Patients ask AI for health recommendations. AI sends them elsewhere.
Be the healthcare provider AI recommends.
Patients now ask ChatGPT 'best orthopedic surgeon in [city]' or 'top-rated fertility clinic near me.' AI gives confident recommendations based on provider credentials, reviews, and clinical content. If your practice isn't optimized for AI recommendation, patients go to whoever is.
Industry Snapshot
- 29% Of patients ask AI for provider recommendations
- 91% Trust rate for AI healthcare recommendations
- 60 Days to first AI recommendation appearance
The Healthcare AEO Problem
- ChatGPT recommends competitor providers when patients ask about your specialty
- AI models surface hospital aggregator pages instead of your practice
- Your provider credentials and outcomes aren't structured for AI parsing
- Patients are forming provider shortlists in AI before they ever visit your site
How We Solve This
Step 1: Healthcare AI recommendation audit
We test patient-intent queries across ChatGPT, Perplexity, and Gemini for your specialties and markets. Map exactly which providers get recommended and the signals driving those recommendations.
Step 2: Provider entity engineering
We structure your provider credentials, patient outcomes, specialty expertise, and clinical content so AI models confidently recommend your practice. Medical credential markup, outcome data, specialty authority.
Step 3: Recommendation monitoring
Track your AI recommendation status for every specialty and market you serve. Monitor competitor positions. Expand coverage to new conditions and service areas.
Who This Is For
- Health systems that want to be the first provider AI recommends
- Specialty practices losing patients to AI-recommended competitors
- Telehealth platforms competing for AI health discovery traffic
- Healthcare leaders who see AI transforming patient acquisition
Frequently Asked Questions
Is it ethical to optimize healthcare for AI recommendations?
Absolutely. We're not manipulating medical information. We're ensuring AI models have accurate, complete information about your providers and services so patients get better recommendations. Bad optimization would be leaving AI to recommend based on incomplete data.
How do AI models evaluate healthcare providers?
They assess credentials, patient reviews, clinical outcomes, specialty depth, and institutional authority. We optimize all of these signals so AI has high confidence recommending your practice for relevant queries.
Can this work for large health systems with many providers?
Yes. We build systematic optimization for provider directories, specialty departments, and service lines. Each provider and specialty gets optimized for their relevant AI recommendation queries.
How does HIPAA affect healthcare AEO?
We never touch patient data. Our optimization focuses entirely on public-facing content — provider credentials, service descriptions, and clinical expertise signals. Everything is HIPAA-compliant by design.
Can AI recommendations really drive patient acquisition?
They already are. When a patient asks ChatGPT for a specialist recommendation and gets your practice's name, that's a warm lead walking through your door. The trust transfer from AI is powerful.
How do you handle the sensitivity of health-related AI recommendations?
With extreme care. We optimize for factual, credential-based signals — board certifications, clinical outcomes, specialty training. We never manipulate health information. AI models recommend you based on genuine expertise.
What's the typical cost for healthcare AEO?
A fraction of what a single new patient is worth over their lifetime. If your average patient lifetime value is $5,000-30,000+, even a handful of AI-sourced patients per month makes this a no-brainer investment.
Can you optimize for telemedicine and virtual care queries?
Yes. Telehealth queries are exploding in AI search. 'Best online therapy,' 'virtual dermatologist,' 'telehealth for anxiety' — these AI recommendation positions are wide open and incredibly valuable.