Clinical documentation · Production
DeepScribe AI Revenue Cycle: AI-Coded Claims from Clinical Encounter
The problem
Clinical encounters must be translated into correctly coded, compliant claims — a process prone to coding gaps, compliance risks, and delays in reimbursement.
Workflow diagram · grounded in source
1
Point-of-care encounter
trigger
“all at the point of care”
2
AI code recommendation
ai_action
“DeepScribe recommends the appropriate CPT, ICD-10, and HCC codes during the visit”
3
Denial-risk validation
validation
“DeepScribe flags any elements that need further review or could trigger claim denials”
4
Sync to billing systems
integration
“Structured outputs are pushed directly to your revenue cycle platform, EHR, or billing system”
Reported outcome
Physicians report completing notes before leaving the office, with no after-hours chart work remaining.
Reported metrics
Physician documentation burdengetting all my notes done before I leave the office
Reported stack
DeepScribeEHR
Frequently asked questions
What did this team achieve with this AI workflow?
Physicians report completing notes before leaving the office, with no after-hours chart work remaining.
What tools did this team use?
DeepScribe, EHR.
What results were reported?
Physician documentation burden: getting all my notes done before I leave the office (source-reported, not independently verified).
How is this clinical documentation AI workflow structured?
Point-of-care encounter → AI code recommendation → Denial-risk validation → Sync to billing systems.