Our Case Studies
AI Recommendation Rate Grew From 18% to 74% in 90 Days
A multi-location orthopedic group engaged AI Health Strategist for a 90-day AI visibility pilot to determine whether AI optimization could become a scalable patient acquisition strategy, not just a visibility exercise. The client is de-identified at its request.
Strong practice, invisible to AI
- Inconsistent AI presence. AI assistants frequently recommended competitors for local orthopedic care.
- Weak entity signals. Physician profiles lacked structured authority signals, and location data conflicted across sources.
- SEO-only content. Service line content was built for search engines, not AI-generated answers.
- No measurement. No reporting existed to track AI visibility or competitive position.
Assess, optimize, measure
- Assessment. AI visibility benchmark, competitive analysis, physician entity assessment, and a Knowledge Graph and location consistency review.
- Optimization. 22 physician profiles enhanced, 3 locations standardized, and 5 service lines optimized with AI-ready content and strengthened entity relationships.
- Executive reporting. AI leadership dashboard, competitive benchmarking, and a quarterly roadmap.
Results after 90 days
| KPI | Before | After 90 Days |
|---|---|---|
| AI Visibility Index | 41 | 82 |
| Physicians recommended by AI | 18% | 74% |
| AI citation frequency | Low | +265% |
| Local search coverage | 52% | 91% |
| Entity authority score | 46 | 84 |
| Competitive ranking | #5 | #2 |
An estimated 136 new patients and a 13:1 return
These patient and revenue figures are modeled, not billed. We start from the measured lift in appointment requests (+24%) and phone inquiries (+16%), apply a conversion rate assumption to estimate incremental new patients (136), and multiply by the client-provided lifetime value per patient ($3,200). That yields an estimated $435,200 in downstream revenue and an estimated 13:1 return versus the pilot. Actual results depend on each organization's conversion and case mix.
AI visibility metrics were independently measured with a dedicated third-party AI visibility platform. Engagement metrics came from the client's website analytics and internal office systems across the 90-day engagement. Patient volume, downstream revenue, and ROI are directional estimates modeled from those measured signals. This pilot was measured before the current AHS standard was in place, so treat it as directional precedent rather than a directly comparable baseline. AHS engagements now apply the AHS standard: 75 patient queries per service line across six AI platforms (ChatGPT, Perplexity, Gemini, Google AI Overviews, Grok, and Claude), for 450 data points per service line per measurement cycle.
