Clinical documentation · Production

Abridge AI Platform Reduces Documentation Burden Across 50+ Specialties at WVU Medicine

The problem

WVU Medicine clinicians faced rising documentation burden that contributed to burnout and early retirement risk, threatening rural patient access in communities where there is often no immediate clinician replacement. The health system also relied on approximately $4 million in annual human scribe expenditure.

Workflow diagram · grounded in source
1
Clinical encounter begins
Trigger
Patient visits occur across ambulatory, emergency department, and inpatient care settings.
▾ source quote
“ambulatory visits, emergency department encounters, and inpatient care”
2
AI captures encounter in background
Ai action
Abridge's AI platform operates in the background during the patient-clinician visit.
▾ source quote
“Abridge's enterprise-grade AI platform for clinical conversations also changes the dynamics of the patient visit by operating in the background. Rather than staring at a computer screen, clinicians can maintain eye contact and focus more fully on conversations with patients.”
3
AI drafts clinical documentation
Ai action
AI-drafted clinical documentation supports diverse clinical workflows.
▾ source quote
“AI-drafted clinical documentation to support diverse clinical workflows”
4
Documentation delivered to clinician
Output
Clinical documentation drafted by AI is provided to clinicians while preserving the privacy of patient-clinician interactions.
▾ source quote
“clinical documentation drafted by AI provides another option for supporting clinicians while preserving the privacy of patient-clinician interactions”
Reported outcome

WVU Medicine clinicians reported 77% increased work satisfaction, a 43% increase in ability to accommodate urgent patients, and a 30% reduction in scribe reliance; after-hours documentation fell from 19.1 to 9.2 hours per week and adoption reached approximately 1,500 provisioned users across 50+ specialties.

Reported metrics
Clinician satisfaction at work77%
Ability to accommodate urgent patients43%
In-room scribe reliance30%
After-hours documentation workdecreased from 19.1 hours per week to 9.2 hours
Show all 7 reported metrics
clinician satisfaction at work77%
ability to accommodate urgent patients43%
in-room scribe reliance30%
after-hours documentation workdecreased from 19.1 hours per week to 9.2 hours
annual scribe spend (pre-deployment baseline)approximately $4 million annually
clinicians provisionedapproximately 1,500
clinicians who recorded at least onceover 1,000
Reported stack
Abridge
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Source
https://www.abridge.com/case-study/wvu-medicine
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Frequently asked questions

What did this team achieve with this AI workflow?

WVU Medicine clinicians reported 77% increased work satisfaction, a 43% increase in ability to accommodate urgent patients, and a 30% reduction in scribe reliance; after-hours documentation fell from 19.1 to 9.2 hours…

What tools did this team use?

Abridge.

What results were reported?

Clinician satisfaction at work: 77%; Ability to accommodate urgent patients: 43%; In-room scribe reliance: 30%; After-hours documentation work: decreased from 19.1 hours per week to 9.2 hours (source-reported, not independently verified).

How is this clinical documentation AI workflow structured?

Clinical encounter begins → AI captures encounter in background → AI drafts clinical documentation → Documentation delivered to clinician.

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