Can we try it before committing?

Yes. A free pilot reviews an agreed subset of charts and reports potential coding gaps and missed opportunities. Findings depend on the records provided and require validation by your coding team before decisions are made.

Is patient data secure?

Security and privacy requirements must be evaluated for the contracted product. Ask for the certificate holder, scope, validity and supporting document. HIPAA obligations and local regulatory authorizations are different from ISO certification.

How does EHR integration work?

We connect directly to Epic, Cerner, MEDITECH, and other major systems. Charts flow automatically for pre-bill review—no manual uploads. Corrections push back to your workflow in real-time.

How do you stay current with coding rules?

We track payer-specific rules and local/national coverage determinations (LCD/NCD) so reviews stay aligned with current standards.

How do we identify problem areas?

Dashboards show coding patterns, error rates, and trends by provider, department, or facility. Spot who needs training, which codes get denied most, and track improvement over time.

How quickly can we go live?

Go-live timing depends on EHR access, data quality, workflow scope, security review, and team training. Before deployment, we agree on a coding baseline and reporting cycle so results are measured against the organization’s actual starting point.

Outpatient Coding

Bill accurately and reduce preventable denials

Automated pre-bill CPT/ICD-10 coding audits catch errors before submission, helping teams submit cleaner claims and reduce compliance risk.

Request a Demo

Last updated: October 5, 2026

The Challenge

Healthcare organizations face critical coding challenges that impact revenue and compliance

What causes denials and lost revenue in outpatient coding?

Coding discrepancies can arise when the billed service, clinical documentation and payer requirements do not agree. Review the relevant encounter, documentation gaps and coverage exceptions before choosing a correction. Do not infer a billable service from an incomplete record or treat a coding suggestion as proof that a payer will accept the claim.

Lost Revenue from Undercoding

Services under-documented or billed at lower levels create significant revenue leakage. Missed add-on codes and incomplete documentation leave money on the table.

Compliance Risks from Overcoding

Overbilling can trigger audits, force repayment, and create compliance risk. Without proper review, coding errors can lead to serious compliance issues.

Operational Inefficiencies

Review effort grows when coders must locate supporting records and resolve missing documentation. Measure time and exceptions on a defined sample.

AI Solution

AI-Powered Chart Review

Transform your coding workflow with intelligent automation that scales

How does AI pre-bill coding review reduce denials?

Assisted coding review compares available documentation with the rules configured for the agreed workflow. Each candidate should retain its source evidence and unresolved exceptions for a coder’s approval. Coverage depends on the records received and their quality. Confirm the applicable payer policy and regional code system; suggested changes do not establish reimbursement.

Capture Missed Revenue

Review potentially omitted codes only when the documented service, payer criteria and applicable code system support them.

Reduce Compliance Risk

Flag overcoding, documentation mismatches, and coverage-policy mismatches, such as local or national coverage determinations (LCD/NCD), before claims go out.

Scale Coding Capacity

Evaluate assisted review capacity on the received records. Human approval and unresolved exceptions remain part of the workload.

How It Works

A simple three-step process to transform your coding workflow

How do you implement AI pre-bill coding audits?

Evaluate the workflow in three stages: confirm available documents and applicable coding rules; review candidates with the source record and exceptions; approve a correction before submitting or exporting it. Test the specific connector and write-back permissions separately. Measure the reviewed cohort and missing data rather than promising coverage of records never received.

Customize Your AI

Work with a dedicated engineer to configure the AI environment for your specific coding requirements and payer rules.

Pre-Bill Review

Review coding candidates alongside supporting documentation and the configured payer rules; resolve exceptions before approval.

Approve corrections

Approve a supported correction, then verify the contracted export or EMR connector and write permissions separately.

Arkangel AI vs. manual coding audits

How AI pre-bill review compares with manual CPT/ICD-10 audits on coverage, speed, and denials.

Is AI coding review better than manual audits?

Manual and assisted review both require the same documentation, payer rules and accountable human decision. Compare them on a defined sample using evidence quality, unresolved exceptions, review time and adjudicated errors. An assisted candidate can save navigation effort but still needs validation. Do not assume full coverage, automatic EMR changes or a fixed denial reduction.

Capability	Arkangel AI	Manual coding audits
Chart coverage	Received records, with coding candidates and missing-data exceptions	Reviewer selects the agreed sample or cohort
Turnaround	Measure processing and human-review time on the pilot	Measure reviewer time using the same records
Rule application	Candidates use the configured payer rules; verify version and exceptions	Reviewer applies and documents the agreed criteria
Capacity	Measure assisted throughput and approval workload	Measure reviewer capacity, staffing and review time
Denial prevention	Candidates need source evidence and approval before submission	Reviewer records evidence, exceptions and approved changes

Human review

A coding candidate needs evidence, regional rules and a coder’s decision before it becomes an approved correction.

