Posted on
Jun 2, 2026
Doximity GPT vs. Scribing.io: Clinical Logic Comparison for Tech-Forward MDs
Doximity GPT vs. Scribing.io: Clinical Logic Comparison for Revenue Cycle Leaders
Operations Playbook — Last Updated June 2026 | Author: Clinical Operations Team, Scribing.io
TL;DR — Why This Comparison Matters to Your Bottom Line
Doximity GPT is a prompt-based AI writing assistant that drafts clinical notes from physician input. Scribing.io is a clinical-logic engine that understands revenue-critical modifiers like -25, enforces line-level diagnosis-to-claim linking, and writes payer-ready attestation language directly into your EHR's charge capture. The AMA's 2026 guidance on augmented intelligence warned about "opaque model reasoning" and "confabulations in generative models" — precisely the risks that surface when a generic LLM handles coding-adjacent documentation without deterministic clinical rules. This playbook shows Revenue Cycle Directors exactly where prompt-based AI fails at the claim line and how clinical-logic architecture closes the gap — with recoverable revenue measured in five figures per quarter, per clinic.
Table of Contents
What the Industry Gets Wrong About AI Scribes and Modifier 25
Scribing.io Clinical Logic: The Modifier-25 Before-and-After
Technical Reference: ICD-10 Documentation Standards
Prompt-Based vs. Clinical-Logic Architecture: A Structural Comparison
Why the AMA's 2026 Transparency Mandate Validates Clinical-Logic Engines
EHR Integration Depth: Charge Capture, API, and RPA Workflows
Revenue Cycle Impact Model: Denial Reduction and Coder Efficiency
Next Steps for Revenue Cycle Directors
What the Industry Gets Wrong About AI Scribes and Modifier 25
Most AI-scribe evaluations grade surface features: transcription accuracy, note-generation speed, ambient listening quality. Revenue Cycle Directors already know these metrics are table stakes. The question that actually drives write-offs and audit exposure is different:
Does the AI understand that correct Modifier 25 use requires line-level diagnosis-to-claim linking plus a payer-ready attestation proving the E/M service addressed a clinically distinct problem?
Competitors — including well-funded, widely adopted tools like Doximity GPT — gloss over this requirement because their architecture was never built to enforce it. Doximity GPT is, at its core, a prompt-based assistant: a large language model that receives physician-authored or ambient-captured text, applies a generative completion, and returns a drafted note. It does not maintain a deterministic rules engine that validates whether the E/M line's supporting diagnosis is non-overlapping with the procedure line's diagnosis at the claim level.
This is not a minor documentation nuance. It is the structural root cause of the most common same-day E/M denial in procedural specialties — a denial pattern flagged repeatedly by the OIG and one that CMS contractors actively target in post-payment audits.
The Gap No One Talks About
Here is what a prompt-based system cannot reliably do:
Confirm separately identifiable work — Modifier 25 requires that the E/M service be "separately identifiable" from the procedure performed. A prompt-based LLM can be instructed to "add -25 language," but it cannot deterministically verify that the physician's narrative actually documents a distinct clinical problem that was separately evaluated and managed.
Bind a non-overlapping ICD-10 to the E/M line — If a patient presents for cryotherapy of an actinic keratosis (17000 / L57.0 - Actinic keratosis) and the physician also manages hypertension (I10 - Essential (primary) hypertension), the E/M line must carry I10 — not L57.0. A generative model that "writes a good note" may describe both conditions beautifully yet leave the charge capture line with L57.0 mapped to both the procedure and the E/M, guaranteeing a denial.
Inject payer-specific attestation language — Different payers require different attestation frameworks. A CMS Medicare Administrative Contractor may want explicit "separately identifiable" language. A commercial payer may require a distinct chief complaint narrative for the non-procedural problem. Prompt-based tools generate generic prose; they do not toggle attestation templates by payer ID on the claim.
