I asked ChatGPT to recommend a primary care doctor in Seattle who accepts new patients. It recommended "Cascade Internal Medicine," a small group practice with a modest online presence. The physicians with the highest ratings on Zocdoc and Google, doctors with 200+ reviews and 4.9-star averages, didn't appear. I ran the same query on Perplexity and Gemini. Across all three engines, only one practice overlapped between any two responses. AI search engines are recommending healthcare providers based on signals that have almost nothing to do with traditional online reputation management.
For healthcare providers who have spent years building Google reviews and Zocdoc profiles, this represents a new patient acquisition channel operating on completely different rules.
The Experiment
I asked three AI search engines: "Can you recommend a good primary care doctor in Seattle who's accepting new patients?"
ChatGPT's Response
ChatGPT recommended four providers, mixing individual physicians and group practices. It included brief descriptions of each, emphasizing patient experience and accessibility.
- Cascade Internal Medicine — described as "patient-centered practice with same-week availability"
- Dr. Sarah Westbrook, Ballard Family Health — highlighted for "thorough appointments and preventive care focus"
- Pacific Northwest Primary Care — noted for "integrated approach with mental health screening"
- Capitol Hill Medical Group — described as "LGBTQ-affirming, sliding scale options"
Perplexity's Response
Perplexity gave three recommendations and cited sources. Two citations pointed to health-focused blog posts. One pointed to a Seattle magazine "best doctors" list from 2025.
- Fremont Health Collective — cited from a neighborhood health blog
- Dr. James Nakamura, Queen Anne Family Practice — cited from Seattle Met's annual doctor list
- Pacific Northwest Primary Care — the only overlap with ChatGPT, cited from a patient advocacy blog
Gemini's Response
Gemini recommended five providers with a more clinical tone, emphasizing credentials and specialties.
- University District Family Medicine — noted for "residency teaching practice with evidence-based protocols"
- Greenwood Integrative Health — highlighted "functional medicine approach alongside conventional care"
- Dr. Rachel Okonkwo, Beacon Hill Primary Care — described as "board-certified in both internal medicine and pediatrics"
- Madison Park Medical Associates — noted for "concierge-adjacent model with 30-minute standard appointments"
- Cascade Internal Medicine — overlap with ChatGPT, described as "established group accepting most insurances"
What Google and Zocdoc Show vs. What AI Shows
Google's top results for the same query were dominated by large health systems (Swedish, Providence, UW Medicine) and individual providers with 300+ reviews. Zocdoc's results favored providers with high availability scores and volume metrics.
AI search engines ignored both signals entirely. Not one large health system appeared in any AI recommendation. The AI responses favored independent practices and small groups, providers whose reputations exist in editorial content and community discussions rather than in platform-specific ratings.
What the Recommended Providers Had in Common
Published health content that answered patient questions. Every recommended practice had a blog or resource section with detailed articles addressing common patient concerns. Not generic "5 tips for a healthy heart" content, but specific, locally relevant material: "how to find a doctor accepting new patients on the Eastside," "what to expect at your first integrative medicine appointment in Seattle." AI search engines found these pages because they directly answered the kinds of questions patients ask.
Appeared in editorial "best of" lists or health blogs. The practices that showed up across multiple engines were mentioned in local publications, health-focused blogs, or annual "best doctors" roundups. These editorial mentions serve as third-party validation that AI search engines weight heavily. 85% of AI citations come from third-party sources, and healthcare is no exception.
Had a distinct positioning or specialty. Generic "primary care for everyone" messaging didn't surface. The practices AI recommended each had something specific: integrative medicine, LGBTQ-affirming care, mental health integration, extended appointment times. AI search engines appear to favor providers they can describe with a clear, differentiating attribute, because it makes for a more useful recommendation.
Were discussed in patient communities. Two practices appeared in Reddit threads (r/Seattle, r/SeattleWA) where people asked for doctor recommendations. Online patient communities function as peer referral networks that AI search engines monitor.
What the Missing Providers Lacked
Large system branding without individual differentiation. The major health systems (Swedish, Providence) have massive web presences but their individual providers blend together. When someone asks "recommend a doctor," AI search engines struggle to pick a specific physician from a system with 500+ providers unless that individual has distinct third-party mentions.
Platform-dependent reputation. Providers whose online presence existed only within Zocdoc, Google, or their health system's website had no signal for AI search engines to find externally. High Zocdoc ratings don't translate to AI recommendations because AI engines don't crawl Zocdoc's internal rating system the same way they ingest editorial content.
