10 Best Ways Doctors Are Using AI for Clinical Documentation in 2024

1. Real-Time Ambient Capture During Patient Visits

The most common use case: clinicians launch Ara or similar tools at the start of a visit and let it record continuously. The AI ingests audio, separates patient from provider, and generates a structured summary within seconds of the appointment ending.

Doctors simply glance at the note, flag any errors, and sign. No re-reading, no manual dictation.

• Providers save 6-8 hours weekly on documentation.
• Turnaround is faster than human scribes.
• Enables heads-up, patient-focused conversation.

2. Telehealth Documentation Without Extra Participants

Virtual visits expose the awkwardness of remote scribes joining calls. Smart clinicians now pipe telehealth audio directly into Ara, eliminating the need for a third participant and simplifying consent workflows.

• Patients stay comfortable (no scribe on the call).
• Notes auto-generate as the visit happens.
• Works across Zoom, Doxy, Amwell, and other platforms.

3. Post-Visit Summaries for Shared Medical Appointments

Group visits, family meetings, and care huddles involve multiple speakers. AI tools with speaker separation allow clinicians to quickly pinpoint action items per participant and assign follow-ups without hours of manual note review.

• Speaker tagging prevents misattribution.
• Action items surface automatically.
• Care coordinators save time on assignment.

4. Specialty Clinic Workflows (Cardiology, Ortho, OB)

Specialty practices use ambient AI as a foundation and layer on templates afterward. A cardiologist's visit gets a generic summary, then downstream staff slot in EF percentages, medication adjustments, and follow-up imaging orders.

This hybrid approach keeps providers flexible while maintaining clinic-specific structure.

• Templates stay light and reusable.
• Providers are not locked into rigid forms.
• Downstream teams save time on data entry.

5. RPM Nurse Call Documentation

Remote patient monitoring programs rely on nurse-patient calls to gather vitals and adherence data. AI documentation captures the call, generates a note, and posts it to both the EHR and RPM platform without manual uploads.

Supervising physicians then audit vitals alongside narrative context in one place.

• Eliminates manual nurse charting.
• Improves billing accuracy for RPM codes.
• Audit trails are automatic.

6. Asynchronous Care Team Handoffs

Large teams use AI transcripts as structured handoff documents. Nursing summarizes a patient call, the AI produces a SOAP note, and the covering physician reads a clean summary instead of scrolling through free-text notes.

Consistency improves, and clinician-to-clinician communication becomes more reliable.

• Structured handoffs reduce miscommunication.
• On-call physicians spend less time in history.
• Safety improves because context is preserved.

7. Quality Improvement and Compliance Audits

Medical directors use AI-generated transcripts to audit clinical practice patterns. Instead of reading free-text notes written by multiple providers, they analyze structured data tagged with diagnoses, meds, and plan items.

This allows easier correlation with outcomes and identifies coaching opportunities.

• Compliance reviews are faster.
• Coaching is data-driven instead of impression-based.
• Variance analysis becomes systematic.

8. Patient Engagement and After-Visit Summaries

AI transcripts generate patient-facing summaries automatically. Clinics send these to patients via patient portals, improving engagement and health literacy without extra staff effort.

• Patient engagement metrics improve.
• Recall and adherence go up.
• Support staff time drops.

9. Billing and Coding Accuracy

Coders use AI-structured notes with tagged diagnoses, procedures, and medical decision-making to assign codes with higher confidence. This reduces coding errors and leaves fewer notes for physician review.

Revenue capture improves because documentation explicitly supports each billed service.

• Fewer denied claims due to missing documentation.
• Coding time drops by 20-30%.
• Audit rates fall as quality improves.

10. Multi-Language Support and Accessibility

Clinics serving diverse populations use AI transcription that supports Spanish, Mandarin, Vietnamese, and other languages. This eliminates the need for in-visit interpreters for documentation and levels the playing field for non-English-speaking providers.

• Reduces reliance on professional interpreters.
• Expands care capacity in underserved areas.
• Improves documentation for non-native English speakers.