Built on 5 years of proprietary data from working closely with 250+ doctors and thousands of patients. Sophie predicts where patients get confused, designs the optimal journey, and accelerates treatment starts.
Patient confusion not clinical failure is the #1 driver of treatment abandonment in specialty pharma. The traditional support model is broken.
never filled or abandoned after the first prescription. That's nearly half your patients lost before they even start.
go unanswered. Patients see unknown numbers and ignore them. Call centers were designed for a different era.
from scratch using traditional agencies. By the time you launch, you've already lost the patients who needed help most.
For a $500M specialty brand, patient confusion and treatment abandonment can mean $20–25M in lost revenue per year, per brand. Even a 10% improvement in adherence recovers $50M annually.
Sophie isn't ChatGPT. She's a purpose-built AI trained on 5+ years of real patient confusion data built from hands-on experience working side-by-side with 250+ doctors and thousands of patients to understand exactly how patients move forward (or don't) with complex therapies.
Those years of close collaboration observing real exam rooms, real hesitations, real breakthroughs are baked into every recommendation Sophie makes. No competitor has this data. No generic AI tool can replicate it.
Identifies the exact confusion points your patients will face scored by severity, emotional weight, and risk of abandonment before you launch.
Maps the optimal patient journey using the See-Think-Do-Care framework, placing the right content at each stage, channel, and emotional state.
Drives faster treatment starts and higher long-term adherence through physician-led video education delivered via SMS at 98% open rates.
Sophie learns from two sources: our own proprietary engagement data from working with doctors and patients, and the broader external landscape of patient behavior and pharma regulations.
Sophie takes your product label, patient population data, and therapy complexity and produces a complete, actionable patient education strategy.
Sophie analyzes your drug label and patient population to identify the top confusion vectors scored by severity and emotional impact. She scans CMS data, behavioral science models, and real patient forum sentiment to build a complete confusion map.
Maps the complete patient journey using the See-Think-Do-Care framework, identifying the right content for each stage, channel, and emotional state. Every touchpoint is designed to address a specific barrier at a specific moment.
Generate MLR-Ready Content
AI generates physician-led video scripts, SMS sequences, email flows, and portal content all pre-screened for MLR compliance using our OPDP warning letter database and fair balance engine.
Launch across SMS, video, and email. Real-time engagement data feeds back into Sophie, driving continuous improvement of content, cadence, and messaging making every touchpoint smarter over time.
Sophie scans your product label, patient journey, and support ecosystem then delivers a scored analysis of exactly where patients get confused and what to do about it.
| Confusion Vector | Severity | Risk Level | Recommended Action |
|---|---|---|---|
| Self-injection anxiety & technique confusion | 9.2 / 10 | CRITICAL | Physician-led video demo within 24hrs of Rx |
| Side effect fear (immunosuppression) | 8.5 / 10 | HIGH | Proactive SMS series addressing top 5 fears |
| Insurance / prior auth navigation | 7.8 / 10 | HIGH | Step-by-step access guide + hub warm transfer |
| Dosing schedule confusion | 7.1 / 10 | MODERATE | Visual dosing calendar via SMS at Day 1, 7, 30 |
| Lifestyle impact uncertainty | 6.4 / 10 | MODERATE | Patient story video: life on therapy |
Every touchpoint is designed by Sophie based on real confusion data delivered via physician-led video, SMS, and email at the exact moment patients need guidance.
Sophie is trained on years of real patient confusion data gathered through hands-on work with doctors and patients in clinics across the country. No competitor has this data.
Proprietary dataset of patient confusion patterns across oncology, rare disease, immunology, and neurology built one clinic at a time.
Video watch rates, SMS response patterns, rewatch behavior, and time-to-fill correlations from real campaigns with real patients.
Content performance data by specialty, condition, and patient demographics what works, what doesn't, and why.
Trained on FDA guidelines, OPDP warning letters, and pharma regulatory precedent for pre-cleared content generation.
Real results from real patient engagement programs not projections.
Physician-led SMS video vs. traditional outreach methods
Patients who engage with Hoot content vs. control group
Client name withheld for privacy: patients re-engaged via Hoot
Whether you're evaluating a single brand or transforming your entire patient support portfolio.
Every day a patient is confused is a day they're not on therapy. Sophie changes that with AI built specifically for pharma.
Schedule a Demo →30-minute call · No commitment · See a live demo of your therapy's journey
