Phase 3: Multi-Specialty Expansion

Timeline

12–16 weeks

Investment

$33,000 labor (rate discounted 15%)

$16,000 technical resources (estimated)

Objectives

Expand NLP models to oncology, rheumatology, and behavioral health (or other specialty choice).

Key Deliverables

Multi-Specialty NLP Models

Tuned for oncology/rheumatology notes.

Generalization Testing

Validates performance across specialties.

FHIR Pipeline Optimization

GCP Healthcare API integration.

Sprints

Sprint

1

2

3

Focus

Model tuning

Workflow customization

Compliance audits

Roles

AI Specialist (120hrs), Data Annotation (50hrs)

UI/UX (40hrs), Backend (80hrs)

PM (40hrs), Backend (40hrs)

Investment Breakdown


Human Resources: $33,000

AI Specialist:

Adapt NLP models for oncology/rheumatology

Resolve specialty-specific ambiguities

Backend Developer:

Optimize FHIR pipelines on GCP.

Configure multi-specialty testing environments


Data Annotation:

Label 1,000+ specialty-specific notes

UI/UX Designer:

Customize dashboards for behavioral health workflows

Project Management:

Coordinate compliance audits

Technical Resources: $16,000 (estimate)

Annotated Datasets:

Synthetic data generation ($10,000)

GCP High-Throughput:

Non-HIPAA cloud compute ($3,000/month)

Compliance Audits:

Real-time testing database ($3,000)