Choosing the right AI medical coding software can improve revenue integrity and compliance, but recent category growth can make it hard to tell which tools are best for your healthcare organization.
This guide helps RCM leaders compare the most popular AI coding and chart review solutions for outpatient teams.
AI medical coding tools: Features and functions to consider
AI coding and chart review solutions help RCM teams automate rote tasks and achieve new efficiencies across several functions associated with medical coding and clinical documentation review.
The evaluation process for any tool should start with a determination of the functions where your team is seeking greater automation efficiencies, what other tools your team is already leveraging, and whether you will adopt various point solutions or seek a multi-purpose platform:
- Autonomous coding: AI verifies that CPT codes are accurate and adequately supported by documentation.
- Revenue discovery: AI ensures each encounter is fully coded to capture all revenue opportunities supported by the documentation.
- Documentation compliance: AI ensures documentation is unique, adequately supports CPT codes, and meets payer-specific guidelines
- Clinical quality: AI analyzes each note to determine whether the provider has followed all payer and clinical requirements and standards.
- Clinical intelligence: AI aggregates metrics related to clinical quality measures, coding accuracy, documentation compliance, and revenue capture across all encounters, delivering operational and clinical intelligence based on comprehensive rather than sample data.
Beyond these functional specifications, another key consideration is where your AI coding tools fit into existing workflows. Most RCM leaders want a solution that flags providers at the point of charting, so they can resolve issues before sending charts downstream.
In addition, a solution that aggregates performance data enables clear feedback loops for provider performance improvement programs without requiring leaders to invest in separate program design and implementation.
AI coding tools: Technical requirements
The first litmus RCM teams should apply to any tool regards two technical specifications that are unrelated to AI or medical coding: EHR integration and review coverage.
- EHR integration: Most outpatient teams decide between a tool that’s already native to their EHR, or one that integrates with it to solve for additional gaps or desired outcomes that are not well addressed by their EHR.
Without a seamless EHR integration, teams will be forced to design and implement an additional process for data imports and exports, and are less likely to benefit from reduced reimbursement timelines.
- 100% note coverage: Some AI solutions are limited to sample review. Without 100% review, teams do not experience the same benefits: revenue uplift will be artificially capped, and performance analytics will still be representative rather than comprehensive. .
Point of integration
Solutions that review at the point of charting are also the simplest and most efficient way to ensure claims are correct before submitting them. Anything else won’t flow well with common processes, demanding that you engage in costly, time-consuming workarounds that constrain profit margins and limit solution ROI.
Comparison of AI coding tools
Charta
Coding engine: LLM-based, customized per implementation by dedicated AI engineering
Coverage scope: 100% pre-billing chart review versus roughly 1% of charts typically covered by manual audit sampling
Workflow: Immediately after note close, pre-billing
Setting or specialty: Provider groups, MSOs, health systems, payers, and home health and hospice organizations across primary care, urgent care, and behavioral health
Payer specificity: Payer-specific compliance checks confirming documentation supports the highest defensible E/M level, not a single generic rule set
Integration
Every Charta implementation is built and maintained by a dedicated, CPC-certified AI engineer who customizes the integration to each practice's workflows and stays available to update the model as payer rules, CMS guidelines, and state requirements change. Charta has integrated with dozens of commercial EHRs, including ones in this guide, such as athenahealth, Experity, and eClinicalWorks, through marketplace integrations.
Documentation and coding
Charta reads handwritten notes, faxes, and scanned images in addition to typed documentation, a wider input range than a rules engine limited to structured fields. Unlike tools that only check whether documentation is present, Charta evaluates whether it satisfies payer guidelines, assessing each chart for uniqueness, completeness, and payer-specific compliance. It codes autonomously, flags unsupported or missing codes and E/M leveling inconsistencies, and routes corrections via autocorrect in the EHR, automated provider notes, or a billing work queue depending on how much human oversight a practice wants.
Compliance and audits
Charta is HIPAA, SOC 2, and GDPR-compliant and applies payer-specific compliance logic rather than one generic rule set. Every AI decision is backed by detailed reasoning and document citations, meaning organizations are always prepared for an audit.
Provider performance improvements
Because Charta reviews 100% of encounters, clinical leaders can compare performance across sites and providers, drill into individual encounters, and send automated scorecards with specific feedback on coding accuracy, documentation compliance, and care quality measures without manually reviewing charts.
Outcomes
Charta provides revenue uplift of up to 11%, an 11:1 return on investment, a 98% reduction in cost per chart reviewed, and full chart coverage versus roughly 1% under manual sampling. Customers report a 90% increase in pre-billing chart review coverage and a 25% reduction in clinical management workflow duties, achieved without adding headcount.
