The search journey has changed shape

For years, a patient in pain followed a fairly predictable path: search a symptom, maybe search a provider type, then search "near me." Each step was a separate query, and the patient did most of the synthesis themselves — comparing websites, reading reviews, checking a map.

That journey has compressed. A patient today might type a single, complete question into ChatGPT — "I threw my back out lifting boxes, should I see a chiropractor or just rest it?" — and receive a synthesized answer that includes general guidance and, often, a recommendation to see a specific type of provider. If that same patient is in Boulder or Longmont and mentions their location, or if the AI engine has location context, the answer can move from generic advice to naming actual local practices.

Google's AI Overviews compress the journey differently. A patient typing "chiropractor for sciatica near me" into Google may see a generated summary paragraph sitting above the traditional blue links and map pack, often naming a small number of specific practices or describing what a good match "looks like" before the patient ever scrolls further.

What a typical AI-assisted patient search looks like today

Most patient queries fall into a few recognizable patterns:

  • Symptom-first, provider-second. "Why does my lower back hurt after running" leads into "should I see a chiropractor or physical therapist," which can eventually lead to a location-specific ask.
  • Direct comparison questions. "Chiropractor vs physical therapist for a herniated disc" is a common phrasing, and AI engines generally answer with a comparative explanation before suggesting how to find either type of provider locally.
  • Urgency-driven, local-first questions. "Chiropractor near me open today" or "who can see me for whiplash after a car accident in Longmont" skip the general research phase entirely and go straight to a local recommendation request.
  • Trust-verification questions. Patients sometimes ask an AI engine to sanity-check a provider they already found elsewhere: "is [clinic name] a good chiropractor" or "does this clinic treat sports injuries." This is a different use case — verification rather than discovery — but it still depends on the same underlying signals (reviews, structured data, content) that drive initial discovery.

Each of these patterns rewards a slightly different type of content. Symptom-first questions reward educational, condition-specific pages. Comparison questions reward clear, balanced explanations rather than one-sided marketing copy. Urgency-driven questions reward a complete, accurate, easily crawled Google Business Profile. Verification questions reward a strong and recent review base plus consistent information across the web.

Why AI engines are more selective than traditional search results

A traditional Google results page can show ten organic listings, a map pack of three, and several ads — plenty of room for a patient to see a wide range of options. An AI-generated answer typically names far fewer businesses, often two to four, because the model is trying to give a single coherent recommendation rather than a menu of options.

This selectivity is the core reason GEO matters. Ranking eighth in a traditional Google search still puts a practice on the page and gives it a chance of being clicked. Being the fifth-most-relevant business for an AI-generated answer that only names three practices means being functionally invisible for that query, even though the practice may be a perfectly legitimate, well-reviewed option.

Our research across the Front Range consistently shows this same pattern: a small number of businesses are named repeatedly across many related queries, while most local businesses, including many with solid reputations, are never mentioned by an AI engine at all. The gap is not about quality of care — it is about how legible that quality is to a system that has to make a confident recommendation from structured, verifiable evidence.

What this means for a chiropractic or physical therapy practice

Because AI-assisted patients often start with a symptom or a comparison question rather than a provider name, a practice's best opportunity to enter the research journey early is through content that plainly answers those upstream questions. A page that thoroughly explains "what to expect from your first chiropractic visit after a car accident" is doing double duty: it helps an actual visitor, and it gives an AI engine a specific, well-organized answer to lift when a patient asks that exact question.

Local, urgency-driven questions depend far more on Google Business Profile completeness and accuracy than on the website itself, since that is the data source AI engines most often draw from for "near me" style questions. Verification questions depend on review recency and consistency of information (the same business name, address, and phone number appearing identically everywhere the practice is listed).

Practically, this means a practice does not need to guess which single lever to pull. Symptom-first content, a complete Business Profile, and a healthy review cadence each serve a different stage of the same patient journey, and neglecting any one of them leaves a gap an AI engine will simply route around in favor of a competitor that has covered it.

More on this topic

Curious what ChatGPT, Perplexity, and Google AI Overviews currently say about your practice, if anything at all? Run a free AI Visibility Audit at novasapienlabs.com/audit, or reach out at novasapienlabs.com/contact to talk through what you find.