Coding Optimizer

What it does

Coding Optimizer takes in coded inpatient encounters and related clinical documentation from a health system. It uses generative AI to review them and produces suggestions for missed diagnoses and procedures. Each suggestion includes a justification, exact quotes from the record, links to supporting documentation, a potential DRG or quality-indicator impact, denial and compliance risk flags, and a confidence level.

Coders work in a dashboard that can rank opportunities by DRG impact, quality indicators, or confidence. Suggestions appear alongside the documentation they cite, with coding guidelines and codebooks embedded for validation. Coders review, validate, and approve codes themselves rather than accepting AI output automatically. AKASA describes building a model trained on the health system's own clinical and financial data.

Designed for inpatient coding and CDI teams at health systems that want a second review of coded encounters. Access begins with a sales-led engagement, requested through a consultation or contact form on the AKASA site. Output quality depends on the health system's documentation, and every suggestion requires coder review and approval.

What it can do

• Reviews coded inpatient encounters and relevant clinical documentation across service lines
• Identifies missed diagnosis and procedure coding opportunities with justifications and exact quotes
• Surfaces potential DRG, quality indicator, and CMI impact
• Flags denial and compliance risks
• Assigns AI confidence levels to each suggestion
• Dashboard for prioritizing opportunities and linking to supporting documentation

Example workflows

Inpatient encounter review

Input: coded inpatient encounters and related clinical documents. Steps: the GenAI reviews encounters for missed opportunities and potential DRG and quality impact. Output: prioritized suggestions with justifications, quotes, and confidence levels.

Coder validation and approval

Input: AI suggestions in the dashboard. Steps: the coder checks the justification and linked documentation against embedded guidelines, then validates or rejects. Output: approved codes recorded by the coding team.

Before you choose

• Suggestions require coder validation and approval; the tool does not finalize codes.
• The model is trained per health system on its own data, so results depend on that documentation.
• Performance figures on the page are vendor-supplied results from a single health system and are not independently verified.

Listed under

• Healthcare
• Billing and revenue cycle
• Scribes and documentation
• Clinical documentation
• Compliance
• Denial prevention
• Health systems
• Inpatient coding

Get started

Request a consultation or submit the contact form on the page.

1. Schedule an AI consultation or submit the contact form on the AKASA site.
2. Provide clinical and financial data so AKASA can train a model for your health system.
3. Have coders review the dashboard and embed coding guidelines and codebooks for validation.

Pricing amounts, plans, and billing model are not published. Contact AKASA for pricing.

We couldn’t find published prices for Coding Optimizer. See their pricing page ↗

Sources for this overview

Product pages, documentation, and pricing checked 29 Oct 2026. Each source supports part of the overview; availability and prices can change.

• AKASA Coding Optimizer ↗akasa.com

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Last checked 29 Oct 2026. Every field keeps its source and date; unknown means we couldn’t confirm it.