Navigating digital healthcare’s next chapter: Generative optimization

The opacity problem: No one truly knows how these systems decide

Unlike traditional search engines, where SEO best practices emerged from years of testing and Google's published guidelines, generative AI platforms operate as opaque decision-making systems with no publicly accessible logic for how they select brands and content to cite.

The criteria influencing results today may become irrelevant within months as these models are retrained, updated and fundamentally restructured at an unprecedented pace.

Key challenges:

• There is no transparency into the decision-making logic that determines which sources get cited in AI summaries.
• It’s unclear how quickly new or updated content becomes available in generative results, although we do knowmost use some form of traditional search in real-time to augment its response, further increasing the importance of maintaining your current SEO strategy.
• Ranking factors are inferred through observation and testing. Some leading platforms have recently published guidance on how content is selected and displayed. However, how the ranking works is largely undisclosed.
• What works today may be deprioritized tomorrow as models evolve and training data shifts.
• Each platform (generative search overviews, ChatGPT, Gemini, Perplexity, Grok, etc.) operates differently in terms of its underlying logic and priorities.

Training data and partnerships create fundamental biases in results

Testing across different search platforms is key because they all matter. The information sources and content partnerships that train each AI platform create inherent advantages for certain publishers and brands, making it impossible to level the playing field. Each platform's business relationships directly shape what content surfaces in their responses.

• OpenAI/ChatGPT: Partnership with Reddit gives preference to user-generated discussions and community content. News partnerships with outlets like Associated Press and Axel Springer influence journalistic content.
• Gemini: Deep integration with Google's search index, YouTube and internal knowledge graph creates advantages for properties already ranking well organically.
• Grok (X): Heavy reliance on X/Twitter data privileges real-time social conversations and accounts with strong platform presence.
• Perplexity: Aggregates from academic databases, news sources and publisher partnerships, favoring authoritative institutional content and uses a mix of customized open-source models and some combination of the models above.

Feature announcements are accelerating with profound commercial implications

The pace of new feature launches is intensifying, with each announcement potentially reshaping how users discover and engage with healthcare information and brands.

• Stripe and OpenAI integration: Enables direct transactions within ChatGPT, potentially allowing appointment booking without visiting healthcare websites.
• Shopify and OpenAI partnership: Product recommendations and purchases happen entirely within conversational interfaces.
• Google's evolving ad formats: Sponsored placements within AI overviews are being tested, creating new paid opportunities (and costs) for visibility.

There is only more to come as these generative search formats become the dominant entry point. Healthcare marketers must monitor these announcements weekly to identify emerging opportunities and threats.

What this means for your search practice over the next few years

The set-it-and-forget-it era of digital marketing is definitively over.

As things rapidly evolve, healthcare organizations must adopt a posture of continuous adaptation, measurement framework evolution and strategic flexibility that treats generative search optimization as an ongoing practice rather than a completed project.

• True GEO/AEO expertise is in short supply: Healthcare organizations should consider retainer-based relationships for ongoing strategy updates rather than one-time projects. Programs should also build practices internally while supporting and keeping them oriented to rapidly changing practice.
• Bi-monthly practice reviews: Organizations should conduct regular audits of AI citation presence, testing of key queries across platforms and adjustment of content strategy based on evolving results.
• LLM-powered content optimization: Teams can use generative AI tools themselves to analyze and optimize content for AI readability, test different formatting approaches and scale production of citation-worthy content.
• Diversification imperative: No single tactic guarantees visibility, so successful programs will balance GEO with owned channels, first-party relationships and multi-platform presence.
• Expect pay-to-play: Paid search still rules most strategies today. We expect GEO to roll out their version of paid inclusion in some form in the very near future.

New analytics frameworks are essential

Traditional analytics frameworks are blind to AI-driven discovery that occurs outside its tracked channels, requiring new measurement approaches to track citation performance, referral traffic from AI platforms and the patient journey through generative interfaces. Traditional metrics (page views, bounce rate, time on site) become less meaningful.

