Healthcare Startup Launch: A Case Study
AppUnstuck Team
Your Partner in Progress
The Challenge
Dr. Lisa Chen had a vision: a telehealth platform for video consultations, medical records, and prescriptions. She spent 4 months building it on Replit with AI code generators. The app worked beautifully in development.
But when it came time to launch, she hit a wall. Every deployment attempt failed with cryptic errors. The app would work for a few hours, then crash. Patient data was not persisting correctly. Her launch date was just 3 weeks away.
Lisa was stuck. She had invested $15,000 and countless evenings into this project. She had 200 patients on a waiting list. But she could not ship a broken product in healthcare. The stakes were too high.
The Partnership
When Lisa reached out, she was exhausted from debugging AI apps. She had tried re-deploying, posting on forums, and even hiring a freelancer who disappeared. We started with a 30-minute call to understand her blockers.
In our initial audit, we discovered the core issues:
- Database connections were timing out in production.
- Environment variables were not properly configured.
- The video call feature relied on a Replit-specific API.
- No error logging, so failures were invisible.
We laid out a clear 3-week plan to improve the AI system design and ensure AI reliability:
- Week 1: Stabilize the database and fix deployment configuration.
- Week 2: Replace Replit-specific features with production-ready alternatives.
- Week 3: Testing, security hardening, and launch preparation.
Lisa appreciated the transparency. She knew exactly what we would work on each week. We are proud of this transparent approach at AppUnstuck.
The Solution
We tackled the blockers one by one, keeping Lisa in the loop.
Week 1: Getting the Foundation Stable
We migrated the database to a managed PostgreSQL instance. We set up proper environment variable management and configured the deployment pipeline. Lisa could finally deploy without everything breaking.
Week 2: Making It Production-Ready
The video call feature was the trickiest part. We replaced it with a HIPAA-compliant video solution. This is a critical step, as explained in resources like the HIPAA Journal's compliance guide. We also added proper error handling.
Week 3: Polish and Launch
We added monitoring so Lisa could see if anything went wrong after launch. We conducted security testing to ensure patient data was protected. We helped Lisa test the entire patient flow. By day 18, everything was ready.
The Results
Lisa launched on schedule. In the first week, 150 patients signed up. By week three, she had over 500 active users booking consultations.
Here is what changed:
Business Impact:
- ✅ Launched on time after being stuck for 2 months
- ✅ 500+ users in first 3 weeks
- ✅ $12,000 in revenue (first month)
- ✅ Zero downtime since launch
- ✅ HIPAA-compliant and secure
Peace of Mind:
- ✅ Can deploy updates confidently
- ✅ Knows immediately if something breaks
- ✅ Spends time on patients, not debugging
"I was terrified I'd have to abandon the whole project. AppUnstuck didn't just fix the technical issues—they helped me understand what was wrong. Now I can focus on my patients instead of panicking about the app crashing. We launched on time, and it's been rock solid ever since."
— Dr. Lisa Chen, Founder
Three months later, Lisa has over 2,000 patients. She has even started building new features, confident that changes will not break everything.
Key Takeaways
Warning signs you might be stuck:
- You have postponed your launch date multiple times.
- It works in development but fails in production.
- You do not have proper error logging or monitoring.
- You are afraid to make changes because something might break.
When to get help:
If you are more than a month past your planned launch date, do not wait. Every week of delay costs you users and revenue. A professional audit can identify blockers in hours that might take you weeks to find.
Get Unstuck
Stuck in a similar situation? We would love to help you get moving again. Start with a free 30-minute audit where we will identify your blockers and map out a clear path forward.
This case study represents a common scenario we see with AI-generated healthcare apps. Client details have been modified to protect privacy.