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athenahealth
Coding engine: Proprietary rules engine plus AI (Express Coding for one-click suggestions)
Coverage scope: Auto-populated coding with Clinical Documentation Improvement (CDI) nudges; Express Coding reportedly coded over half of charges during its beta
Workflow: At/after documentation, fed by ambient visit notes
Setting or specialty: Multi-specialty ambulatory practices
Payer specificity: Proprietary rules engine paired with AI payer surveillance, plus separate denial-reduction and payment-recovery tools
Integration
Express Coding and the CDI nudges are native to athenaOne, while athenahealth runs a marketplace of 500+ third-party solutions used by roughly 75% of its customer base. This results in a stitched-together system that can result in gaps and inconsistencies, requiring manual workarounds common to legacy systems.
Documentation and coding
Ambient Notes capture the encounter, while Express Coding reads that documentation plus any orders to auto-populate E&M and CPT codes with one-click confirmation, reportedly covering more than 51% of charges during its beta. Real-time CDI flags, added as part of the AI-native RCM rollout, are meant to catch documentation issues while the provider is still in the chart.
Compliance and audits
The proprietary rules engine pairs with AI-driven payer surveillance aimed at reducing denials before submission, but lacks the full scope of integrated capabilities that typically provide comprehensive reviews.
Provider performance improvements
CDI flags and automated denial advice surface directly to providers and billing staff as part of its AI RCM update.
Outcomes
athenahealth reports a 13% reduction in denials from its Auto Insurance Selection feature and a 26.4% increase in payment recovery from Automated Denial Advice, vendor-reported figures tied to the broader RCM suite, not the coding engine in isolation.
Given how mature the athenaOne rules engine already is, practices likely see solid baseline coding accuracy out of the box, but the bigger open questions relate to the additional capabilities and required lift of various third-party marketplace tools.
eClinicalWorks
Coding engine: Rules-based Clinical Rules Engine (CRE) with NLP, plus a newer AI-driven RCM layer
Coverage scope: Runs across submitted progress notes within the eCW RCM stack, not a blanket independent chart audit
Workflow: Mid-revenue-cycle, after documentation, before claim submission
Setting or specialty: Multi-specialty ambulatory practices on the eClinicalWorks EHR
Payer specificity: A Learning Engine adapts from actual payer rejections; Advanced Claims Editing applies 30,000+ pre-submission rules
Integration
The CRE and RCM AI tools are native to the eClinicalWorks EHR and revenue-cycle stack, so suggestions surface inside the system where providers already chart. Practices can define AI workflows using natural-language instructions rather than building custom rules from scratch, which requires internal monitoring for consistent prompts or risks someone confusing your system. The quality of responses to instructions may vary.
Documentation and coding
The Clinical Rules Engine combines structured-data triggers with keyword-oriented NLP to recommend E&M and CPT codes and drop ICD-10/Z codes. The newer AI layer scans full progress notes to flag coding gaps, but it only functions as a lightweight documentation check.
Compliance and audits
Advanced Claims Editing applies more than 30,000 rules before a claim goes out, aimed at clean-claim submission, but rules-based reviews are always more rigid—and therefore are potentially riskier—than some other reviews. The Learning Engine specifically adapts based on real payer rejection patterns rather than static rules alone, but that also requires historic data rather than providing the agility to respond to real notes.
Provider performance improvements
Feedback here runs mostly engine-to-engine with the Learning Engine adjusting its own rules from payer rejections rather than looping back directly to the clinician.
Outcomes
eClinicalWorks has tied its AI-driven RCM investment to a projected roughly $900 million revenue impact for its own business, a figure describing the company's business outcome, not an audited client-level result.
For an individual practice, the realistic expectation is fewer claim-edit rejections and cleaner E&M/CPT suggestions, since the tools center on claims editing and rule-learning rather than a full independent chart audit.
Experity
Functionality
Coding engine: Rules-based engine, native to the EHR
Coverage scope: Real-time auto-calculation during charting, not an independent post-visit chart audit
Workflow: During documentation (concurrent, point of care)
Setting or specialty: Urgent care (majority U.S. urgent care EHR market share), plus occupational medicine, primary care, telehealth, and workers' comp
Payer specificity: Not payer-specific; internal E&M over/under-coding reports, with the algorithm updated for annual code changes
Integration
Coding support is native to the Experity EHR/PM platform. Experity also offers an ambient AI scribe that captures the visit and feeds coding output directly into the same platform.
Documentation and coding
Experity reads structured documentation as the clinician charts, calculating ICD, CPT, and E&M codes, and flagging missing ancillary or procedure codes along the way—but it doesn't independently confirm that the note actually supports the code it generated.
