At HIMSS26:
Generative AI for revenue cycle accuracy
AKASA uses custom-trained large language models to strengthen documentation accuracy, improve coding, and protect margin across 100% of inpatient encounters.
Net new revenue
(per 10,000 discharges)
Improves earned revenue
through prebill accuracy
Additional quality indicators
(per 10,000 discharges)
Strengthens risk adjustment,
severity capture,
and performance reporting
Annual net revenue improvement
for one client
Demonstrates enterprise-scale
financial impact
AKASA uses custom-trained large language models to strengthen documentation accuracy, improve coding, and protect margin across 100% of inpatient encounters.
Why legacy tools fall short
Existing tools:
Rely on rigid rules and keyword matching
Review samples — not every encounter
Lack clinical reasoning
Surface noise instead of insight
AKASA:
Reviews 100% of inpatient encounters
Synthesizes the full clinical record
Understands clinical context
Learns from your data, workflows, and teams
Recognition that matters
See what 100% encounter review could mean for you
AKASA partners with health systems to improve documentation integrity, DRG accuracy, and quality capture — at enterprise scale.
Let’s explore what that could look like in your environment.
Complete the form, and our team will follow up.