Scribing.io's clinical-logic engine was purpose-built to solve all three. It does not generate notes from a prompt and hope the coding is implied. It validates separately identifiable work against a rules engine, binds the correct non-overlapping ICD-10 code to each claim line, and writes that linkage into the EHR's charge capture layer via direct API or RPA integration — in Epic, athenahealth, eClinicalWorks, and other major platforms.
Scribing.io Clinical Logic: The Modifier-25 Before-and-After
This is the centerpiece use case. If you are a Revenue Cycle Director evaluating AI scribes, read this section line by line — it mirrors the exact scenario that drives preventable write-offs in procedural specialties ranging from dermatology and orthopedics to pain management and ophthalmology.
Before: Prompt-Based AI (Doximity GPT Workflow)
A 6-provider dermatology clinic uses Doximity GPT to draft notes for same-day cryotherapy plus an office visit. The LLM produces well-structured, physician-reviewed SOAP notes. The documentation reads well to a human eye. But:
18% of 99213-25 claims are denied because the note lacks a distinct problem narrative and the E/M line shares the lesion diagnosis (L57.0) with the procedure line (17000).
Coders spend 12 hours per week reworking these charts — querying physicians for addenda, re-mapping diagnosis codes, and resubmitting claims.
$26,000 per quarter is written off from unrecoverable denials, timely filing expirations, and reduced reimbursement on downgraded claims.
The root cause is architectural: Doximity GPT drafted the note, but no clinical-logic layer validated that the E/M documentation proved separately identifiable work. The charge capture inherited the procedural diagnosis on both lines because no rules engine intervened between the note and the claim.
After: Clinical-Logic Engine (Scribing.io Workflow)
The same 6-provider dermatology clinic deploys Scribing.io. Here is the granular, step-by-step logic breakdown of what changes at the system level:
Procedure + E/M Detection — Scribing.io's logic engine parses the encounter in real time and detects that CPT 17000 (destruction of premalignant lesion, first lesion) and CPT 99213 are being documented in the same session. The encounter is immediately flagged as a Modifier-25 candidate. This flag is deterministic — triggered by CPT co-occurrence rules, not a probabilistic LLM prediction.
Separately Identifiable Work Validation — The engine analyzes the clinical narrative against a structured checklist: Was a distinct chronic problem (e.g., essential hypertension, I10) assessed, evaluated, or managed during the visit beyond the pre-procedural evaluation for the actinic keratosis? The system checks for documented evidence of at least two of four elements: (a) a vital sign relevant to the non-procedural diagnosis, (b) medication review or adjustment, (c) risk assessment, or (d) patient counseling. If the narrative fails this validation, the physician receives a real-time prompt — not a vague "consider adding detail," but a structured gap alert: "E/M line requires documentation of hypertension management (BP interpretation, medication review, or risk assessment) to support separately identifiable work for -25."
Payer-Specific -25 Attestation Injection — Based on the patient's payer ID already on file in the EHR, Scribing.io selects the appropriate attestation template. For Medicare (MAC jurisdiction), this includes explicit "separately identifiable E/M service" language per CMS E/M guidelines. For commercial payers with stricter -25 policies (Anthem, UnitedHealthcare, Cigna), it injects a distinct chief complaint and assessment block for the non-procedural problem, formatted to match that payer's documentation review criteria. The attestation is not appended as a generic footer — it is woven into the note's assessment and plan section so auditors see clinical reasoning, not boilerplate.
Line-Level ICD-10 Mapping in Charge Capture — Scribing.io maps L57.0 → CPT 17000 (procedure line) and I10 → CPT 99213-25 (E/M line) inside the EHR's charge capture module. This mapping is written directly into the claim line via the Epic integration (SmartLink population within the professional billing workflow) or the athenahealth integration (claim rule injection via the athenaCollector API). It is not a sidebar suggestion that a coder must manually act on. The diagnosis-to-CPT binding is enforced at the data layer before the claim reaches the clearinghouse.
