SOLUTIONS | AUTONOMOUS CODING
SEE HOW IT WORKS
96%
CONFIDENCE-BASED ROUTING
Every encounter routes to the right next step — autonomous when proven, expert review when needed.
Proven Results
These stats are placeholders
overall CMI gain
system-wide
in potential
annual revenue impact
more coding opportunities
compared to other solutions
Chief Revenue Officer, 3B+ academic health system
Reduce coding burden
Fill staffing gaps, reduce reliance on high-cost labor, and lower the operational overhead of managing a large coding team — without replacing your people.
Improve DRG & quality performance
Strengthen revenue integrity through accuracy-first autonomy with full-chart review that captures the complete clinical picture.
Modernize your coding stack
Integrate with your existing encoder and CAC while creating a credible path toward a more efficient, modern coding infrastructure.
01
Prioritize the right encounters
AKASA identifies encounters appropriate for autonomous coding based on confidence, complexity, service line, DRG, and documentation signals.
02
Route by confidence
High-confidence encounters move toward autonomy; everything below your thresholds routes to your coding team with AI-generated analysis and supporting evidence.
03
Code from the full chart
AKASA reviews discharge summaries, H&Ps, operative notes, progress notes, and consults to capture the complete clinical picture.
04
Show the evidence
Every recommendation links directly to chart documentation, creating transparent validation and a clear end-to-end audit trail.
05
Adapt to your health system
AKASA continuously learns your case mix, documentation patterns, service lines, and coding practices over time. No generic data sets.
06
You stay in control
Health systems define confidence thresholds, review rules, service-line rollout, and audit requirements. AKASA automates only where performance is proven and routes everything else to expert review.
Legacy tools
Traditional CACs have rules-based logic that miss clinical nuance. Narrow DRG coverage and limited service lines. Sampling rather than full-chart review. Generic models not tuned to your health system. No credible path to true autonomy.
Outpatient-first AI vendors
Designed for low complexity encounters. Not built for inpatient DRGs. Retrofitted for inpatient, not purpose-built. Unproven at inpatient scale. No credible path to true autonomy.
AKASA autonomous coding
GenAI trained on your clinical and financial data. Highest volume MDCs and DRGs covered across major service lines. Full-chart review, every document, every time. Integrates with your existing EHR, encoder, and billing systems. ICD-10-CM/PCS, Coding Clinic, CMS, and payer guidelines built in. LLM built specifically for your health system.
Frequently asked questions
Does the system handle all inpatient encounters, or only certain specialties?
AKASA can be configured to match your organization’s comfort level. Many health systems start with specific specialties where documentation tends to be more straightforward and confidence scores are higher.
Is there a cap on length of stay or the number of notes the system can review?
AKASA is designed to handle encounters across a wide range of lengths of stay, including extended and complex admissions. There is no hard cap on the number of notes ingested per encounter.
From operative notes, does the system capture both diagnosis codes and PCS codes?
Yes. AKASA identifies and recommends both diagnosis codes and ICD-10-PCS procedure codes, extracting the clinical detail needed for accurate assignment.
Can procedure code recommendations be customized for our organization?
Yes. You can define which procedure types and service lines are in scope, tailoring the system to your coding practices, payer mix, and documentation patterns.
Which note types does the system pull in, and what counts as “codable” documentation?
AKASA uses a recommended set of note types, including discharge summaries, H&Ps, operative notes, progress notes, consults, and imaging reports.
How does the system handle addendums or late documentation added after initial coding?
AKASA monitors for late-arriving documentation that could affect accuracy and flags meaningful changes for re-review.
If one provider documents sepsis but the discharge summary does not, how is that handled?
The system lowers confidence and routes the encounter to a coder with the relevant evidence highlighted for expert determination.
How is the system trained, and does it continue to learn from our data?
AKASA’s models are pre-trained and then tuned using your organization’s clinical and financial data through structured feedback loops.
How does the system stay current with annual ICD-10 updates and Coding Clinic changes?
AKASA maintains current coding logic, including annual ICD-10-CM/PCS changes, Coding Clinic updates, and evolving CMS and payer-specific guidelines.
What determines whether an encounter is appropriate for autonomous coding?
AKASA evaluates confidence, DRG complexity, service line, documentation completeness, and historical accuracy. Your team defines the threshold.
How do teams start with autonomous coding?
Organizations begin with the specialties and encounter types that match their readiness, then expand autonomy as performance is proven.
How is coding quality monitored over time?
AKASA provides visibility into performance and routes exceptions to your experts, creating an auditable feedback loop for continuous improvement.
Ready to uncover the revenue you’ve been missing out on?
We’ll show you what GenAI can do for your organization. Fill out the form and we’ll be in touch.
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