identifiers removed, would achieve the same result. Nothing published says which is used.

Ask what material engineers see during onboarding, the retention schedule, and the training position in contract language.

HIPAA compliance is stated consistently across the product and directory listings, with the account itself described as HIPAA compliant, and the product is additionally described as GDPR ready. Business associate agreement terms are not published for inspection, and signup is self serve so use can begin before any agreement conversation.

No named or dated attestation, no report of either type, no penetration testing statement and no trust centre were located.

The onboarding model gives this absence a specific shape rather than a generic one, and it is the same argument that applies to any vendor with people in the loop. The vendor's differentiator is that named engineers work directly with each new customer to configure note output, escalating to a clinician at the company where a specialty template proves difficult. Human involvement of that kind is precisely what an independent examination exists to describe: who at the vendor can reach customer content, under what authorisation, whether that access is logged, whether it is time bound to the onboarding period, and what happens to any material they worked with afterwards.

A peer in this lane runs certified coders over every note and faces the same question at larger scale. The point is not that human involvement is wrong, since it plainly improves output. It is that a vendor whose model depends on staff access carries a heavier disclosure burden than one whose pipeline is fully automated, not a lighter one, and neither has published a report here.

The European readiness claim adds a second reason. That regulation requires demonstrable technical and organisational measures, and readiness is a self assessment. A vendor claiming it while publishing nothing examined has claimed the harder standard and evidenced neither.

The company appears small and this grade records what a counterparty can verify rather than a judgement that controls are absent.

Ask what external testing exists, and specifically for the access controls and logging governing engineer access to customer content during onboarding.

No clearance claimed and none required for ambient documentation. No device pathway attaches to a note the clinician reviews and signs, and the vendor is explicit that the physician is the one who signs the note rather than the company.

The product is one of the narrower records in this category. Ambient capture, dictation and templates produce a note; billing code generation is present but framed around reimbursement accuracy and reduced claim rework rather than revenue lift. No risk adjustment scoring, no decision support layer, no patient facing component and no order generation were located. The scope expansion pattern documented across most of this lane is largely absent.

One feature of the design belongs on this axis rather than elsewhere, because it bears on what the signature means. The vendor's stated objective is to mimic and replicate each physician's own writing style, tuned by engineers during onboarding, so that output reads as that clinician wrote it. The rationale given is sound: the doctor signs the note, so it should sound like the doctor.

It also removes a signal. Where generated text is stylistically indistinguishable from a clinician's own writing, nothing in the finished record indicates which passages were drafted by a system and which were composed by the person signing. That is not a regulatory problem today, and it matters for anyone later reviewing that note in a complaint, an audit or a claim, and for institutions that expect to identify AI assisted documentation in their own records.

Ask whether generated passages are distinguishable at review, and whether any provenance marker persists after signing.

No fairness statement, subgroup analysis or accent and dialect performance disclosure was located, and no language coverage is claimed, which is at least internally consistent since the product does not market multilingual capability. Billing code generation is framed as reimbursement accuracy and reduced claim rework rather than revenue lift, placing it at the mild end of the coding gradient this index tracks.

The second pass surfaces two things about how this vendor makes quantified claims, and they bear on how a reader should weigh every unevidenced statement on the site rather than on bias specifically.

The headline time saving figure differs between the vendor's own pages. Several state one range and another states a figure roughly two and a half times higher for the same product and the same claim. A number that varies that much across a vendor's own material is not a measurement; it is a choice. That does not make the product worse, and it does mean an unevidenced figure from this source should not be carried into a business case.

The accuracy language goes further. The site describes the product as transforming raw patient data into perfectly accurate notes. No speech recognition and generation system can support perfect accuracy, third party reviews report users citing inaccurate summaries, and the claim is the kind another vendor in this lane explicitly repudiates on a published page arguing that overclaim erodes trust.

That contrast is the useful one. A vendor that will assert perfect accuracy without evidence is a vendor whose silence on subgroup performance cannot be read as considered restraint.

Ask for accuracy by accent and dialect, the evaluation set composition, and the basis for any quantified claim.

Two passes located no accuracy or error figure, no published limitations and no warranty, indemnity or remediation commitment, and the two claims that do appear are constructed so that neither can be tested. The product is described as the number one artificial intelligence medical scribe, and separately as delivering the most accurate billing codes of any scribe on the market. The second is the weaker of the two despite sounding stronger, and the reason is worth stating as a general test.

A comparative superlative asserts a result against every competitor without publishing a single measurement of any of them, so it claims more than a bare accuracy figure would while supporting less. A vendor competing specifically on coding accuracy is the one vendor that should publish a coding accuracy result, and coding is where an error carries consequences beyond documentation, because a wrong code shapes what is billed and creates false claims exposure for the practice submitting it.

Nothing published characterises where the coding output degrades, what a clinician should check before accepting a suggested code, or what the vendor commits to when one is wrong. Ask for a coding accuracy result with a defined denominator and reference standard, and for the comparison that supports the market leading claim.

Named systems, transfer level mechanism. Skriber lists DrChrono, Epic, Practice Fusion and eClinicalWorks among supported records and says it works with most EHR systems, but the described mechanism is that notes can be easily transferred into the record rather than written back into structured fields.

Naming systems while describing transfer means a buyer should establish exactly what their own integration does, because a named logo and a bidirectional connection are not the same thing and this index has seen both presented identically.

No hosting region, residency option or subprocessor detail was located, and nothing establishes which model service processes the encounter or what it retains. Delivery is cloud with full functionality claimed across desktop, tablet and mobile.

Residency is not an idle question on this record. The vendor claims readiness for the European data protection regulation alongside the United States rule, and that regulation imposes conditions on where personal data is processed and on transfers outside the region. A vendor asserting readiness for it while publishing no hosting region has claimed compliance with a framework whose first practical question it does not answer. Establish whether a European deployment is processed in region, and whether that differs from the default.

One integration detail from the second pass is worth crediting for its honesty. The vendor names integrations with three major record systems and states plainly that the depth of integration depends on access permissions at each hospital or clinic. That is true of every vendor in this category and almost none of them says it. A buyer should read it as intended: what a demonstration shows may not be what their own institution's permissions allow, and integration depth is a question for their own environment rather than a product specification.

It also leaves the mechanism unstated. Ask whether the connection is a certified interface, a marketplace application or an extension, since that determines whose identity its writes carry and how they appear in the audit log.

Ask for the hosting region per jurisdiction, the subprocessor list, the model provider, and the integration mechanism per record system.

A free trial is offered and promoted prominently, and a rate of roughly 149 US dollars per month circulates through third party review sites, but that figure was not confirmed on the vendor's own pricing page in this pass and one software directory offers to construct a personalised pricing breakdown, which suggests pricing is not straightforwardly published. Graded C rather than higher because the number a buyer would rely on comes from competitors and directories rather than the vendor.

Note formats are properly enumerated, covering SOAP, history and physical, discharge, intake, DAP and custom templates, which is more than several competitors specify and spans inpatient as well as outpatient structures. Documented users include urgent care and residents in training. Against that, no specialty count, no specialty tuning claim and no language coverage was located, so breadth cannot be compared with vendors that publish it. Specialty fit here is achieved through the prompt engineering onboarding rather than through pre built specialty models.