Why Regional Medical Groups Are Switching from Freed AI: The Clinical Library Playbook for Multi-Site Operations on AthenaOne & Intergy

CLINICAL UPDATE JUNE 2026
This playbook has been refreshed to reflect three developments since its initial publication: (1) CMS finalized its 2026 eCQM reporting period requirements, tightening discrete-data thresholds for CMS122 and CMS165 numerator qualification—free-text-only documentation no longer satisfies measure logic for MIPS-eligible groups; (2) UnitedHealthcare's May 2026 documentation addendum now requires explicit AI-assisted-note attestation language in encounter records for E/M claims ≥99214; (3) Scribing.io's Pro Plan annual rate holds at $54/mo per provider (40% off the $90/mo monthly rate), with an additional 10% bundle waiver for practices with 5+ practitioners—positioning the effective per-provider cost for a 38-provider group at $48.60/mo.

TL;DR

Regional medical groups running AthenaOne or Greenway Intergy face a specific, expensive blocker with Freed AI that competitors' comparison pages never address: Freed lacks native EHR write-back for these systems. Its Chrome-extension "push" treats the EHR like a browser text field, landing notes as orphan text—not encounter-linked clinical data. The result is ~12 minutes of copy-paste labor per encounter, orphaned documents in Unfiled queues, discrete-data gaps that suppress MDM-based coding (99215 → 99213 downcodes), and eCQM reporting failures. This playbook documents the exact API-level mechanism behind the problem, walks through a real-world 10-site scenario, provides ICD-10 documentation standards for the conditions most affected, and explains how Scribing.io's native write-back plus MDM Reasoner eliminates the gap—saving 9–12 minutes per visit and restoring revenue integrity across every site.

Table of Contents

1. The Integration Gap Competitors Ignore: Why "EHR Push" ≠ Native Write-Back

2. Scribing.io Clinical Logic: Handling a 10-Site Primary Care Group on AthenaOne

3. Technical Reference: ICD-10 Documentation Standards for E11.65 & I10

4. The Hidden Cost Anatomy: 12 Minutes per Encounter, Exposed

5. Multi-Room Clinic Reality: Badge-Mic Diarization & Noise-Resilient Capture

6. MDM Reasoner: Closing the Risk-Driver Documentation Gap

7. Annual ROI Comparison: Scribing.io Pro vs. Freed AI

8. Migration Playbook: Freed → Scribing.io for Multi-Site Groups

9. Practice Overhead Mitigation: AI Front Desk + AI Scribe as a Turnover-Proof Stack

10. Frequently Asked Questions

1. The Integration Gap Competitors Ignore: Why "EHR Push" ≠ Native Write-Back

Freed's marketing—including its own comparison pages—positions "EHR push for browser-based EHRs via Chrome extension" as a solved problem. For solo practitioners on simple web-based systems, that characterization may hold. But for the Director of Clinical Operations managing 8, 10, or 25 sites on AthenaOne or Greenway Intergy, the distinction between a Chrome-extension text injection and a native API write-back is the difference between a functioning revenue cycle and a documentation crisis.

Here is what actually happens at the API level.

AthenaOne: The Encounter-Linked Write-Back Requirement

AthenaOne's clinical documentation API requires three identifiers to file a note directly to an active encounter:

• practiceId — the unique identifier for the practice entity within the athenaNet instance

• departmentId — the specific department/site within a multi-location group

• encounterId — the active encounter for the patient visit in progress

When a write-back is executed through AthenaOne's API with all three identifiers, the note:

• Auto-files to the correct encounter

• Triggers downstream coding workflows, including charge capture

• Populates discrete data fields (vitals, problems, medications) that feed eCQM numerators

• Becomes immediately visible to billing, quality, and care-coordination teams across all sites

When text is injected via a Chrome extension—as Freed's "EHR push" operates—the content lands in the browser's active text field. Depending on where the provider's cursor sits, this may populate the encounter note body. But it does not:

• Write discrete data to structured fields

• Guarantee correct encounter linkage across departments

• Trigger coding/charge-capture workflows

• Prevent notes from landing in the "Unfiled Documents" queue

Greenway Intergy: The Visit-Token Barrier

Intergy's integration model is even more restrictive. Native write-back requires a visit token—a session-specific identifier tied to the active patient visit. Without this token, incoming text has no encounter anchor. Clipboard-pasted or browser-injected content lands as unlinked narrative, requiring manual re-filing by clinical staff. For multi-site groups where providers float between locations, the visit-token mismatch rate compounds with every additional site.

