👥 Section 03: User Stories & Problem Scenarios
Objective: Deep dive into patients and caregivers facing overwhelming clinical trial discovery, highlighting pains and how Clinical Trial Navigator transforms their journey.
👤 Primary User Personas
Persona #1: Determined Cancer Patient Sarah
Demographics:
Age: 42-52 | Location: Suburban US (e.g., Midwest) | Occupation: Marketing Manager, mid-size firm | Income: $80K-$120K | Tech Savviness: Medium | Decision Authority: Individual
Background Story: Sarah was diagnosed with stage III breast cancer two years ago. After standard chemo and radiation, she's in remission but anxious about recurrence. She juggles a full-time job, two teens, and weekly check-ups. Her goal is proactive treatment exploration to extend remission. Success means more quality time with family without fear. Daily, she scans doctor emails and forums, but feels lost in medical jargon.
Current Pain Points:
- Overwhelmed by 100s of trials on ClinicalTrials.gov – weekly search yields nothing actionable (frustrating helplessness).
- Eligibility criteria in doctor-speak; misreads disqualifiers, wasting doctor calls (2-3 hrs/month).
- No tracking for new trials; misses updates on her condition (emotional: fear of missing cures).
- Geographic filters absent; drives 4+ hrs to unsuitable sites ($200/gas lost).
- Jargon-filled summaries; can't explain to family (time: 5+ hrs deciphering).
- No notifications; checks manually bi-weekly (anxiety spikes).
Goals: Primary: Find 3+ matching trials/month. Secondary: Understand risks/benefits plainly; track logistics. Emotional: Empowered, hopeful. Metrics: 80%+ match confidence.
Current Solutions: ClinicalTrials.gov (poor UX), Google searches, doctor referrals (incomplete, infrequent). Spends 10 hrs/month, $50 on printouts.
Buying Behavior: Trigger: Post-scan anxiety. Research: App reviews, patient forums. Criteria: Ease-of-use, accuracy, free trial. Budget: $10/month. Barriers: Privacy fears, app overload.
Persona #2: Exhausted Caregiver Mike
Demographics:
Age: 38-45 | Location: Urban (e.g., NYC) | Occupation: Software Engineer | Income: $120K-$160K | Tech Savviness: High | Decision Authority: Family influencer
Background Story: Mike's 8-year-old daughter has a rare genetic disorder (SMA). Nights are spent researching amid work and therapies. He quit side gigs to focus; success is any trial slowing progression. Routines: School drop-offs, PT sessions, forum lurking. Motivated by hope for normalcy.
Current Pain Points:
Goals: Primary: Personalized rare trial matches. Secondary: Logistics planner, notifications. Emotional: Relieved. Metrics: Enroll in 1 trial/year.
Current Solutions: Antidote Match (pharma-biased), Facebook groups. Spends 15 hrs/week, $100/month travel scouts.
Buying Behavior: Trigger: Doctor "no options." Research: Reddit/HackerNews. Criteria: Pediatric focus, integrations. Budget: $15/month. Barriers: Data security for child.
Persona #3: Devoted Daughter Lisa
Demographics:
Age: 55-65 | Location: Rural | Occupation: Retired Teacher | Income: $50K-$70K | Tech Savviness: Low-Medium | Decision Authority: Budget owner
Background Story: Lisa cares for 78yo mom with Alzheimer's. Moved closer; days are doctor visits, meds. Goals: Trials to slow decline. Success: Mom recognizes grandkids longer.
Current Pain Points: (Abbrev for space: Similar to above, elder-focus: mobility barriers, jargon terror.)
Persona #4: Proactive Autoimmune Warrior Alex
Demographics:
Age: 28-38 | Urban | Data Analyst | $90K+ | High Tech | Individual
📖 "Day in the Life" Scenarios (Before Solution)
Scenario #1: Post-Diagnosis Desperation Search
Context: Sarah (Persona #1), Sunday evening, weekly, home laptop.
Current Experience: Sarah's scan showed a spot. Panicked, she Googles 'breast cancer trials near me' at 8PM. Hits ClinicalTrials.gov – 500+ results. Filters barely work; clicks 20, reads dense PDFs. 'Inclusion: ECOG <=2' baffles her (what's ECOG?). Calls doctor voicemail (no reply). Tries Antidote – pharma ads everywhere, no plain explain. By midnight, 4 hrs gone, 1 maybe-trial noted in Notes app. Exhausted, tearful, takes sleeping pill. Outcome: Partial list, high anxiety, no action. Time: 4 hrs. Money: $0 direct, but missed family time.
Pains: Jargon, no personalization, emotional drain.
Scenario #2: Caregiver Midnight Hunt
Scenario #3: Weekly Check-In Chaos
📋 User Stories
🎯 Job-to-be-Done Framework
Job #1: When diagnosed, quickly find matching trials, so I can explore options fast.
Functional: Questionnaire → matches. Emotional: Hopeful. Social: Share with doc. Alternatives: Google. Underserved: Plain lang speed.
📊 Problem Validation Evidence
🗺️ User Journey Friction Points
| Stage | Action | Friction | Emotion | Opportunity |
|---|---|---|---|---|
| Awareness | Google 'trials for cancer' | Overload | Overwhelmed | Targeted ads |
✨ Scenarios with Solution (After State)
Scenario #1: Post-Diagnosis – With Solution
After Experience: Sarah opens app post-scan. Questionnaire: 5 min inputs symptoms/records. Instant matches: 85% score on Trial X, plain: 'You qualify as non-smoker under 60.' Logistics: 45 min drive, $20 Uber. Saves to dashboard, sets alert. 20 min total, confident email to doc. Smiles, plans family dinner. Outcome: 3 trials tracked, actioned.
| Metric | Before | After | Improvement |
|---|---|---|---|
| Time | 4 hrs | 20 min | 92% reduction |
| Frustration | 9/10 | 1/10 | 89% better |
Key Insight: Solution shifts users from desperation to empowerment, validating 80%+ retention potential.