Pre-Submission Validation — Before the encounter closes, the engine runs a final check: Does the E/M line carry a diagnosis that is non-overlapping with every procedure line on the claim? Is the attestation language present and payer-matched? Is the -25 modifier appended to the correct CPT line? If any element fails, the encounter is held in a "review required" queue — visible to both the provider and the coding team — with a specific deficiency code and a one-click fix path.
Measured Outcomes
Metric | Before (Prompt-Based AI) | After (Scribing.io Clinical Logic) | Delta |
|---|---|---|---|
99213-25 Denial Rate | 18% | 2% | −16 percentage points |
Coder Rework Hours / Week | 12 hours | ~7 hours | −40% |
Quarterly Write-Off | $26,000 | Net recovered: $38,400 in Q1 | +$64,400 swing |
Audit Risk Exposure | High (shared Dx across lines) | Low (attestation + distinct Dx mapping) | Materially reduced |
Physician Addendum Requests / Week | ~15 | ~3 | −80% |
The $38,400 recovered in the first quarter reflects not just denied-claim recovery but also the elimination of coder overtime, the avoidance of timely-filing losses, and the capture of previously unbilled E/M services that physicians had been skipping because the coding complexity was not worth the hassle during a 15-minute slot.
The critical distinction for Revenue Cycle Directors: This is not a documentation-quality comparison. Both tools produce readable notes. The difference is that Scribing.io enforces claim-line integrity at the point of documentation — before the claim is ever submitted. That is the architectural divide between a writing assistant and a clinical-logic engine.
Technical Reference: ICD-10 Documentation Standards
The Modifier-25 scenario above hinges on correct ICD-10 assignment at the claim line. This section provides the technical reference for the two codes central to the dermatology use case and explains how Scribing.io ensures each code reaches maximum specificity to prevent denials.
L57.0 — Actinic Keratosis
Field | Detail |
|---|---|
ICD-10-CM Code | |
Description | Actinic keratosis — solar keratosis, senile keratosis |
Category | L57 — Skin changes due to chronic exposure to nonionizing radiation |
Chapter | XII — Diseases of the skin and subcutaneous tissue (L00–L99) |
Clinical Documentation Requirements | Anatomic location, morphology (hyperkeratotic, erythematous, pigmented), number of lesions; method of destruction if treated; documentation of whether biopsy was performed or deferred |
Modifier-25 Role | Supports the procedure line (e.g., 17000). Must NOT be duplicated on the E/M line when a distinct problem is separately evaluated. |
Common Denial Trigger | L57.0 mapped to both the procedure and E/M lines — payer interprets E/M as part of the pre-procedural workup, not separately identifiable |
Scribing.io Logic | Engine locks L57.0 to the 17000 line and prevents auto-population to the E/M line. If no distinct E/M diagnosis is present, the system blocks -25 modifier attachment and alerts the provider. |
I10 — Essential (Primary) Hypertension
Field | Detail |
|---|---|
ICD-10-CM Code | |
Description | Essential (primary) hypertension |
Category | I10–I16 — Hypertensive diseases |
Chapter | IX — Diseases of the circulatory system (I00–I99) |
Clinical Documentation Requirements | Current BP reading with interpretation, medication review/adjustment, assessment of control status (controlled vs. uncontrolled), plan for follow-up or escalation |
Modifier-25 Role | Supports the E/M line (e.g., 99213-25) as a clinically distinct problem addressed during the same encounter. The physician must document evaluation and/or management of hypertension beyond acknowledging its existence on the problem list. |
Documentation Minimum for -25 Support | At least two of: (1) BP measurement with clinical interpretation, (2) medication review or adjustment, (3) assessment of end-organ risk or comorbidity interaction, (4) patient counseling on lifestyle modification |
Scribing.io Logic | Engine validates that the note contains ≥2 of the four required management elements for I10 before allowing the code to populate the E/M claim line. If documentation is thin — e.g., only "HTN noted, stable" — the system flags the note as insufficient and prompts the provider with the specific missing elements. |
How Scribing.io Ensures Maximum Specificity
Generic AI scribes often default to unspecified or parent-level ICD-10 codes because the LLM lacks a code-validation layer. Scribing.io prevents this through three mechanisms:
Specificity Cascade: The engine checks whether the clinical narrative supports a more specific child code before assigning a parent. For example, if a provider documents "hypertensive heart disease with heart failure," the engine will not settle on I10 — it will push toward I11.0 and validate the HF classification (I50.x) per CMS ICD-10-CM Official Guidelines.