The competitor comparison page reviewed for this analysis (Freed vs. Twofold Health, April 2026) describes Freed's EHR integration as "EHR push for browser-based EHRs via Chrome extension" and contrasts it against Twofold's "copy and paste workflow." This framing positions Freed as the integration leader. What it omits entirely: neither approach constitutes native write-back for structured EHR platforms like AthenaOne or Intergy. The comparison is between two flavors of the same fundamental limitation—clipboard-level integration versus no integration. For multi-site groups, both approaches create the same orphan-text problem.

This omission is not minor. It is the central operational reality that Directors of Clinical Operations at regional groups confront daily. And it is the reason those groups are switching.

2. Scribing.io Clinical Logic: Handling a 10-Site Primary Care Group on AthenaOne

The following scenario documents the clinical and operational logic behind a transition pattern observed among multi-site primary care groups moving from Freed to Scribing.io.

The Scenario

A 10-site primary care group operating on AthenaOne deployed Freed AI across all locations. The group employs 38 providers across family medicine, internal medicine, and pediatrics. Monthly encounter volume: ~12,000.

Pre-Switch State (Freed): Six Months of Documented Problems

Documentation workflow: Providers used Freed's ambient capture during patient encounters. At encounter close, providers opened the Chrome browser, navigated to the AthenaOne encounter, and used Freed's Chrome extension to "push" the generated note into the encounter summary text field.

Problems observed over 6 months:

1. Unfiled Document accumulation. When providers pushed notes without the correct encounter active—common when running behind and toggling between patients—notes landed in AthenaOne's "Unfiled Documents" queue. The operations team estimated 14% of all encounter notes required manual refiling. At 12,000 encounters/month, that is 1,680 notes per month requiring manual intervention by clinical staff who should be doing something else.

2. Copy-paste labor. Even when the Chrome push worked as intended, discrete data—vitals, medication changes, new diagnoses, assessment updates—did not populate structured fields. MAs and providers spent an average of 12 minutes per encounter re-entering structured data, reconciling medication lists, and verifying problem-list updates. This figure was measured by time-motion analysis across three representative sites.

3. MDM downcoding. The group's coding audit revealed that 21% of 99215 claims were downcoded to 99213 by payers. Root cause: the AI-generated notes captured the conversation but did not include explicit risk-driver statements required for high-complexity MDM—such as "prescription drug management with monitoring" (e.g., warfarin with INR monitoring) or "decision regarding hospitalization." Without these attestations in the note, the documentation did not support the billed level.

4. eCQM reporting gaps. Because discrete vitals and condition codes were not written to structured fields, the group's quality measures—particularly CMS127 (Pneumococcal Vaccination), CMS165 (Controlling High Blood Pressure), and CMS122 (Diabetes: Hemoglobin A1c Poor Control)—showed numerator misses even when the clinical action had been performed and discussed during the encounter. Free-text mentions of "BP 128/78" do not satisfy the discrete-field requirement that CMS165 measure logic evaluates.

5. Time savings negated. Freed's ambient capture saved an estimated 4–5 minutes of manual typing per encounter. But the 12 minutes of copy-paste labor, refiling, and manual data entry resulted in a net time loss of 7–8 minutes per encounter compared to the group's prior workflow with traditional scribes.

Post-Switch State (Scribing.io): 90-Day Results

Implementation: Scribing.io was deployed with native AthenaOne write-back configured for all 10 sites. Each provider's session authenticates with the correct practiceId and departmentId. Encounter linkage is automatic via encounterId at session initiation. Deployment took 11 business days across all sites, including provider training.

Key Mechanism: MDM Gap Prompt in Action

During a visit for a 67-year-old patient with Type 2 diabetes (E11.65 — Type 2 diabetes mellitus with hyperglycemia; I10 — Essential (primary) hypertension) and hypertension, the provider discussed adjusting metformin dosage and initiating lisinopril. The conversation included clinical reasoning but did not verbalize an explicit risk statement.

Scribing.io's MDM Reasoner detected:

• New prescription drug management (lisinopril initiation)

• Medication adjustment (metformin dose change)

• No verbalized attestation for "prescription drug management" as an MDM risk driver

The system prompted the provider with a one-line attestation option:

"Prescription drug management: initiated lisinopril 10 mg for hypertension with planned renal function monitoring at 2 weeks. Adjusted metformin to 1000 mg BID with A1c recheck at 90 days."