Practice-level E&M reports show you under-coding and over-coding trends, but feedback is retrospective and aggregate rather than chart-by-chart coaching for individual providers.
Compliance and audits
Experity stated in 2021 that its coding algorithm is "audited and updated regularly," including automatic updates for annual E/M code changes. Payer-by-payer compliance checks or a documentation-citation trail for individual codes aren't detailed in its customer-facing information.
Provider performance improvements
Under/over-coding data is surfaced to practices through E&M reporting that management can use to coach providers, but it’s a practice-level report, not a real-time nudge to the individual clinician mid-chart or a personalized set of feedback.
Outcomes
Experity states its coding and ancillary-capture tools drive $11–14 of additional revenue per visit, a vendor-reported figure that has not been independently audited.
Its coding review works from structured triggers rather than full narrative review, so it's reasonable to expect it catches revenue tied to clearly documented ancillary services—but it’s less likely to catch subtler undercoding buried in text that never hits a rule trigger.
CodaMetrix
Coding engine: AI-powered coding automation, analyzing the full patient record over time
Coverage scope: Autonomous coding with human review routed to flagged or low-confidence cases, lacks blanket 100% autonomy
Workflow: Post-documentation, mid-cycle, feeding directly into billing
Setting or specialty: Health systems and academic medical centers, radiology, pathology, surgery, and other service lines
Payer specificity: Audits codes against payer-specific guidelines, update timing may vary
Integration
CodaMetrix integrates with Epic, Cerner, athenahealth, and eClinicalWorks to varying degrees. It doesn’t provide a full compatibility list, so other EHRs may not be supported.
Documentation and coding
Rather than reading a single note, CodaMetrix analyzes the longitudinal record, including problem lists, labs, imaging, prior notes, and medications, to generate ICD-10-CM, CPT, and HCPCS codes.
E/M coding specifically is flagged with lower confidence and, along with other uncertain cases, routed to a human coder together with the AI's rationale, offering a documentation-quality checkpoint rather than proactive fixes within the workflow.
Compliance and audits
CodaMetrix is HIPAA-attested and SOC 2 Type II certified, and its audit trail includes both the coding rationale and the specific EHR excerpt that supported the code.
Provider performance improvements
The feedback loop runs mainly to coders rather than providers, since flagged cases and rationale are routed to human coding staff. This requires leaders to design and implement their own provider improvement programs.
Outcomes
CodaMetrix states a 5:1 return on investment over five years, a 30% reduction in coding costs, 70% less manual effort, 5x faster turnaround, and a 60% reduction in denials.
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Nym Health
Coding engine: Proprietary Clinical Language Understanding (CLU) combining machine learning with rules-based clinical ontologies, but explicitly not a pure LLM approach
Coverage scope: Fully autonomous on charts it can confidently interpret, anything it can't parse routes to human coders
Workflow: Post-documentation
Setting or specialty: ED, urgent care, radiology, primary care, hospitalists, ancillary, and same-day surgery
Payer specificity: Applies AMA, CMS, and WHO coding guidelines
Integration
Nym targets large health-system EHRs integrating with Epic, Cerner (Oracle Health), and Meditech via FHIR.
Documentation and coding
Its CLU engine parses documentation directly to generate ICD-10-CM/PCS, CPT, E&M, modifiers, and HCPCS codes. It also provides documentation-improvement reporting, while any chart it can't confidently parse routes to a human coder rather than force-coded.
Compliance and audits
Nym describes its output as audit-ready with traceable documentation behind every code it assigns.
Provider performance improvements
Feedback is structured as documentation-improvement reporting rather than a real-time flag during charting.
Outcomes
Nym reports greater than 95% coding accuracy overall, with its newer radiology product cited at 96%+.
AKASA
Coding engine: Custom LLM trained per health system
Coverage scope: Reviews 100% of encounters
Workflow: Mid-revenue-cycle, between care delivery and billing
Setting or specialty: Enterprise health systems
Payer specificity: Flags missed documentation, unsupported codes, and DRG accuracy issues
Integration
AKASA's coding assistant is deployed as a strategic collaboration with health systems rather than a typical integration or self-serve marketplace app
Documentation and coding
The model reads full encounters to surface suggested codes along with the specific clinical references behind them, which a human coder then accepts or revises rather than coding fully autonomously. It recently piloted capabilities that flag missed documentation and DRG accuracy issues for certain customers with no mention of a wider rollout.
Compliance and audits
AI suggestions are backed by clinical references to the specific place in the chart, giving coders something concrete to verify, but compliance checks are not autonomous.
Provider performance improvements
The feedback loop runs to the coder, who accepts or revises the AI's suggestion, rather than directly back to the treating provider.
