Section 06: MVP Roadmap & Feature Prioritization
MVP Definition & Core Value Proposition
Core Problem Solved: Patients overwhelmed by 450k+ jargon-filled trials on ClinicalTrials.gov, missing life-saving matches.
- User questionnaire for health profile
- AI trial matching with % score
- Plain-language trial summaries
- Basic dashboard to save/track trials
- Location-based filtering
User: 70% complete first match in <5 min.
Business: 100 users/mo1, 30% D7 retention, 8% premium conversion.
Validation: Test matching accuracy >80% via user feedback.
Feature Inventory (35 Total)
Categorized by priority: Core MVP (6), Quick Wins (8), Major Initiatives (8), Nice-to-Haves (13).
| Feature | User Value | Biz Value | Effort | Category |
|---|---|---|---|---|
| User Auth (Email/SSO) Secure login/signup. | H | H | L | Core MVP |
| Health Questionnaire Structured Q&A for conditions/symptoms. | H | H | M | Core MVP |
| AI Trial Matching Fetch from ClinicalTrials.gov, score eligibility. | H | H | M | Core MVP |
| Plain Language Summaries LLM-generated patient briefs. | H | M | M | Core MVP |
| Dashboard (Save Trials) List view of saved trials. | H | H | L | Core MVP |
| Location Filter Radius-based trial search. | H | M | L | Core MVP |
| Email Notifications New matches/status changes. | H | H | L | Quick Win |
| Trial Status Tracker Real-time recruiting updates. | M | M | L | Quick Win |
| Basic Filters (Phase/Comp) Sort by phase, compensation. | M | M | L | Quick Win |
| Share Trial Link One-tap sharing. | M | L | L | Quick Win |
| Premium Upsell Prompt In-app upgrade nudge. | L | H | L | Quick Win |
| FAQ Viewer Common trial questions. | M | L | L | Quick Win |
| Search Bar Keyword trial search. | H | M | M | Quick Win |
| FHIR Health Import Import records for auto-fill. | H | H | H | Major Init. |
| Match Explanation Details Breakdown of qualify/disqualify. | H | M | H | Major Init. |
| Push Notifications Real-time mobile alerts. | H | H | M | Major Init. |
| Calendar Integration Visit scheduling. | M | M | H | Major Init. |
| Trial Comparison Tool Side-by-side views. | H | M | M | Major Init. |
| Logistics Estimator Travel/cost calculator. | H | M | H | Major Init. |
| Contact Facilitator Pre-filled emails to coordinators. | M | H | M | Major Init. |
| Analytics Dashboard User match history. | M | H | H | Major Init. |
| Dark Mode UI theme toggle. | L | L | L | Nice-to-Have |
| ... +23 more Nice-to-Haves (e.g., Offline Sync, Caregiver Mode, B2B Lead Gen) | Nice-to-Have | |||
Value vs. Effort Prioritization Matrix
Low Value
Green=Build First, Blue=Next, Yellow=Opportunistic, Red=Skip
Phased Development Roadmap
Phase 1: Core MVP (Weeks 1-8)
Objective: Launch beta with end-to-end matching flow using ClinicalTrials.gov API + LLM. Unlock instant value: personalized trial discovery in plain language. Prioritize speed via low-code (Supabase, Clerk). Validates core hypothesis: Patients engage if eligibility is simplified (target 80% match accuracy).
| Feature | Priority | Effort | Week |
|---|---|---|---|
| User Auth | P0 | 2d | 1 |
| Health Questionnaire | P0 | 4d | 2 |
| AI Matching + Summaries | P0 | 7d | 3-4 |
| Dashboard + Location Filter | P0 | 5d | 5 |
| Testing/Polish | P0 | 10d | 6-8 |
Phase 2: Product-Market Fit (Weeks 9-16)
Objective: Boost retention with notifications/tracking; add freemium gating. Test PMF via 35% retention. Integrate Stripe for premium. Leverage user feedback to refine AI prompts (improve match score from 80% to 90%).
| Feature | Priority | Effort | Week |
|---|---|---|---|
| Email Notifications | P0 | 3d | 9 |
| Premium Features (Limits/Upsell) | P0 | 5d | 10-11 |
| Trial Status + Filters | P1 | 4d | 12 |
| Feedback System | P1 | 3d | 13-14 |
Phase 3: Growth & Scale (Weeks 17-24)
Objective: Drive acquisition with sharing/referrals; scale AI with caching. Optimize for $3K MRR. Prep B2B leads. Focus viral loops in patient communities.
| Feature | Priority | Effort | Week |
|---|---|---|---|
| Push Notifs + Referrals | P0 | 5d | 17 |
| Trial Comparison | P1 | 6d | 18-19 |
| Analytics + A/B Testing | P1 | 7d | 20-22 |
Phase 4: Expansion (Months 7-12)
Objective: Enterprise features like FHIR/logistics; international trials; B2B white-label. Target $15K MRR, Series A metrics.