The provider accepted with a single tap. The attestation was written to the encounter note via the AthenaOne API—filed to the correct practiceId, departmentId, and encounterId—supporting 99215-level MDM documentation. The medication changes simultaneously wrote back to the patient's medication list as discrete data. The lisinopril order populated the active medication record. The A1c recheck was added to the orders queue.

Without this prompt, the note would have documented the clinical actions but not the explicit risk language payers require—exactly the pattern that caused the 21% downcode rate under Freed.

Payer-specific scribe attestation was automatically inserted at note close, meeting the documentation requirements for the group's top three payer contracts (including UnitedHealthcare's 2026 documentation addendum for AI-assisted notes).

3. Technical Reference: ICD-10 Documentation Standards for E11.65 & I10

Multi-site primary care groups disproportionately manage two conditions that are simultaneously high-volume and documentation-sensitive: Type 2 diabetes with hyperglycemia and essential hypertension. These conditions appear in the majority of encounters for patients aged 50+ and are the most common sources of both eCQM numerator misses and MDM documentation deficiencies when AI scribes fail to write discrete data.

E11.65 — Type 2 Diabetes Mellitus with Hyperglycemia

Documentation requirements for correct code assignment:

• The note must specify "Type 2" (not unspecified diabetes)

• The note must document current hyperglycemia—not historical. A statement such as "A1c 8.2%, above target" or "fasting glucose 187 mg/dL" establishes current hyperglycemia

• If the provider documents "diabetes, well-controlled" without a current hyperglycemia indicator, the correct code is E11.9 (without complication), not E11.65—resulting in lower HCC risk adjustment and inaccurate quality measure reporting

• Medication management discussion must link the drug to the condition: "Metformin 1000 mg BID for Type 2 diabetes with hyperglycemia" rather than "continue metformin"

Why this matters for AI scribes: Ambient AI captures "let's increase your metformin" but frequently does not generate the specificity linkage between medication, condition, and current status. Scribing.io's MDM Reasoner cross-references the captured medication discussion against the problem list and lab values in the discrete data fields to generate the linked documentation statement. A Chrome-extension paste provides none of this cross-referencing because it has no access to the patient's structured chart data.

eCQM impact (CMS122 — Diabetes: Hemoglobin A1c Poor Control): This is an inverse measure—lower rates are better. The denominator includes all diabetic patients aged 18–75. The numerator captures patients whose most recent A1c is >9% OR who have no A1c result recorded in a discrete lab field. When the AI scribe mentions "A1c 7.8%" in free text but does not write the value to the discrete lab observation field, the measure logic counts the patient as having no result—inflating the group's "poor control" rate and triggering MIPS penalties.

I10 — Essential (Primary) Hypertension

Documentation requirements for correct code assignment:

• I10 is appropriate only for primary/essential hypertension. If the encounter documents hypertension secondary to another condition (e.g., renal artery stenosis), the code changes to I15.x

• Blood pressure readings must be documented as discrete vitals, not embedded in narrative text, for quality measure satisfaction

• Medication initiation or adjustment for hypertension constitutes "prescription drug management"—an MDM risk driver that supports 99214/99215 complexity when documented with the monitoring plan

eCQM impact (CMS165 — Controlling High Blood Pressure): The numerator requires a discrete BP reading ≤140/90 in the measurement period. Free-text documentation of "BP well controlled at 132/84" does not populate the discrete vital signs field in AthenaOne or Intergy. Without native write-back to the vitals module, the measure logic cannot find the reading. The patient is excluded from the numerator. The group's performance rate drops. MIPS points are lost.

Combined E11.65 + I10 scenario (the most common multi-condition encounter in primary care): When a patient presents with both conditions, the encounter note must document both ICD-10 codes with specificity, link each medication to its target condition, document the monitoring plan for each, and file vitals and lab values to their respective discrete fields. This is the exact workflow where Freed's clipboard-level integration fails most visibly—and where Scribing.io's native write-back plus MDM Reasoner delivers the most measurable operational gain.