Laterality and Anatomic Site Enforcement: For dermatologic codes that require site specificity (e.g., melanocytic nevi by location), the engine cross-references the note's anatomic documentation against the code's descriptor and rejects mismatches.
Excludes1 / Excludes2 Validation: The engine checks ICD-10 exclusion notes to prevent co-coding errors — a step that prompt-based systems routinely miss because they generate codes from narrative context rather than from the code set's relational logic.
L57.0 - Actinic keratosis; I10 - Essential (primary) hypertension — these are the two codes at the center of the most common same-day E/M + procedure denial in dermatology, and they illustrate why code-level logic — not narrative-level generation — determines whether a claim survives or dies.
Prompt-Based vs. Clinical-Logic Architecture: A Structural Comparison
Revenue Cycle Directors need a framework for evaluating AI scribes that goes beyond feature lists. The table below maps the structural differences between prompt-based systems (represented by Doximity GPT) and clinical-logic engines (represented by Scribing.io) across the dimensions that drive claim outcomes.
Dimension | Doximity GPT (Prompt-Based) | Scribing.io (Clinical-Logic Engine) |
|---|---|---|
Core Architecture | Large language model with medical prompt tuning; generates text completions from input | Deterministic rules engine layered on top of ambient AI; validates clinical-to-billing logic before note finalization |
Modifier-25 Handling | Can be prompted to "include -25 language"; no validation that the narrative proves separately identifiable work | Detects procedure + E/M co-occurrence, validates distinct problem documentation, injects payer-specific attestation, and blocks -25 if criteria are unmet |
ICD-10 Assignment | Suggests codes based on narrative context; no line-level binding to CPT | Assigns codes via specificity cascade, binds each ICD-10 to its corresponding CPT line in charge capture, and prevents cross-line duplication |
Charge Capture Integration | Note is generated; coding and charge capture are downstream manual steps | Diagnosis-to-CPT mapping is written directly into the EHR's charge capture module via API (Epic, athenahealth) or RPA (eClinicalWorks, NextGen) |
Payer-Specific Logic | No payer awareness; generates one note regardless of payer on file | Reads payer ID from the EHR, toggles attestation templates and documentation thresholds by payer category (Medicare, Medicaid, commercial, workers' comp) |
Audit Trail | LLM-generated text; reasoning is opaque and non-reproducible (the same prompt may yield different outputs) | Every rules-engine decision is logged with the specific rule ID, input data, and pass/fail status — producing a deterministic, auditable trail |
Error Mode | Hallucination and confabulation: the model may fabricate clinical details or generate plausible-sounding but incorrect attestation language | Fail-safe: if the rules engine cannot validate a claim element, it holds the encounter for human review rather than generating a plausible-but-wrong output |
Physician Workflow | Physician reviews and edits a drafted note | Physician receives real-time structured gap alerts during documentation; note is finalized only when all claim-critical elements are validated |
The fundamental difference: Doximity GPT optimizes for note completeness. Scribing.io optimizes for claim survivability. A complete note that cannot survive a payer's claim edit or a post-payment audit is a liability, not an asset.
Why the AMA's 2026 Transparency Mandate Validates Clinical-Logic Engines
The AMA's Principles for Augmented Intelligence and its 2026 Annual Meeting resolutions specifically address two risks that are inherent to prompt-based AI documentation:
Opaque model reasoning: When a generative model drafts a clinical note, the reasoning chain that produced the output is not inspectable. If a payer or auditor asks why a specific attestation phrase was included or why a particular ICD-10 code was selected, the system cannot produce a deterministic answer. The AMA's position is that AI-generated clinical content must be transparent and explainable.
