Healthcare AI Agents & Video

Healthcare Video Use Cases

Telehealth

• Session documentation: Summarize telehealth visits
• Quality assurance: Ensure documentation completeness
• Patient follow-up: Extract action items and next steps

Clinical Documentation

• Procedure recording: Document surgical and clinical procedures
• Training content: Extract educational segments
• Compliance verification: Ensure protocol adherence

Remote Monitoring

• Patient activity: Monitor patient movement and activities
• Wound assessment: Track healing progress from video
• Physical therapy: Analyze exercise form and compliance

Privacy-First Architecture

Healthcare video is PHI. Architecture must reflect this:

┌─────────────────────────────────────────────────┐
│         Healthcare Video Processing              │
│                                                  │
│  ┌─────────────────────────────────────────┐    │
│  │           Privacy Boundary              │    │
│  │                                         │    │
│  │  Video → [Enhancement] → [Analysis]     │    │
│  │            (BetterVideo)   (Agent)      │    │
│  │              ↓               ↓          │    │
│  │         Zero-retention    On-premise    │    │
│  │                          or BAA-covered │    │
│  └─────────────────────────────────────────┘    │
│                     ↓                           │
│            Structured Output Only               │
│        (No PHI in agent outputs)                │
└─────────────────────────────────────────────────┘

Key Requirements

• BAA with all vendors: BetterVideo provides BAAs
• Zero-retention processing: Video deleted immediately
• Encryption everywhere: TLS in transit, AES at rest
• Audit trails: Document all video access
• De-identified outputs: Agent outputs shouldn't contain PHI when possible

Telehealth Documentation Agent

class TelehealthDocumentationAgent:
    async def document_session(self, session_video: str) -> ClinicalNote:
        # 1. Enhance video for better transcription/analysis
        enhanced = await bettervideo.enhance(session_video)

        # 2. Extract audio and transcribe
        transcript = await self.transcribe(enhanced)

        # 3. Extract key visual observations
        visual_observations = await self.extract_observations(enhanced)

        # 4. Generate structured clinical note
        note = await self.generate_note(
            transcript=transcript,
            observations=visual_observations
        )

        # 5. Verify completeness
        completeness = await self.check_completeness(note)

        return ClinicalNote(
            subjective=note.subjective,
            objective=note.objective,
            assessment=note.assessment,
            plan=note.plan,
            completeness_score=completeness
        )

    async def extract_observations(self, video: str) -> List[str]:
        """Extract clinically relevant visual observations."""
        # Sample frames throughout session
        frames = extract_frames(video, interval=30)  # Every 30 seconds

        observations = []
        for frame in frames:
            obs = await vision_model.analyze(
                frame,
                prompt="Note any clinically relevant observations: "
                      "patient appearance, visible symptoms, mobility, etc."
            )
            if obs:
                observations.append(obs)

        return observations

Compliance Considerations

• HIPAA: Ensure BAAs with all vendors, encryption, access controls
• State laws: Some states have additional requirements
• Consent: Ensure patient consent for video recording and AI analysis
• Retention: Follow medical record retention requirements (separate from processing)

BetterVideo's approach:

• BAA available for healthcare customers
• Zero-retention processing — video isn't stored beyond enhancement
• Never trained on uploads — patient data stays private
• Deletion certificates available on Secure tier