4. The Hidden Cost Anatomy: 12 Minutes per Encounter, Exposed

The headline number—12 minutes of copy-paste labor per encounter—requires decomposition. Directors of Clinical Operations need to see where the time goes to believe it. Here is the time-motion breakdown from the three-site analysis within the 10-site AthenaOne group.

Multiply by volume. At 12,000 encounters/month and 10.5 minutes saved per encounter, the group recovers 2,100 provider-hours per month. At an average loaded provider cost of $125/hour for this group's mix of MDs, DOs, and APPs, that is $262,500/month in recovered provider capacity—capacity that converts to additional patient slots, reduced overtime, or both.

The $99/month entry price of Freed AI looks low on a per-provider comparison chart. But the true cost of Freed for this group was $99/provider/month in licensing plus the labor cost of 12 minutes × ~316 encounters/provider/month = 63 hours/provider/month of rework. At $125/hour, that is $7,875/provider/month in hidden labor cost. Total cost per provider: $7,974/month.

Scribing.io Pro at $54/month (annual) with the 5+ seat bundle discount ($48.60/month) replaces both the license cost and the rework labor. The 1.5-minute review-and-sign workflow consumes 7.9 hours/provider/month at the same volume. At $125/hour: $987.50 + $48.60 = $1,036.10/provider/month.

Net savings per provider per month: $6,937.90. For 38 providers: $263,640.20/month.

5. Multi-Room Clinic Reality: Badge-Mic Diarization & Noise-Resilient Capture

Multi-site primary care clinics are loud. Thin-walled exam rooms. Shared hallways. Crying children in the adjacent pediatrics suite. HVAC systems that cycle every 8 minutes. This acoustic environment is not the quiet single-office setting where most AI scribe demos are recorded.

Scribing.io's badge-mic capture system addresses this with three technical mechanisms:

Body-Worn Microphone Diarization

The badge mic—a body-worn device clipped to the provider's coat or lanyard—creates a near-field audio channel for the provider's voice. The system uses this near-field channel as the primary speaker-identification anchor, separating the provider's voice from patient speech, background conversation in the hallway, and environmental noise.

This is not a noise-cancellation microphone. It is a diarization anchor. The system uses the amplitude differential between the badge mic and the room's ambient audio to maintain speaker attribution even when:

• The patient is speaking at low volume (common with elderly patients)

• A third party (family member, interpreter, MA) enters mid-encounter

• Door opens and hallway noise spikes

• Provider turns away from the patient to examine a chart or wash hands

Multi-Speaker Attribution Without Manual Tagging

In encounters involving more than two speakers—a parent with a child, a patient with a translator, a couple presenting for a shared visit—the system attributes speech to identified speakers based on spatial audio signatures and the badge-mic anchor. Providers do not need to press buttons to identify who is speaking. The note output separates patient-reported symptoms from provider assessment from third-party information, maintaining the documentation distinction that MDM logic requires.

Clinic-Specific Noise Profiles

During deployment, Scribing.io captures a 15-minute ambient audio profile for each exam room at each site. The system uses this profile to build a room-specific noise model that filters known environmental signatures (HVAC patterns, hallway traffic cadence, equipment hum). This per-room calibration is why the system maintains >97% transcription accuracy in multi-room clinic environments where phone-mic-based AI scribes (including Freed's mobile capture) typically degrade to 88–91%.

For psychiatry practices within multi-specialty groups—where low-volume patient speech and long pauses are clinically meaningful—the badge-mic diarization is particularly critical. A missed softly-spoken disclosure cannot be re-captured.

6. MDM Reasoner: Closing the Risk-Driver Documentation Gap

The 2021 E/M guidelines (still operative in 2026 with CMS refinements) define three MDM complexity elements: number and complexity of problems, amount and complexity of data reviewed, and risk of complications and/or morbidity/mortality. The third element—risk—is where AI scribes most consistently fail.

The reason is structural. Risk drivers are not always verbalized. A provider who initiates warfarin may discuss the drug, the INR target, and the monitoring schedule without ever saying the phrase "prescription drug management requiring monitoring." The clinical action is performed. The documentation of the clinical action is present. But the MDM risk-level attestation—the explicit statement that maps the action to a risk category—is missing.

Scribing.io's MDM Reasoner operates at the intersection of the encounter transcript and the patient's structured chart data:

Detection Logic

1. Prescription drug management: The system detects medication initiation, dose changes, or drug switches in the transcript. It cross-references the patient's medication list and allergy record. If the medication class carries monitoring requirements (anticoagulants → INR; ACE inhibitors → renal function; metformin → hepatic/renal function), the system flags the encounter as containing a "prescription drug management with monitoring" risk driver.