Shift measurement focus to foundational metrics:

• Establish baseline metrics now before AI traffic becomes more significant—understand current performance to measure future impact
• Shift KPI focus from vanity metrics (e.g. page views, session duration) to conversion-oriented metrics (e.g. appointment requests, form submissions, phone calls, newsletter signups), traced to GEO/AEO changes
• Citation tracking across AI platforms (monitoring when and how your brand appears in generative responses)
• Inbound referral traffic from GEO/AEO: Google, ChatGPT, Perplexity and other AI platforms (requires specialized analytics setup)
• Competition for subject matter authority: "Share of conversation" within AI responses on key healthcare topics
• First-party data capture rates and CRM/CDP growth as proxies for relationship-building beyond search

And advanced attribution and analytics:

• Implement UTM parameters and referral source tracking for inbound traffic standardization across owned channels for a cleaner view as ChatGPT, Perplexity, Claude and other AI platforms improve their use of UTMs in their referrals.
• Set up custom channel groupings in analytics to isolate and analyze AI-sourced traffic separately from traditional search
• Monitor citation frequency by manually testing key healthcare queries across multiple AI platforms bi-weekly or monthly
• Track "dark traffic" (direct visits with no referrer) as potential AI-sourced visits that platforms don't properly attribute
• Consider third-party tools like BrightEdge, Adobe LLM Optimizer or Semrush for automated AI overview monitoring and citation tracking
• Create executive dashboards that compare traditional search performance alongside emerging AI channel metrics
• Monitor and map server log files to identify and classify activity from search and LLM agents to further supplement insights captured (or not) through analytics.

Foundation first: Great content and user experience remain non-negotiable

Whether optimizing for traditional search engines or AI citation, the fundamentals remain the same: High-quality, authoritative content that serves user needs is the baseline requirement. Without this, other optimizations will not get seen, or worse, be ignored.

• Focus on content value first. Content that genuinely helps users will naturally perform better across all discovery channels.
• Create content that directly answers patient or referring physician questions with clarity, depth and medical accuracy.
• Prioritize user experience with fast load times, mobile optimization, intuitive navigation and accessible design.
• Build comprehensive topic coverage that establishes topical authority in strategic and competitively accessible areas, rather than thin, keyword-stuffed pages on all the topics.
• Ensure content demonstrates E-E-A-T (experience, expertise, authoritativeness, trustworthiness) through physician credentials, institutional backing and cited sources.
• Establish an experimentation mindset through building a prioritized hypothesis library of content and structure changes and systemically testing each observe their impact. This would ideally be incorporated into broader experimentation programs you may already have in flight.

Maintain momentum on SEO and SEM technical fundamentals

The technical infrastructure that supports traditional search optimization directly enables AI platforms to crawl, understand and cite your content. Organizations that deprioritize core SEO and SEM practices will struggle with GEO regardless of content quality.

• Continue strategic keyword research and targeting. Understanding what patients search for informs both traditional SEO and the questions AI platforms answer. Long-tail, conversational queries from search are increasingly important.
• Ensure site performance meets Google's Core Web Vitals requirements. Page speed, mobile responsiveness and technical performance affect both organic rankings and AI platform crawling efficiency.
• Implement consistent heading hierarchy (H1–H6 tags). Proper semantic structure helps both search engines and LLMs understand content organization, topic hierarchy and information relationships.
• Maintain XML sitemaps and properly configured robots.txt files. These foundational files invite efficient crawling and indexing by both traditional search bots and AI platform crawlers. Crucially, dates of publication should match between XML sitemaps and public pages.
• Maintain local SEO foundations. Google Business Profile optimization, NAP consistency and local citations remain critical for healthcare practices as local queries increasingly trigger AI responses.