Key Features: FHIR Import, Logistics, B2B Dashboard, API.Success Criteria: [ ] 5K users | [ ] $15K MRR | [ ] 3 enterprise pilots.
Feature Prioritization Framework
Priority Score = (User Value × 0.4) + (Biz Value × 0.3) + (Ease × 0.3) | User/Biz:1-10 | Ease:10=easy.
| Rank | Feature | User | Biz | Ease | Score | Phase |
|---|---|---|---|---|---|---|
| 1 | AI Matching | 10 | 10 | 7 | 9.1 | MVP |
| 2 | User Auth | 9 | 10 | 10 | 9.4 | MVP |
| 3 | Dashboard | 9 | 9 | 9 | 8.9 | MVP |
| 4 | Email Notifs | 9 | 9 | 10 | 9.2 | Phase2 |
| 5 | Health Q | 10 | 9 | 7 | 8.8 | MVP |
| 6 | Location Filter | 9 | 8 | 9 | 8.6 | MVP |
| 7 | Premium Upsell | 7 | 10 | 10 | 8.5 | Phase2 |
| 8 | FHIR Import | 9 | 9 | 4 | 7.7 | Phase3 |
| 9 | Push Notifs | 9 | 9 | 6 | 8.1 | Phase3 |
| 10 | Summaries | 10 | 8 | 7 | 8.5 | MVP |
Rules: >7.5=P0 MVP | 6-7.5=P1 | <6=P2+.
Technical Implementation Strategy
| Feature | AI Approach | Tools | Complexity | Cost/User |
|---|---|---|---|---|
| Matching/Summaries | LLM prompts on eligibility | OpenAI GPT-4o | M | $0.15 |
| Explanation | Chain-of-thought parsing | Claude 3.5 Sonnet | L | $0.08 |
| Logistics | Geo + cost estimation | Google Maps API | M | $0.05 |
- Auth: Clerk (5d save)
- DB: Supabase (6d)
- Payments: Stripe (3d)
- Email: Resend (3d)
- Hosting: Vercel PWA (3d)
| Component | Cost |
|---|---|
| AI APIs | $120 |
| Supabase/Vercel | $40 |
| Clerk/Stripe | $30 |
| Total | $190 ($1.90/user) |
Development Timeline & Milestones
W3-4: ░░░░░░░████████░░░░░░ Core AI/Matching
W5-6: ░░░░░░░░░░░░░████████ Dashboard/Polish
W7-8: ░░░░░░░░░░░░░░░░░████ Beta Launch
W9-16: ░░░░░░░░░░░░░░░░░░░██ PMF Features
W17-24:░░░░░░░░░░░░░░░░░░░░ Growth
M2 (W4): [ ] Matching live [ ] UI flow [ ] AI tested
M4 (W8): [ ] 50 betas [ ] Feedback [ ] Bug-free
M6 (W24): [ ] 1K users [ ] $3K MRR [ ] Viral ready
Resource Allocation
| Phase | Team | FTE |
|---|---|---|
| 1 (W1-8) | Founder Eng + Designer PT | 1.25 |
| 2-3 (W9-24) | + Fullstack Eng + Clinical PT | 2.5 |
Skills: React/Next.js (✓✓), Node (✓✓), LLM Prompts (No outsource), DevOps (Yes).
Risk Management
| Risk | Severity | Mitigation | Contingency |
|---|---|---|---|
| Scope Creep | 🟡 | Lock MVP spec; parking lot | Cut P2 features |
| AI Accuracy | 🔴 | Human review loop; prompt iter | Fallback manual filter |
| API Reliability (Trials.gov) | 🟡 | Caching; backup scrapers | Static data fallback |
| Compliance (HIPAA) | 🔴 | SOC2 early; disclaimers | Anon mode only |
| Burnout | 🟡 | Buffers; outsource UI | Extend 2wks |
Launch Strategy
[ ] Waitlist 500 (patient forums)
[ ] Demo video
[ ] PH prep
[ ] 100 staged users
[ ] Surveys/interviews
[ ] Product Hunt top5
[ ] Reddit (r/cancer, rare dz)
[ ] $1K ads
[ ] Weekly cohorts
[ ] 25 interviews
Success Metrics by Phase
| Phase | Metric | Target |
|---|---|---|
| 1 (W8) | Beta users | 50-100 |
| Onboarding | >70% | |
| Match usage | >60% | |
| NPS | >7/10 | |
| 2 (W16) | MAU | 250+ |
| D30 Ret | >35% | |
| Premium | 10+ | |
| NPS | >30 | |
| 3 (W24) | MAU | 1K+ |
| MRR | $3K+ | |
| Viral K | >0.3 | |
| Churn | <7% |