Camarena Health returns 12,800 clinician hours with Freed AI scribe across 24 sites
Camarena Health's providers across 24 clinical sites relied on human scribes, but high turnover and callouts meant providers were working with a new scribe almost every week. Retraining was a recurring burden, and the resulting documentation gaps caused billing delays as providers tried to close over 22 daily notes each.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Providers deliver patient care
On any given day, 50–60 providers deliver care across 10+ specialties, creating documentation work per encounter.
Tools used
FreedEHR
Outcome
Over 50 clinicians across 24 sites adopted Freed with no heavy training or IT implementation cycle. Documentation time dropped from roughly 10 minutes to about 2 minutes per visit, returning approximately 12,800 hours to clinicians in a single year—the equivalent of roughly 6 full-time clinicians—while also accelerating the billing cycle.
What failed first
Camarena tested several AI scribe competitors, including Athena's native AI scribe built directly into their existing EHR, but it did not pass—competitor notes were less concise and accurate than Freed's.