Outcomes
Because AKASA keeps a human coder in the loop on every chart, the realistic benefit looks more like faster, better-supported coder decisions than a fully hands-off coding process.
AGS Health (Autonomous coding)
Coding engine: AI paired with human coders
Coverage scope: Complete chart coverage, with roughly 40–50% coded autonomously
Workflow: Post-documentation, with a 2-business-day turnaround target
Setting or specialty: Health systems and provider organizations
Payer specificity: Computer-Assisted Professional Coding module supports MIPS and payer-specific rules
Integration
AGS Health's Autonomous Coding runs on its own AGS AI Platform, built in part on the EZDI technology it acquired in 2021, rather than being layered onto a third-party engine.
Documentation and coding
Its AI reads clinical documentation directly to generate ICD-10-CM/PCS, CPT, HCPCS, modifiers, E&M, and MIPS-related codes through its Computer-Assisted Professional Coding module. Then, charts pass through validation with CDI sold as a separate product rather than built into the coding tool itself.
Compliance and audits
Because every case runs through human validation, the compliance check is built around a person reviewing the AI's output rather than a fully automated audit trail, which could slow operations and limit consistency.
Provider performance improvements
Coder feedback trains the model over time, resulting in a coder-facing loop rather than one that reaches the treating provider directly.
Outcomes
Since roughly half of charts are handled autonomously at the outset with the rest reviewed by coders, the realistic near-term expectation is meaningfully reduced coder workload rather than full elimination of human coding.
XpertDox (XpertCoding)
Coding engine: Proprietary Hybrid AI Engine combining AI, NLP, and machine learning
Coverage scope: States greater than 94% automation on coded charts
Workflow: Post-documentation, autonomous coding feeding directly into claim submission
Setting or specialty: Urgent care and Federally Qualified Health Centers (FQHCs)
Payer specificity: Applies payer-specific coding logic
Integration
XpertCoding integrates with Epic, athenahealth, eClinicalWorks, ModMed, and Experity as a supported integration
Documentation and coding
Its AI reads clinical notes via NLP and machine learning to generate CPT, Category II CPT, ICD-10, and HCC risk-adjustment codes. A CDI dashboard surfaces documentation gaps, requiring cross-referencing.
Compliance and audits
XpertDox states the product maintains an audit trail and is HIPAA-compliant, but has limited other information.
Provider performance improvements
Its CDI dashboard provides information, but practice leaders must digest and analyze the information to design and implement improvement programs.
Outcomes
Across multiple customer case studies, it claims denial rates fall to roughly 1-2%, charge capture rises 15-20%, and quality-code capture improves 30-60%.
Solventum 360 Encompass (formerly 3M)
Coding engine: AI blending statistical models with human-led rules, built on the legacy 3M Clinical Reference System
Coverage scope: Computer-assisted coding suggests codes for coder review across all charts; a separate Autonomous Coding module codes routine outpatient cases
Workflow: Post-documentation using a single coding path across all settings
Setting or specialty: Hospitals and health systems
Payer specificity: DRG/APR-DRG grouping plus quality flags for PSIs, HACs, PPCs, and PPRs
Integration
360 Encompass integrates with hospital and enterprise EHRs.
Documentation and coding
NLP reads and prioritizes clinical documentation, linking each suggested code back to supporting documentation to generate ICD-10-CM/PCS, CPT, HCPCS, E/M, HCC, and DRG/APR-DRG codes, with a code-confidence feature auto-dropping high-confidence codes without coder intervention. CDI runs as a separate product.
Compliance and audits
The platform flags PSIs, HACs, PPCs, and PPRs, to support coding accuracy and hospital quality reporting.
Provider performance improvements
CDI surfaces flags directly to providers inside their EHRs, but only as part of the separate CDI product, which doesn’t provide aggregate feedback for individual providers or at scale
Outcomes
Given its decades-long history in hospital coding, 360 Encompass is a reasonable baseline for how mature, rules-plus-NLP CAC performs at scale, but it provides no leading accuracy figures to compare against newer LLM-based market entrants.
Conclusion
Healthcare organizations have a broad array of solutions from which to choose, but most are likely to benefit from AI-native chart review solutions that streamline workflows with flexible documentation and coding capabilities. These AI-native solutions are likely to outperform legacy systems that have rolled out AI tools to stay relevant.
At the same time, these legacy systems often provide reliable operations for your practice as a whole. It could be wisest to choose a solution that easily integrates at the right points and advances your capabilities as seamlessly as possible.
For more on how AI medical coding software can improve your RCM workflows and revenue integrity, request a demo to talk to an expert.
*The information in this guide is compiled from publicly available resources and may be edited for clarity and concision. It may also vary for users with direct access to specific vendors.