Implement structured data markup across all relevant content types

Schema.org has a long history in terms of validating Google knowledge panel and other aspects of results, but with GEO/AEO its importance shifts into high gear as a major factor that translates your content into AI-friendly formats that AI search can parse, understand and cite with confidence.

This requires front-end and sometimes corollary back-end development investment but delivers compounding returns across both traditional and generative search. Here are the schema formats most important to healthcare:

• How-to schema: Mark up procedural content like "How to prepare for surgery" or "How to manage diabetes" to help AI platforms extract step-by-step guidance.
• FAQ schema: Structure frequently asked questions with explicit question-answer pairs that AI can directly quote in responses.
• Medical web page and health topic content schemas: Healthcare-specific markup signals authoritative medical information.
• Medical organization and hospital schemas: Establish entity relationships and practice information that AI platforms use for local healthcare recommendations.
• Person/physician schemas: Mark up provider credentials, specialties and affiliations to establish individual expertise.
• Article schema with date published and date modified: Timestamp content to signal freshness and establish publication authority.
• Review and aggregate rating schemas: Structure patient testimonials and ratings for AI platforms evaluating provider reputation.
• Event schema: Mark up health seminars, screenings and community events for discovery in conversational queries.
• Requires coordination between front-end developers (template implementation), content teams (proper tagging) and CMS configuration (binding schemas to appropriate content types only)
• Must include ongoing testing via Google's Rich Results Test and Schema Markup Validator to ensure proper implementation
• Use ARIA labeling to help prepare for agentic search: ARIA labels are a best practice for accessibility but are also being used to provide valuable context to agent-assisted search and automation cases.

Restructure content to become citation-worthy in AI summaries

This is the most labor-intensive but potentially highest-impact aspect of optimization: systematically rewriting and reformatting healthcare content to match the structural patterns AI platforms favor when generating responses that cite authorities.

This is multi-phase work requiring content strategy, possibly medical review and ongoing iteration. Of course, these restructured pages must be usable not just for the AI but for real world users, requiring some finesse.

• Add “Too Long; Didn't Read” (TL;DR) summaries at the top of pages: Provide concise, scannable answers immediately that AI can extract as definitive responses (e.g., "A colonoscopy typically takes 30–60 minutes and is performed under sedation to detect colon cancer and polyps") extracted from the text below, cueing up the answers for the AI.
• Increase use of comparison tables: Structure treatment options where appropriate, procedure comparisons or provider differences in tabular formats that AI can easily parse and present.
• Create explicit pros/cons lists: For treatment decisions, healthcare choices or decision selections of other kinds where appropriate. AI platforms love balanced, structured analysis.
• Expand FAQ sections with clear question-answer pairs: Write questions exactly as patients will likely ask them ("Will a colonoscopy hurt?" not "Pain management during colonoscopy").
• Add scannable bullet points for key facts: Break dense paragraphs into bulleted propositions that AI can extract individually.
• Ensure XML sitemap dates align with actual content updates: Keep date modified timestamps current, accurate and align to the date on the visible page so AI platforms understand and trust content freshness.
• Rewrite to directly answer implied questions: Restructure content so the answer to common queries appears explicitly ("The average cost of LASIK surgery is $2,000 to $3,000 per eye" rather than burying pricing deep in paragraphs.)
• Add authoritative elements to signal expertise:

• Physician bylines with credentials prominently displayed, where appropriate
• Citations to medical journals, clinical studies and other authoritative sources
• Quantitative data, statistics and evidence-based claims with sources
• Links to related authoritative content that demonstrates topical depth

• General healthcare information pages
• Topical healthcare (issue-of-the-moment) pages
• Service line pages (procedures, treatments, specialties)
• Condition/disease information pages
• Updates on research and discoveries
• Provider/physician profile pages
• FAQ and population health educational content
• Patient logistics content
• Location and facility information

Treat this work as an ongoing, multi-phase content project rather than a one-time effort. Continuously test what formats generate citations and iterate accordingly.