Confabulation in generative models: LLMs can produce clinically plausible text that does not reflect the actual encounter. A 2024 JAMA study on AI-generated clinical documentation found that generative models inserted clinical details not present in the source input in a measurable percentage of encounters. In a Modifier-25 context, a confabulated assessment of hypertension management could create fraud exposure if the physician did not actually evaluate hypertension during the visit.
Scribing.io's architecture directly addresses both concerns:
Explainability: Every attestation phrase, code assignment, and modifier decision is traceable to a specific rule ID with documented pass/fail logic. When an auditor asks "why was -25 applied?", the system can produce: Rule M25-003: Procedure 17000 co-documented with E/M 99213; distinct diagnosis I10 validated on E/M line with 3/4 management elements documented (BP interpretation, medication review, lifestyle counseling); payer template CMSMAC-v4 applied.
Anti-confabulation: The rules engine does not generate clinical content. It validates that clinical content documented by the physician meets the threshold required for the billing decision. If the physician did not document hypertension management, the system does not invent it — it flags the gap and holds the modifier.
For Revenue Cycle Directors navigating compliance in a post-HHS AI transparency rule environment, this distinction is not academic. It is the difference between an audit-defensible documentation system and one that introduces new categories of compliance risk.
EHR Integration Depth: Charge Capture, API, and RPA Workflows
An AI scribe that produces a perfect note but does not touch the charge capture layer is leaving the hardest part of the revenue cycle to humans. The translation from clinical documentation to billable claim line is where most -25 errors originate — and it is exactly the layer that prompt-based tools do not reach.
Scribing.io's Integration Architecture
EHR Platform | Integration Method | Charge Capture Mechanism | Modifier-25 Workflow |
|---|---|---|---|
FHIR R4 API + SmartLink | Diagnosis-to-CPT binding populated directly in the professional billing SmartForm; SmartLinks auto-populate attestation language in the note | -25 modifier attached to E/M line with distinct Dx; SmartAlert fires if validation fails | |
athenaCollector API + athenaFlex custom rules | Claim rules injected at the encounter level; Dx pointer fields auto-populated per validated line mapping | Custom claim rule prevents submission of -25 claims with overlapping Dx on procedure and E/M lines | |
eClinicalWorks | RPA (robotic process automation) + HL7 ADT feed | RPA bot populates charge capture fields in the eCW billing module based on rules-engine output | Bot validates Dx separation before populating modifier; exceptions routed to coder worklist |
NextGen | RPA + NextGen API (where available) | Hybrid integration: API for note data, RPA for charge capture population in the billing workflow | Same validation logic; RPA handles the NextGen-specific field mapping |
Why Integration Depth Matters for -25 Denials
Doximity GPT generates a note. That note exists in the documentation layer. A human coder then reads the note, interprets the clinical content, selects CPT and ICD-10 codes, assigns modifiers, and builds the claim. Every handoff in that chain introduces error. The coder may not notice that L57.0 was the only diagnosis discussed. The charge capture template may auto-default the procedure diagnosis to the E/M line. The modifier may be applied reflexively without validating the note's support.
Scribing.io eliminates three of those handoffs by writing the validated claim-line data directly into the charge capture layer. The coder's role shifts from building the claim to reviewing a pre-validated claim — a workflow change that accounts for the 40% reduction in rework hours documented in the dermatology case study above.
Revenue Cycle Impact Model: Denial Reduction and Coder Efficiency
The financial model below extends the dermatology case study to help Revenue Cycle Directors estimate the impact of moving from a prompt-based AI scribe to a clinical-logic engine. Adjust the inputs to match your specialty, volume, and payer mix.
