At HFMA Tennessee:
AI for revenue cycle accuracy
$0.0M
Net new revenue
(per 10,000 discharges)
Improves earned revenue
through prebill accuracy
0,000
Additional quality indicators
(per 10,000 discharges)
Strengthens risk adjustment,
severity capture,
and performance reporting
$0M
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
Mid-cycle accuracy is just the beginning
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.