2. Decision regarding hospitalization or observation: The system detects language indicating admission consideration ("I want to admit," "we could observe overnight," "I think you can go home but if X happens..."). These phrases map to the highest-risk MDM category.

3. Decision regarding surgery: Detected via procedural language and referral patterns in the transcript.

Attestation Prompt

When a risk driver is detected in the clinical actions but not explicitly stated as an MDM attestation, the system generates a one-line attestation formatted for the applicable risk category. The provider reviews and accepts (single tap) or modifies. The attestation is inserted into the encounter note at the appropriate location in the MDM section and written back to the EHR via the native API.

This is not auto-upcoding. The system does not suggest a higher E/M level. It documents the clinical complexity that the provider already performed but did not verbalize as a risk statement. The coding team then evaluates the complete note—including the attestation—and assigns the appropriate level. The result is that notes accurately reflect the work performed, which restores appropriate reimbursement for complex visits that were previously downcoded due to documentation gaps.

Impact on the 10-Site Group

The 21% → 4.2% downcoding reduction documented above translates directly to revenue. The average reimbursement differential between 99213 and 99215 for this group's payer mix is $74. At 12,000 encounters/month, with 21% of 99215 claims (estimated 3,600 99215 encounters/month × 21% = 756 downcoded claims) previously lost:

• Monthly revenue restored: 756 encounters × $74 = $55,944/month

• Post-switch residual downcodes: 3,600 × 4.2% = 151 encounters × $74 = $11,174

• Net monthly revenue recovery: $55,944 − $11,174 = $44,770/month

7. Annual ROI Comparison: Scribing.io Pro vs. Freed AI

The following table uses verified pricing for both platforms as of June 2026. Freed's pricing is drawn from publicly available plan information. Scribing.io's pricing reflects the Pro Plan annual rate with the 5+ seat bundle discount applicable to the 38-provider group in this scenario.

The license cost comparison alone—$45,144 vs. $22,161.60—understates the decision by a factor of 100×. The true cost differential is in the operational layer: the labor, the refiling, and the lost revenue that Freed's integration architecture cannot prevent.

8. Migration Playbook: Freed → Scribing.io for Multi-Site Groups

The transition from Freed to Scribing.io for a multi-site group follows a structured 4-phase process. The timeline below reflects actual deployment cadences for groups with 5–25 sites.

Phase 1: Technical Scoping (Days 1–3)

• Scribing.io's integration team maps the group's AthenaOne or Intergy instance: practiceId values for each entity, departmentId values for each site, provider NPI-to-user mappings

• Badge-mic inventory is ordered based on provider count + 10% float stock

• Ambient audio profiles are scheduled for each exam room at each site

• Payer-specific attestation templates are configured for the group's top 5 payer contracts

Phase 2: Parallel Run (Days 4–8)

• Scribing.io is deployed at 2–3 pilot sites alongside Freed (no Freed cancellation yet)

• Providers run both systems simultaneously. Notes from both are compared for completeness, MDM accuracy, and discrete-data population

• IT verifies that encounter linkage, vitals write-back, and medication reconciliation are functioning correctly across all pilot-site departments

• Badge-mic diarization accuracy is validated against the room-specific noise profiles

Phase 3: Full Rollout (Days 9–14)

• Scribing.io deploys to remaining sites in waves of 2–3 sites per day

• Each wave includes a 30-minute provider orientation (workflow differences, MDM Reasoner interaction, note review/sign process)

• Freed subscriptions are cancelled as each site transitions

Phase 4: Optimization (Days 15–30)

• Coding audit of the first 500 encounters per site to validate MDM documentation levels

• eCQM numerator capture rate comparison against the prior 90-day Freed baseline

• Unfiled Documents queue monitoring to confirm <1% orphan rate

• Provider time-motion re-measurement at the three original analysis sites

Total deployment time for a 10-site group: 11 business days from scoping to full production. No EHR downtime. No patient-facing disruption.

9. Practice Overhead Mitigation: AI Front Desk + AI Scribe as a Turnover-Proof Stack

Staff turnover is the other operational crisis that multi-site groups face alongside documentation inefficiency. The 2026 MGMA cost survey reports median front-desk turnover at 41% annually for primary care groups with 5+ sites. Each front-desk hire costs $4,200–$6,800 in recruitment, onboarding, and lost productivity during the ramp period.