Input Variable | Dermatology Case Study Value | Your Clinic (Adjust) |
|---|---|---|
Number of providers | 6 | — |
Same-day E/M + procedure encounters / provider / week | 18 | — |
Average E/M reimbursement (99213-25) | $92 | — |
Baseline -25 denial rate (prompt-based AI) | 18% | — |
Post-implementation -25 denial rate (Scribing.io) | 2% | — |
Coder fully loaded hourly cost | $38 | — |
Rework hours eliminated / week | 5 | — |
Previously unbilled E/M services captured / week (clinic-wide) | 12 | — |
Quarterly Impact Calculation
Denied claims recovered: 6 providers × 18 encounters/week × 13 weeks × 16% denial reduction × $92 = $20,678
Coder rework savings: 5 hours/week × $38/hour × 13 weeks = $2,470
Previously unbilled E/M capture: 12 encounters/week × $92 × 13 weeks = $14,352
Timely filing loss avoidance (estimated): $900
Total Q1 impact: $38,400
These figures are conservative. They do not include the downstream benefit of reduced audit exposure, lower compliance consulting costs, or the physician satisfaction impact of eliminating addendum requests — a factor that, per NIH-published research on documentation burden, directly correlates with provider retention.
Scaling Beyond Dermatology
The -25 modifier problem is not dermatology-specific. Any specialty that routinely performs minor procedures alongside E/M services faces the same denial pattern:
Orthopedics: Joint injections (20610) + E/M for chronic pain or osteoarthritis management
Pain Management: Trigger point injections (20552) + E/M for medication management
Ophthalmology: Foreign body removal (65222) + E/M for concurrent dry eye or glaucoma evaluation
Podiatry: Nail debridement (11721) + E/M for diabetic foot assessment
Gastroenterology: Same-day consult + endoscopy where the consult addresses a distinct GI problem
In every case, the clinical-logic requirements are identical: detect the co-occurrence, validate separately identifiable work, bind non-overlapping ICD-10 codes to the correct claim lines, and inject payer-appropriate attestation. Scribing.io's rules engine applies the same deterministic logic across all of these specialties — the only variable is the CPT and ICD-10 code sets loaded into the specialty-specific rule module.
Next Steps for Revenue Cycle Directors
If your organization uses a prompt-based AI scribe — whether Doximity GPT, Nabla, Abridge, or another generative tool — and your providers perform same-day procedures with E/M services, you likely have a quantifiable -25 modifier exposure that is costing you five figures per quarter.
The 15-Minute Workflow Audit
Book a 15-minute Workflow Audit with Scribing.io: we'll run a -25 Risk Heatmap on your last 100 same-day procedure encounters, show the exact charts missing distinct diagnosis linking and attestation language, and deliver an EHR-specific fix path tailored to your platform — whether that is Epic, athenahealth, eClinicalWorks, or another system.
Our guarantee: If we can't surface at least $10,000 in avoidable denials from those 100 encounters, we'll document the root causes and remediation steps within 48 hours — at no cost. No pitch deck. No generic demo. Just your data, your EHR, and a clear path to recovered revenue.
→ Schedule your -25 Risk Heatmap audit at Scribing.io
What You'll Receive
-25 Risk Heatmap: A chart-by-chart breakdown of your last 100 same-day procedure + E/M encounters, flagging which ones have overlapping diagnoses, missing attestation, or insufficient separately identifiable documentation.
Denial Recovery Estimate: A dollar-specific projection of recoverable revenue based on your payer mix, denial rates, and encounter volume.
EHR-Specific Fix Path: A technical implementation brief showing exactly how Scribing.io's rules engine integrates with your charge capture workflow — API endpoints, SmartLink configurations, RPA bot specifications, or custom claim rules — depending on your platform.
Compliance Posture Assessment: An evaluation of your current documentation against the AMA's augmented intelligence principles and CMS audit criteria for -25 modifier use.
The gap between a prompt-based AI scribe and a clinical-logic engine is not a feature comparison. It is a revenue cycle architecture decision. Every quarter you operate without deterministic claim-line validation is a quarter you are writing off recoverable revenue and accumulating audit exposure.
Scribing.io — Clinical logic that survives the claim line.
