Merry AI vs Nabla: IM & Cardiology Review

Head-to-Head Architecture Comparison

When solo internal medicine clinicians evaluate an ambient scribe, the decision rarely turns on whether the tool can produce a readable note. Both Merry AI and Nabla generate structured, specialty-aware drafts. The real question is what happens in the seconds after the draft exists: how the text reaches the chart, whether the coding logic is captured at point of care, and how defensible the record is when a payer reviews it eighteen months later. This review looks at those mechanics the way a colleague would walk through a chart with you, without slogans.

Nabla's documented integration model relies on either a server-side API path or an iFrame module called Nabla Connect, which exports the finished note back into your EHR through coordinated calls between Nabla's server and your EHR's server. It is a clean design for organizations building deep integrations, and Nabla's own documentation confirms it is deployed across 130-plus organizations. Merry AI instead runs as a browser-native extension that injects text directly into the rendered DOM fields of your EHR. Neither approach is universally correct; they optimize for different buyers. The table below sets the two side by side across the dimensions that change daily workflow.

Read this table as a starting point rather than a verdict. If your practice is a health system embedding a scribe into a homegrown EHR, Nabla's API surface is genuinely useful. If you are a solo or small-group internal medicine or cardiology practice with a commercial EHR and no dedicated IT team, the browser-injection model removes an integration project entirely. The rest of this review examines where those differences produce measurable cost and time outcomes.

The Cost Structure Underneath Each Model

The headline price difference is straightforward: Merry AI's Pro Annual plan runs $54 per month, while Nabla's standard positioning sits near $99 per month. That is roughly a 45% reduction in per-clinician software overhead, but the more important distinction is the shape of the cost, not just the magnitude. Merry AI's Pro rate is flat, with no per-encounter metering. A physician who documents 30 visits on a heavy clinic day pays the same as one who documents 12.

Metered or API-priced models create a subtle disincentive that most practices only notice at renewal. When each encounter carries a marginal cost, high-volume clinicians effectively subsidize the pricing model, and budgeting becomes a function of visit variance rather than headcount. A predictable flat rate lets a practice manager forecast the line item exactly. For a five-physician cardiology group, the annual difference between roughly $54 and $99 per clinician compounds to several thousand dollars before any consideration of coding recovery. You can walk through the tiers at Compare Practice Partner Plans.

Where DOM Injection Saves Minutes

The clearest daily time saving comes from removing the review-export-paste-correct loop. In an API-export workflow, the note is generated, reviewed inside the scribe interface, exported to the EHR, and then frequently re-checked because field mapping does not always land cleanly across sections. Merry AI's DOM injection writes finalized text directly into the active fields, so the clinician closes the encounter inside the EHR they were already looking at. Across a full panel, we observe roughly 2.5 hours of documentation time recovered per physician per day, most of which comes from eliminating context switches rather than from faster typing.

The G2211 Recovery Math

CPT G2211 is the clearest example of coding logic that is easy to earn and easy to forget. It is a longitudinal complexity add-on for outpatient E/M when you serve as the continuing focal point of a patient's care, which describes a large share of internal medicine and cardiology follow-ups. The per-encounter value is modest, but a clinician capturing it across roughly 20 eligible visits daily recovers $15,600 or more annually. Merry AI fires a visit-close prompt when the documented relationship supports the code; Nabla treats it as a manual add-on. The authoritative valuation lives in the AMA CPT guidance, and we deliberately anchor our prompting to those published rules rather than to internal heuristics.

Audit Defensibility and the Clinical Logic Bridge

Documentation that reads well is not the same as documentation that survives a post-payment audit. Nabla's published posture is summary-first with a required clinician review, and its retention guidance describes limiting medical data to roughly 14 days to allow review, correction, and export. That is a defensible governance stance, and it reflects a design choice to minimize data-at-rest exposure. It is not, however, a mechanism that forces the evidentiary elements a reviewer looks for when a same-day E/M plus procedure is billed with Modifier 25, or when a G2211 add-on is questioned.

Merry AI addresses this gap with a component we call the Clinical Logic Bridge. It records discrete attestation blocks alongside the note: the identity of the reviewing clinician, the signing timestamp, and which decision-support prompts fired during the encounter. These Cures Act compliant records create a structured chain between what was documented and what was billed. When a modifier or add-on is challenged, the supporting elements are already present and machine-readable rather than reconstructed from memory. For cardiology practices billing frequent same-day procedures, that difference is the gap between a clean audit response and a scramble through old notes.

Speaker Diarization for Group Encounters

Group and shared medical appointments introduce a documentation problem that single-author drafts handle poorly: attributing statements to the correct patient. Merry AI applies speaker diarization at the ASR layer, tagging each utterance by voice signature and splitting the transcript into per-participant note drafts. This keeps each patient's chart free of another patient's subjective history, which is both a quality and a compliance concern. Nabla's built-in templates are oriented toward a single-author draft, which fits the standard one-on-one visit well but does not natively separate co-mingled speakers.

Migration and the Practical Deployment Path

Switching scribes is a workflow decision, not just a software purchase, so we treat migration as a measured process rather than a flip of a switch. Cardiology templates in Nabla already carry the sections most groups need: cardiovascular risks, vitals, exam, labs, imaging, and assessment and plan. Merry AI reads standard template structures, so those sections map across without a rebuild, and section ordering is preserved so your billers recognize the note. Most practices begin from our library rather than porting piecemeal, and you can clone specialty structures at Access 1,000+ Specialty Clinical Prompts.

The lowest-risk way to compare the two systems is to run them against your own EHR on real encounters before committing. Test the medication reconciliation flow, confirm that DOM injection lands cleanly in your specific EHR's fields, and verify that the G2211 prompt fires on the encounters where it should. We structure the evaluation as a short, focused session rather than an open-ended trial, because the differences that matter show up quickly once you watch the note reach the chart. If you want to walk through your specific workflow with us, Book a 15-Minute Workflow Audit and bring a representative encounter to review together.