When evaluating a generative AI clinical scribe, it's useful to structure the assessment around clinical risk, workflow fit, technical integration, compliance, and financial impact. A good vendor should be able to answer these questions with specific evidence rather than marketing claims.
1. Accuracy and Clinical Quality
Focus on whether the tool reliably produces documentation that clinicians can trust.
Ask:
- How is transcription accuracy measured (e.g., word error rate, clinical concept accuracy)?
- How is note accuracy validated in real clinical settings?
- What specialties has the model been tested in?
- How does performance vary with accents, multiple speakers, telehealth visits, or noisy environments?
- What are the most common documentation errors?
- How often does the system hallucinate diagnoses, medications, procedures, or exam findings?
- Does the AI distinguish between patient statements, clinician observations, and inferred information?
- Can the AI cite or highlight where each section of the note came from in the conversation?
- What percentage of notes require substantial editing by clinicians?
- How are updates to the underlying model validated before deployment?
Evidence to request:
- Peer-reviewed studies
- Independent validation studies
- Specialty-specific performance metrics
- Customer quality metrics
- Error analyses
2. Clinical Workflow
A technically good scribe can still fail if it disrupts clinicians.
Ask:
- How much clinician editing is typically required?
- How long after the encounter is the note available?
- Does it generate:
- HPI
- ROS
- Physical exam
- Assessment & Plan
- Procedure notes
- Discharge instructions
- Can clinicians customize templates?
- Does it learn provider preferences?
- Can multiple clinicians review/edit simultaneously?
- How are corrections incorporated?
Key metric:
Does documentation time actually decrease without increasing review burden?
3. Integration with EHRs
Integration often determines implementation success.
Ask:
- Which EHRs are supported natively?
- Is there certified integration with:
- Epic Systems
- Oracle Health
- MEDITECH
- athenahealth
- eClinicalWorks
- Is the integration via:
- APIs
- FHIR
- SMART on FHIR
- HL7
- Native embedded workflow
- Does the AI write directly into structured fields or only generate free-text notes?
- Can it populate:
- diagnoses
- medications
- orders
- problem list
- ICD-10 codes
- CPT codes
- Does it require copy/paste?
- Does it preserve clinician workflows?
Technical considerations:
- Authentication
- Single sign-on
- Downtime procedures
- Audit logs
- API rate limits
4. Security and Privacy
Healthcare AI should undergo the same scrutiny as other clinical systems.
Ask:
- Is the vendor HIPAA compliant?
- Will they sign a Business Associate Agreement (BAA)?
- Is PHI used to train foundation models?
- Is customer data isolated?
- How long is audio retained?
- Can audio retention be disabled?
- Are recordings encrypted at rest and in transit?
- Where is data stored geographically?
- Can customers delete all recordings?
- What security certifications do they maintain (SOC 2, ISO 27001, etc.)?
- Have they experienced any security incidents?
5. Liability and Risk
One of the most important discussions.
Ask:
- Who is legally responsible if the AI introduces an incorrect statement?
- Does the vendor provide contractual indemnification?
- What liability limits exist?
- Does the malpractice carrier have guidance regarding AI-generated documentation?
- Is every note required to be reviewed and signed?
- Does the system clearly identify AI-generated content?
- Are edits attributable?
- Are all versions retained?
Important distinction:
The clinician generally remains responsible for the final signed documentation, regardless of AI assistance.
6. Regulatory Considerations
Ask:
- Is the product considered clinical decision support or documentation assistance?
- Has the vendor discussed regulatory positioning with the FDA where applicable?
- What quality management processes govern model updates?
- How are software changes documented?
- Is there change control for major model releases?
7. Reimbursement and Revenue Cycle
Most AI scribes do not directly increase reimbursement, but they may improve documentation quality and coding completeness.
Ask:
- Does the tool support more complete documentation for E/M coding?
- Does it identify documentation gaps?
- Can it suggest missing elements without auto-inserting unsupported findings?
- Does it assist with ICD-10 or CPT coding?
- Has the vendor demonstrated improvements in:
- coding accuracy
- documentation completeness
- reduced claim denials
- improved charge capture
- Are any reimbursement claims independently validated?
Request evidence rather than anecdotal ROI claims.
8. Financial ROI
Ask:
- Average reduction in documentation time?
- Reduction in after-hours charting ("pajama time")?
- Improvement in clinician satisfaction?
- Impact on burnout?
- Additional patients seen per day?
- Reduction in human scribe costs?
- Licensing model:
- per clinician
- per encounter
- enterprise
- Implementation costs
- Integration costs
- Ongoing support costs
9. Model Governance
Ask:
- Which foundation model(s) are used?
- Can the vendor swap models without customer approval?
- How frequently are models updated?
- Can customers delay upgrades?
- Is there rollback capability?
- How is model drift monitored?
10. Operational Support
Ask:
- Implementation timeline?
- Required IT resources?
- User training?
- Clinical onboarding?
- Support SLAs?
- Dedicated customer success?
- Downtime procedures?
- Business continuity plans?
11. Metrics to Monitor After Go-Live
Agree on success measures before implementation, such as:
- Documentation time per encounter
- Same-day chart closure rate
- Clinician satisfaction
- Note edit rate
- Hallucination/error rate
- Coding accuracy
- Documentation completeness
- Patient throughput
- Revenue per encounter (where appropriate)
- Patient satisfaction
- Burnout scores
High-priority "must-answer" questions
If you have limited time with a vendor, prioritize these:
- What independent evidence demonstrates clinical note accuracy in our specialties?
- How often do clinicians materially edit AI-generated notes?
- How are hallucinations detected and prevented?
- How does the product integrate with our EHR, and what data can it write back?
- Who bears liability if AI-generated documentation contributes to a clinical error?
- Is patient data ever used to train models?
- What measurable improvements have customers seen in documentation time, chart closure, coding quality, and clinician satisfaction?
- What governance process exists for model updates, validation, and rollback?
These questions tend to separate mature, enterprise-ready AI scribe vendors from products that are still primarily demonstrating technical capability without robust clinical, operational, and governance practices.
