AI-Suggested ICD-10 Codes, Flagged Before They Leave Your Agency.
Lime suggests ICD-10 codes from the clinical documentation and flags missing or potentially incorrect codes, helping your coding team work faster while maintaining CMS and payer compliance.
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Built for the agency owner whose biggest claim-denial driver is a coding miss. AI suggests codes and flags gaps before submission.
Features
Key Features
AI-Suggested Codes
AI analyzes clinical notes and proposes the most accurate ICD-10 codes, flagging missing or potentially incorrect codes for your coders.
Sequencing Support
Code sequencing matters for PDGM and medical necessity. AI proposes a sequence so your coders confirm it faster.
Documentation Prompts
When clinical documentation doesn't support a code, AI prompts the clinician for additional detail. Your coder reviews the response before signing off.
Audit-Ready Trail
Every code has a clear chain from clinical evidence to AI suggestion. If a RAC, MAC, or UPIC asks, you have the audit trail ready.
From a medical coder using Lime
The ability to quickly convert conversations or assessment details into structured documentation helps reduce repetitive typing and improves workflow efficiency.
Higher First-Pass Acceptance
Coding errors are the leading cause of denials. Accurate codes mean cleaner claims and faster payment cycles.
Fewer Denials From Coding Misses
Lime flags missing or potentially incorrect codes before submission, so your team fixes them before a payer does.
No Coding Backlog
AI drafts the codes, so your coders process more charts without hiring or pulling clinicians off patient care.
AI Works Best With a Certified Coder
Pair This AI With a Trained ICD-10 Coder
AI ICD-10 suggestions accelerate coding, but the highest-accuracy results come when a credentialed coder (CCS, HCS-D, HCS-H) reviews and finalizes the codes.
See Virtual Coding StaffSoftware is often included at no additional cost in staffing engagements.
Ready to improve your ICD-10 coding accuracy?
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