HIPAA-compliant platform using NLP to match anonymized EHR data with clinical trials. Targets pharma companies to accelerate patient recruitment while reducing manual screening by 60-80%.
MediTrial Connect addresses the critical bottleneck in pharmaceutical development: patient recruitment for clinical trials. By applying advanced NLP algorithms to anonymized electronic health records, the platform automatically identifies eligible patients while maintaining HIPAA/GDPR compliance. This solution could reduce recruitment timelines by 40-60% for research organizations and hospitals running Phase 2/3 trials.
Core functionality
HIPAA-compliant platform using NLP to analyze anonymized EHR data against clinical trial criteria. Key features include:
- AI-powered patient-trial matching engine with probability scoring
- Researcher dashboard showing real-time match analytics
- Automated pre-screening alerts to healthcare providers
- Blockchain-based audit trail for compliance tracking
- API integrations with major EHR systems like Epic and Cerner
Target user and segment
Primary customers are clinical research organizations (CROs) and pharmaceutical trial managers conducting Phase 2/3 trials. Focused on oncology, rare diseases, and chronic conditions in US/EU markets where trial delays cost up to $8M/day.
Recommended tech stack
- Backend: Python/Django framework
- NLP: spaCy with BERT-based models
- Database: PostgreSQL with pgAnonymizer extension
- Cloud: AWS HIPAA-compliant infrastructure
- Frontend: React.js with TensorFlow.js
- Security: AES-256 encryption + zero-knowledge proofs
Estimated MVP hours and costs
Total development: 920 hours at €100/hour = €92,000
- NLP engine: 240h (€24,000)
- EHR integration framework: 180h (€18,000)
- Researcher dashboard: 200h (€20,000)
- Compliance/audit systems: 180h (€18,000)
- Security architecture: 120h (€12,000)
SWOT-analysis
Strengths: 60-80% manual screening reduction, proprietary NLP algorithms, regulatory compliance by design
Weaknesses: Hospital EHR integration dependencies, high regulatory barriers
Opportunities: $46B clinical trials market growing at 5.8% CAGR, emerging market expansion
Threats: Competition from Medidata/IBM, evolving privacy regulations
First 1000 customers strategy
Acquisition channels:
- Direct sales to top 20 CROs (70% target)
- Medical conference sponsorships (Bio-IT World)
- Co-marketing with EHR vendors
- LinkedIn ABM campaigns for trial managers
€1,200 CAC per enterprise client with 3% conversion from free tier. Initial focus on academic hospital research departments.
Monetization
Business model: Tiered SaaS pricing
– Starter: €5k/trial
– Pro: €15k/annual + €50/match
– Enterprise: Custom pricing
Break-even: €250k ARR (7 Enterprise or 22 Pro clients)
Core team: 6 FTE (2 NLP engineers, 2 full-stack devs, 1 compliance officer, 1 biz dev)
Market positioning and competitors
Regional markets: North America ($22.3B), Europe ($14.7B), APAC ($9B)
Competitors: Medidata Patient Cloud (limited AI), IBM Watson (high-cost), Antidote (manual-heavy)
Differentiation: Real-time EHR analysis via API-first architecture
Sales strategy: Land-and-expand through academic hospitals before targeting pharma partners
