Head-to-Head Architecture Comparison
When cardiology practice managers evaluate ambient documentation, the deciding factor is rarely note prose quality, which both systems handle competently. The real questions are how the note reaches the chart, how much integration your IT team must own, and whether the coding logic behind each claim survives a payer audit. Merry AI and Suki AI answer these questions from opposite architectural directions, and that difference shapes cost, deployment speed, and clinical fit for a heart-failure-heavy panel.
Suki AI is built as a platform: ambient sessions are created per encounter, audio streams to a vendor cloud through Web, Mobile, or Headless SDKs, notes are generated against a CARDIOLOGY specialty flag, and finished documentation is written back into Epic, Cerner, athenahealth, or MEDITECH Expanse via API. It is a capable model, and it carries the enterprise contracting, security review, and multi-stakeholder integration timeline that comes with any SDK-and-writeback platform.
Merry AI collapses that stack into the browser. A scoped Chrome extension detects the open cardiology encounter in your web-based EHR and injects the note directly into the native field the clinician is already looking at. There is no session-ID orchestration, no audio duplication into a separate cloud of record beyond transcription, and no per-encounter API metering. The table below reduces the comparison to the six dimensions cardiology managers ask about most.
| Dimension | Merry AI | Suki AI |
|---|---|---|
| Monthly cost (annual billing) | $54/mo Pro, flat per clinician | ~$99/mo standard plus enterprise minimums |
| DOM injection speed | Sub-second direct tab write into native note field | API queue writeback, review pane copy-paste |
| Multi-speaker group note-splitting | Speaker diarization with multi-party note-splitting | Single-author ambient draft |
| CPT G2211 complexity prompting | Real-time visit-close prompt with attestation | Manual add-on, coder-dependent |
| Attestation logs | Cures Act compliant timestamping of draft, edit, sign | Basic text export |
| Data retention | Streaming transcription, minimal PHI duplication | Encrypted vendor cloud recording storage |
| Contract floor | No high-floor enterprise minimum | Enterprise / health-system contracting posture |
Read the table as a description of two philosophies rather than a scoreboard. Suki AI optimizes for large health systems that already run heavy EHR-integration programs and can absorb the governance overhead. Merry AI optimizes for the independent five-to-fifty provider cardiology group that needs HIPAA-grade protection without a health-system procurement cycle. You can Compare Practice Partner Plans to see how the flat Pro rate maps to your headcount.
Cardiology Workflow Friction
A cardiology clinic day is structured around continuity: the same heart-failure and atrial-fibrillation patients return quarterly, and each visit references prior imaging, device interrogations, and titration decisions. The documentation tool either respects that rhythm or interrupts it, and the interruption cost is where the two products separate most visibly.
Suki AI asks the clinician to perform an explicit ritual each encounter: open the visit, start an ambient session bound to the encounter and patient IDs, let audio stream, end the session, wait for processing, review in a pane, then approve for writeback. Each step is defensible in isolation, but across a thirty-patient day the session-management overhead accumulates, and the copy-paste or review-pane handoff adds a small latency to every note.
Merry AI removes the session ritual entirely. Because the extension reads the active encounter from the EHR tab, documentation auto-associates to the open chart; the clinician speaks, and the structured note lands in the native field with sub-second injection. There is no separate surface to learn, which shortens training for cardiologists and advanced practice providers who already resist adding another window to a crowded desktop.
Longitudinal LVEF Trend Capture
The sharpest cardiology-specific gap is longitudinal trend intelligence. Suki AI generates strong structured notes and pulls vitals and labs into the draft, but its public documentation does not describe embedding a disease-specific longitudinal visualization, such as an LVEF trajectory, into the live chart view at the point of documentation. It produces the note; it does not surface the trend.
Merry AI treats longitudinal metrics as first-class artifacts. When a heart-failure follow-up opens, the LVEF trend across prior echocardiograms can render inline with the assessment, so the cardiologist documents titration decisions against the actual trajectory rather than paging through prior results. For guideline-directed medical therapy, where the ejection-fraction trend drives the plan, having that trajectory adjacent to the note reduces both cognitive load and the chance of an incomplete assessment.
Multi-Speaker Group and Shared Visits
Shared cardiology encounters and group education sessions break single-author ambient models. When a fellow, an attending, and a heart-failure nurse all contribute to a visit, a single-author draft flattens the exchange into one voice and loses the attribution that shared-visit billing sometimes requires.
Merry AI applies speaker diarization to split the transcript by participant and assemble a note that preserves who said and did what. This matters less for a routine solo follow-up and a great deal for structured heart-failure clinics and device clinics where multiple clinicians document against one encounter, and it is a capability the single-author ambient draft does not natively address.
Coding Accuracy and Revenue Integrity
Documentation tools earn or lose their keep on coding integrity, and cardiology is unusually exposed here because its panels are dominated by exactly the longitudinal, complex conditions that CPT add-on code G2211 was designed to capture. The code is reportable alongside standard office E/M visits (CPT 99202–99215) when the clinician is the continuing focal point for a serious or complex condition, per CMS guidance, and chronic heart failure is a canonical example.
The problem is behavioral, not conceptual: G2211 depends on documentation language that manual and coder-dependent workflows routinely omit, so the revenue quietly leaks. Suki AI positions this capture as a manual add-on, which leaves it dependent on downstream coder vigilance. Merry AI fires a real-time visit-close prompt when the encounter matches longitudinal-complexity criteria and writes the supporting attestation directly into the note before signature. As always, confirm the current definition against AMA CPT guidance before adjusting your coding policy.
Audit-Defensible Attestation Logic
A code on a claim is not the same as a defensible claim. Merry AI's Clinical Logic Bridge inserts an attestation block explaining why the encounter met the longitudinal-complexity standard, tying G2211 to the documented condition and the clinician's continuing-focal-point role, all under Cures Act compliant timestamping that records the draft, edit, and sign chain. Suki AI's basic text export preserves the note but not the structured reasoning, leaving the rationale to be reconstructed if a payer asks. Your specialty prompt library, including the G2211 language, lives in the Template Center of 1,000+ Specialty Clinical Prompts.
Deployment, Compliance, and Cost
From a practice manager's chair, deployment risk is the quiet line item that dwarfs the subscription. Suki AI's model requires coordinating EHR-vendor technical teams, provisioning API credentials, deploying SDKs, configuring exam-room audio capture, and running change management on a new ambient surface. That is a multi-month program with several stakeholders, appropriate for health systems, heavier for an independent group.
Merry AI's browser-native model narrows the surface area to the extension itself. There is no vendor build to schedule, no SDK to maintain, and no separate cloud of record to duplicate PHI into beyond streaming transcription. On compliance, both platforms operate under HIPAA-grade encryption and BAA coverage; the honest differentiation is not security rigor but contract structure and data-duplication footprint. A narrower PHI footprint means your security assessment scopes to browser and injection behavior rather than a full ambient cloud audit.
On cost, the flat Pro rate of $54 per month per clinician on annual billing removes both the higher standard subscription and, more importantly, the enterprise contract floor and per-encounter metering that shape platform deployments. For a fifty-provider cardiology group, predictable flat pricing plus roughly $15,600 in annual G2211 recovery per clinician changes the entire economic conversation from cost center to margin contributor. The most efficient way to test these claims against your own panel is to Book a 15-Minute Workflow Audit and review the numbers the way a colleague would review a chart with you.
