VoiceAssist Strategic Roadmap - Q1 2025

Executive Summary

With the successful completion of the 10-phase Voice Mode Enhancement and Epic FHIR integration (Phases 6b/7), VoiceAssist is positioned for strategic expansion. This document outlines the prioritized initiatives for Q1 2025 and beyond.

Current State (December 2025)

Completed Capabilities

Key Metrics

• Test Coverage: 95%+ (550+ automated tests)
• Voice Latency: < 500ms first response
• EHR Write Success: > 99%
• Uptime Target: 99.5%

Strategic Initiatives

Priority 1: Platform Expansion

Timeline: Q1 2025 Effort: High

Mobile Application (iOS/Android)

Rationale: Clinicians need bedside access to voice assistant capabilities.

Technical Considerations:

• Offline voice mode with local STT fallback
• Sync protocol for EHR operations when reconnected
• Battery-optimized emotion detection
• Native voice input integration (Siri, Google Assistant)

Implementation Phases:

1. React Native foundation with shared business logic
2. Offline-first data architecture
3. Voice pipeline adaptation for mobile constraints
4. Beta testing with clinical partners

Telemedicine Integration

Rationale: Remote patient care increasingly relies on AI assistance.

Features:

• Video conferencing integration
• Real-time transcription during consultations
• Automatic note generation from conversation
• Patient vitals correlation

Priority 2: Clinical Intelligence Growth

Timeline: Q1-Q2 2025 Effort: High

Specialty Modules

Extend the clinical engine with specialty-specific capabilities:

Care Gap Detection

Implementation:

1. Define preventive care rules (immunizations, screenings, follow-ups)
2. Integrate with patient history from FHIR
3. Proactive alerts during consultations
4. Dashboard for care gap tracking

Technical Components:

• Rules engine extension to clinical_engine/care_gaps.py
• FHIR CarePlan resource integration
• Notification system for overdue items

Clinical Decision Support (CDS)

Features:

• Drug-drug interaction alerts (leverage existing conflict detection)
• Dosing recommendations based on renal/hepatic function
• Guideline-based suggestions with citations
• Integration with UpToDate/OpenEvidence APIs

Priority 3: EHR Enhancements

Timeline: Q2 2025 Effort: Medium-High

Offline EHR Sync

Architecture:

┌─────────────────┐    ┌──────────────┐    ┌─────────────┐
│ Mobile/Desktop  │───▶│ Sync Queue   │───▶│ FHIR Server │
│ (SQLite cache)  │◀───│ (Conflict    │◀───│ (Epic/etc)  │
└─────────────────┘    │  Resolution) │    └─────────────┘
                       └──────────────┘

Conflict Resolution Strategy:

• Last-write-wins for non-critical data
• Manual resolution for medication/order conflicts
• Audit trail for all sync operations

Additional EHR Vendors

Lab Result Trending

Features:

• Automatic trend detection for key biomarkers
• Visual sparklines in voice responses ("Your A1C has improved from 8.2 to 7.4 over 6 months")
• Alert thresholds configurable per patient
• Integration with analytics engine anomaly detection

Priority 4: Voice & AI Advancements

Timeline: Q2 2025 Effort: Medium

Multilingual Support

Target Languages (Priority Order):

1. Spanish (US healthcare demand)
2. Arabic (regional expansion)
3. Mandarin (population coverage)
4. Hindi (emerging market)

Technical Requirements:

• Language detection in language_service.py
• Multilingual medical vocabulary expansion
• TTS voice selection per language
• RTL support for Arabic UI

Emotion & Personalization Refinements

Building on existing emotion engine:

• Baseline Learning Improvements: Extend EMA with cultural sensitivity profiles
• Cross-Modal Fusion: Combine text sentiment with audio prosody for higher accuracy
• Memory Summarization: Emotion-driven summarization of session context

Voice Biometrics

Use Cases:

• Patient authentication for sensitive operations
• Speaker verification during multi-party consultations
• Fraud detection for prescription orders

Privacy Considerations:

• Opt-in only with explicit consent
• Voiceprints stored encrypted, never transmitted
• Local processing where possible

Priority 5: Infrastructure & Privacy

Timeline: Q2-Q3 2025 Effort: Medium

Federated Learning

Rationale: Improve models without centralizing PHI.

Architecture:

• On-device model training for emotion/prosody
• Differential privacy for gradient aggregation
• Central coordinator for model averaging

Edge Deployment

Target Metrics:

• Voice first-response latency: < 200ms
• Offline capability: Full voice assistant functionality
• Model size: < 500MB for edge deployment

Components:

• Quantized STT models (Whisper.cpp)
• Edge-optimized TTS (Piper)
• Local embedding models for KB search

Resource Allocation

Q1 2025 Focus Areas

Success Metrics

Risk Mitigation

Technical Risks

Business Risks

Next Steps

1. Immediate (This Week):

   • Set up mobile development environment
   • Begin Cerner API documentation review
   • Draft care gap detection rules

2. Short-Term (2 Weeks):

   • Mobile app skeleton with auth
   • Prototype Spanish language detection
   • Care gap rules engine design

3. Medium-Term (1 Month):

   • Mobile voice pipeline integration
   • Cerner FHIR adapter skeleton
   • Care gap MVP with 10 rules

Appendix: Technical Dependencies

New Packages Required

{
  "react-native": "^0.73.0",
  "expo": "^50.0.0",
  "@react-native-voice/voice": "^3.0.0",
  "whisper.cpp": "bindings for edge STT",
  "cerner-fhir-sdk": "TBD"
}

API Contracts to Define

• Mobile sync protocol (/api/v2/sync)
• Care gap detection endpoint (/api/clinical/care-gaps)
• Multilingual preference settings (/api/user/language)

Document Owner: Product Team Review Cycle: Monthly Next Review: January 2025