No content answering specific patient questions. Many provider websites list credentials and accepted insurances but never address the questions patients actually ask AI search engines: "How do I find a doctor who actually listens?" "Best doctor in Seattle for chronic fatigue." Without content structured as direct answers to these queries, AI engines have nothing extractable to cite.
No editorial or community presence. If a provider has never been mentioned in a local health blog, neighborhood newsletter, or online community thread, AI search engines have no third-party signal to rely on. Training data and web retrieval both favor providers with documented external validation.
What Healthcare Providers Should Do
Publish patient-education content that answers specific questions. Write pages addressing the exact queries patients ask AI search engines. "What should I look for in a primary care doctor in Seattle?" "How to switch doctors without losing continuity of care." "Best doctor for [specific condition] in [your city]." Open each page with a direct answer in the first 2-3 sentences. Include your specific approach, credentials relevant to that concern, and what differentiates your practice. Healthcare providers optimizing for AI visibility see results when they address these specific questions.
Get mentioned in local health content. Pitch local magazines' annual "best doctors" lists. Contribute expert quotes to health journalists. Write guest posts for local wellness blogs. Offer to be a source for neighborhood newsletters covering health topics. Each editorial mention creates a third-party signal that AI search engines can cite.
Build a distinct practice identity. "General practice accepting new patients" gives AI engines nothing to recommend you for. Define what makes your practice different: extended appointments, specific population expertise, integrative approach, technology adoption. AI search engines recommend providers they can describe with a concrete differentiator.
Generate reviews across multiple platforms. Spread review requests across Google, Healthgrades, Vitals, Zocdoc, and Yelp. AI search engines look for consensus across sources. A provider with reviews only on one platform has less signal diversity than one spread across several.
Engage with patient communities. Monitor local Reddit threads and Facebook groups where people ask for doctor recommendations. You can't post promotional content, but you can encourage satisfied patients to share their experience when they see recommendation requests. Why Reddit matters for AI search explains the outsized role community discussions play.
How Long It Takes
Weeks 1-4: Publish 4-6 patient education pages answering specific questions. Claim all directory profiles. Identify 3-5 local publications to pitch.
Months 2-3: First appearances in AI responses for specific queries ("integrative doctor Seattle," "doctor accepting new patients Capitol Hill"). Generate 15-20 reviews on non-Google platforms. Secure at least one editorial mention.
Months 3-6: Consistent AI presence for your core specialty queries. Continue publishing monthly content. Maintain review generation. Track which engines recommend you.
The healthcare providers showing up in AI search today started building this presence months ago. But most practices haven't begun, which means the competitive landscape in AI search is far less crowded than on Google. Early movers in healthcare have a meaningful window.
The Loudmink AEO platform tracks how healthcare providers appear across all five major AI search engines and identifies gaps in third-party presence. Plans from $99/mo.
Frequently Asked Questions
Does Zocdoc rating affect AI search recommendations?
Not directly. AI search engines don't crawl Zocdoc's internal rating system the way they ingest editorial content or community discussions. However, if a blog post or article references your Zocdoc rating as evidence of quality, that article itself could become a cited source. The rating matters only when it appears in content that AI engines can retrieve.
Will patients really find doctors through ChatGPT?
Yes, increasingly. ChatGPT has 900 million weekly active users as of 2026. For non-emergency healthcare decisions (finding a new PCP, choosing a specialist, evaluating options), patients are asking AI search engines the same way they'd ask a trusted friend. The difference is that the AI's "opinion" is shaped by whatever third-party content it can find about you.
Should large health systems worry about this?
Large systems have brand recognition but often lack individual provider differentiation in AI search. When someone asks "recommend a doctor," AI engines prefer to name a specific person or practice, not a health system. Systems should ensure individual providers have distinct online presence beyond their system profile page.
Is HIPAA a concern with AI search optimization?
Content strategy for AI visibility is entirely patient-education focused. You're publishing general health information, practice descriptions, and expertise demonstrations. No patient data is involved. The same content guidelines that apply to any healthcare marketing apply here.
Do credentials and board certifications help with AI recommendations?
Credentials serve as authority signals that AI search engines factor into recommendations. But credentials alone aren't sufficient. The engine needs to find your credentials mentioned in a retrievable source (your detailed bio page, a "best doctors" list, a health blog profile) to include them in a recommendation. Having the credentials matters less than having them documented in citable content.