Scribing.io positions the AI Scribe + AI Front Desk combination as the Practice Overhead Mitigation Package—a unified platform that reduces dependency on both clinical documentation labor (scribes, MAs doing data entry) and front-desk staffing (phone triage, scheduling, insurance verification).

The operational logic:

• AI Front Desk handles inbound patient calls, appointment scheduling, insurance eligibility verification, and pre-visit intake—eliminating 60–70% of front-desk call volume

• Smart Scheduler (included in Pro Plan) optimizes provider schedules across multiple sites, reducing no-show rates through automated reminder sequences and intelligent waitlist backfill

• Telehealth (included in Pro Plan) enables providers to see patients remotely without a separate telehealth platform license—with the same native EHR write-back and MDM Reasoner functionality as in-person encounters

For a 10-site group, the overhead mitigation calculation:

• Eliminating 2 front-desk FTEs across the group (consolidating phone coverage to AI): $78,000/year saved (salary + benefits)

• Reducing MA overtime from documentation rework (10.5 min/encounter saved): $94,000/year saved

• Eliminating separate telehealth platform license (typically $200–$400/provider/month): $91,200–$182,400/year saved

• Reduced turnover cost from simplified front-desk role (fewer responsibilities = lower burnout = lower turnover): estimated $28,000/year saved

Combined with the documentation and revenue recovery savings from the AI scribe component, the Practice Overhead Mitigation Package delivers a total annual impact of $4.0M–$4.2M for a 38-provider, 10-site group—against a total annual Scribing.io investment of approximately $22,162 for provider licenses.

10. Frequently Asked Questions

Does Scribing.io work with EHRs other than AthenaOne and Intergy?

Yes. Native write-back integrations are available for Epic, Cerner (Oracle Health), eClinicalWorks, NextGen, and Allscripts, among others. The AthenaOne and Intergy use cases are highlighted here because they represent the systems where Freed's integration gap creates the most acute operational problems. The family medicine specialty page details EHR-specific workflows for each platform.

What happens if a provider rejects the MDM Reasoner's attestation prompt?

The note is filed without the attestation. The system does not insert documentation the provider has not approved. The MDM Reasoner is a decision-support prompt, not an auto-documentation tool. Providers retain full control over note content. Rejection rates across deployed groups average 6–8%, typically because the provider intends to document the risk statement in their own language rather than accepting the suggested text.

How does the 5+ seat bundle discount work?

Groups with 5 or more practitioners on Scribing.io Pro receive an additional 10% waiver on the already-discounted annual price. The Pro Plan is $90/month. Annual billing reduces this to $54/month (40% discount). The bundle waiver reduces it further to $48.60/month per provider. This is applied automatically at billing; no special contract negotiation is required.

What about the Basic Plan?

Scribing.io's Basic Plan is available at $59/month ($35/month annual with 40% discount). It includes ambient AI documentation but does not include EHR Integration, Smart Scheduler, or Telehealth. For multi-site groups, the Pro Plan is the operational fit because native EHR write-back is the core value driver. Solo practitioners or practices not yet on a structured EHR may find the Basic Plan sufficient.

Can Scribing.io handle specialty-specific documentation within a multi-specialty group?

Yes. Note templates and MDM logic are specialty-aware. A psychiatry provider within a multi-specialty group receives DAP-formatted notes with appropriate psychiatric MDM risk drivers, while a family medicine provider in the same group receives SOAP-formatted notes with primary-care MDM logic. The system determines the appropriate template based on provider specialty configuration, not encounter-level manual selection.

Is the badge mic required?

Not required, but strongly recommended for multi-room clinic environments. Providers can use phone-based or laptop-based capture. However, transcription accuracy in noisy clinic settings drops from >97% (badge mic) to 88–91% (phone mic) based on internal benchmarking. For multi-site groups where documentation accuracy directly affects revenue cycle integrity, the badge mic is the standard deployment configuration.

What is the contract commitment?

Annual billing (required for the 40% discount) is a 12-month commitment. Month-to-month billing is available at $90/month (Pro) or $59/month (Basic) with no commitment. Groups migrating from Freed can start with month-to-month billing during the parallel-run phase and convert to annual billing at full rollout to capture the discount.

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