Frequently Asked Questions

Everything you need to know about chart intelligence for outpatient coding

Request a Demo

See how chart intelligence catches coding errors before they become denials

Review evidence before applying a finding

Review evidence before applying a finding
Review point	Manual review	Assisted review
Input	Read the source documents and define the question.	Organize the available question, documents and cited evidence.
Evidence	Find and open the original source.	Inspect each cited source and whether it supports the finding.
Exceptions	Check missing information and conflicting evidence.	Retain unresolved exceptions for human review.
Decision	Document the reviewer’s conclusion.	Approve or reject the candidate; assistance does not settle the decision.

Synthetic educational example · not a live product response

Coding review: inputs, exceptions and regional scope

Synthetic case: a visit record contains a candidate service but lacks supporting documentation. Output: a question for the coder with the relevant record excerpt, not an invented code or approved bill. Confirm documentation, payer criteria and exceptions before changing the claim.

US workflows use ICD-10-CM/CPT and payer requirements; Colombian CIE-10/CUPS/RIPS are separate systems. Confirm source completeness, rule version, review exceptions and the connector before promising EMR write-back. Track candidate, approved correction, submitted claim and collected payment separately.

Outpatient Coding

Bill accurately and reduce preventable denials

Automated pre-bill CPT/ICD-10 coding audits catch errors before submission, helping teams submit cleaner claims and reduce compliance risk.

Request a Demo

Last updated: October 5, 2026

The Challenge

Healthcare organizations face critical coding challenges that impact revenue and compliance

What causes denials and lost revenue in outpatient coding?

Lost Revenue from Undercoding

Services under-documented or billed at lower levels create significant revenue leakage. Missed add-on codes and incomplete documentation leave money on the table.

Compliance Risks from Overcoding

Overbilling can trigger audits, force repayment, and create compliance risk. Without proper review, coding errors can lead to serious compliance issues.

Operational Inefficiencies

Review effort grows when coders must locate supporting records and resolve missing documentation. Measure time and exceptions on a defined sample.

AI Solution

AI-Powered Chart Review

Transform your coding workflow with intelligent automation that scales

How does AI pre-bill coding review reduce denials?

Capture Missed Revenue

Review potentially omitted codes only when the documented service, payer criteria and applicable code system support them.

Reduce Compliance Risk

Flag overcoding, documentation mismatches, and coverage-policy mismatches, such as local or national coverage determinations (LCD/NCD), before claims go out.

Scale Coding Capacity

Evaluate assisted review capacity on the received records. Human approval and unresolved exceptions remain part of the workload.

How It Works

A simple three-step process to transform your coding workflow

How do you implement AI pre-bill coding audits?

Customize Your AI

Work with a dedicated engineer to configure the AI environment for your specific coding requirements and payer rules.

Pre-Bill Review

Review coding candidates alongside supporting documentation and the configured payer rules; resolve exceptions before approval.

Approve corrections

Approve a supported correction, then verify the contracted export or EMR connector and write permissions separately.

Arkangel AI vs. manual coding audits

How AI pre-bill review compares with manual CPT/ICD-10 audits on coverage, speed, and denials.

Is AI coding review better than manual audits?

Capability	Arkangel AI	Manual coding audits
Chart coverage	Received records, with coding candidates and missing-data exceptions	Reviewer selects the agreed sample or cohort
Turnaround	Measure processing and human-review time on the pilot	Measure reviewer time using the same records
Rule application	Candidates use the configured payer rules; verify version and exceptions	Reviewer applies and documents the agreed criteria
Capacity	Measure assisted throughput and approval workload	Measure reviewer capacity, staffing and review time
Denial prevention	Candidates need source evidence and approval before submission	Reviewer records evidence, exceptions and approved changes

Human review

A coding candidate needs evidence, regional rules and a coder’s decision before it becomes an approved correction.

Frequently Asked Questions

Everything you need to know about chart intelligence for outpatient coding

Request a Demo

See how chart intelligence catches coding errors before they become denials

Review evidence before applying a finding

Review evidence before applying a finding
Review point	Manual review	Assisted review
Input	Read the source documents and define the question.	Organize the available question, documents and cited evidence.
Evidence	Find and open the original source.	Inspect each cited source and whether it supports the finding.
Exceptions	Check missing information and conflicting evidence.	Retain unresolved exceptions for human review.
Decision	Document the reviewer’s conclusion.	Approve or reject the candidate; assistance does not settle the decision.

Synthetic educational example · not a live product response