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Content Summary
The post warns against non-healthcare developers rushing to build AI healthcare apps without understanding the industry's complexity. It emphasizes that healthcare isn't like typical SaaS - regulations like HIPAA, clinical workflows, and patient safety make it uniquely challenging. The author criticizes "ChatGPT wrappers" and weekend-built telemedicine platforms as dangerous, advocating instead for deep healthcare knowledge, clinician shadowing, patient interviews, and proper compliance before building anything touching patient data. Comments reveal mixed experiences: some succeeded with self-hosted HIPAA-compliant apps, others failed due to compliance complexity, while existing EHR software is universally criticized as terrible.
Opinion Analysis
Mainstream View: The dominant opinion aligns with the OP - healthcare AI requires deep domain expertise and cannot be approached like typical SaaS. Most agree that compliance (HIPAA) is complex and critical, and that existing healthcare software is terrible.
Conflicting Opinions: There's debate about compliance difficulty - some (like gthing) claim "HIPAA isn't that complicated" with proper legal help, while others (arkatron5000) found it overwhelming enough to abandon their project. There's also disagreement about Specode's effectiveness, with one commenter claiming it's not actually HIPAA compliant.
Different Perspectives:
- Healthcare professionals turned developers (Bunnylove3047) see opportunity due to their unique background
- Some believe startups aren't the right vehicle for healthcare innovation beyond simple consumer apps
- Others argue enforcement is lax until scale/data breaches occur, creating false sense of security
- There's tension between startup speed demands and the methodical approach healthcare requires
SAAS TOOLS
| SaaS | URL | Category | Features/Notes |
|---|---|---|---|
| Specode | Not provided | Healthcare backend | Handles healthcare regulations/compliance for telehealth apps |
| EHR software | Not provided | Electronic Health Records | Mentioned as "abysmal" and hard to use |
| CGM apps | Not provided | Consumer health | Apps that interface with continuous glucose monitors, usually by device manufacturer |
USER NEEDS
Pain Points:
- Developers with zero healthcare experience building medical AI tools
- HIPAA compliance complexity beyond simple encryption
- Clinical workflows misunderstood by non-healthcare builders
- Terrible existing EHR software that doesn't fit medical workflows
- Start-ups rushing AI experiments that put patient data at risk
- Lack of enforcement of HIPAA until data leaks or large scale
Problems to Solve:
- Build thoughtful healthcare solutions that understand both tech and medicine
- Replace broken EHR systems with usable software
- Create secure, compliant telehealth platforms
- Bridge gap between consumer health apps and clinical tools
- Ensure patient data security while improving care
Potential Solutions:
- Shadow clinicians to understand real workflows
- Learn actual HIPAA requirements thoroughly
- Talk directly to patients about real needs
- Use specialized healthcare compliance platforms (like Specode)
- Self-hosted HIPAA compliant applications with proper BAAs
- Focus on specific use cases like CGM integration rather than broad platforms
GROWTH FACTORS
Effective Strategies:
- Deep domain expertise (healthcare + tech) before building
- Compliance-first approach rather than retrofitting
- Direct clinician shadowing and patient interviews
- Self-hosted solutions to maintain full control over data
Marketing & Acquisition:
- Targeting specific healthcare niches (e.g., CGM integration)
- Building trust through demonstrated compliance
- Word-of-mouth among healthcare professionals frustrated with existing tools
Monetization & Product:
- Focus on solving specific, painful problems rather than broad "revolutionary" claims
- Premium pricing justified by compliance and specialized knowledge
- B2B model targeting practices rather than direct consumer apps
User Engagement:
- Building for actual clinical workflows rather than generic patterns
- Creating solutions that reduce clinician burnout from bad software
- Community building among healthcare professionals sharing similar frustrations