How to compare pulmonology practices the way an AI engine does when recommending one

A practice that scores well on all three tends to get named; one that's vague or inconsistent tends to get skipped, even if its actual care quality is excellent.

This matters for a pulmonology practice owner because the comparison isn't happening in a directory anymore. A patient with a new asthma diagnosis, a family member trying to find pulmonary fibrosis specialists, or someone whose primary care doctor mentioned a sleep study, increasingly starts with a conversational query like "best pulmonologist for COPD near me" instead of a search engine results page. Understanding how that answer gets assembled is the first step to influencing it.

How reviews and reputation factor into the ranking

AI engines treat patient reviews as a proxy for trust, but they read them differently than a human scanning stars. The engine looks at what reviews say — whether patients mention specific conditions (asthma, COPD, sleep apnea, pulmonary nodules), specific experiences (wait times, how a diagnosis was explained, staff responsiveness), and whether recent reviews echo older ones or contradict them.

A pulmonology practice with a handful of five-star reviews that only say "great doctor" gives an AI engine little to work with. A practice with reviews that mention "explained my spirometry results clearly" or "helped manage my sleep apnea without a big runaround" gives the engine specific, quotable language that matches how patients actually phrase their questions. Reputation, in this context, isn't just a score — it's a body of text the engine can pattern-match against a searcher's intent.

How service and condition coverage influences the match

AI engines compare practices partly by matching a patient's stated or implied need against the specific conditions and services a practice publishes about itself. A practice that treats pulmonary fibrosis but never states it in plain language on any page the engine can access simply won't surface for that search, regardless of how many patients it actually treats for it.

How location signals affect the recommendation

Location matching for AI-generated recommendations depends on consistency more than proximity alone. The engine checks whether a practice's address, service area, and any affiliated hospital or health system name match across its website, listing profiles, and any other public source the engine draws from. Inconsistent addresses, outdated suite numbers, or a practice that recently moved but hasn't updated every mention of its location create friction the engine resolves by choosing a competitor instead.

For a pulmonology practice with a single location, this is usually straightforward to fix. For a group with multiple clinic sites, or physicians who split time between a main office and a hospital-affiliated clinic, the risk is higher: a patient searching for a pulmonologist in a specific suburb may get pointed to the wrong site, or to a competing practice whose location data is simply cleaner and easier for the engine to confirm.

Auditing your practice against these criteria

Auditing a pulmonology practice against these three criteria means checking review content, service page specificity, and location consistency the same way an AI engine would, before assuming a patient search will resolve in the practice's favor. This is a manual review process, not a one-time fix, since reviews accumulate and directory listings drift out of sync over time.

Start with reviews: read the last several months of patient feedback and note whether any mention specific conditions or procedures by name. If most reviews are generic, consider whether front-desk or follow-up communication invites patients to mention what they were treated for. Next, review every page on the practice website that describes services, and confirm that condition names and procedure names appear in plain text, not only in images or PDFs an engine may not read as easily. Finally, pull up the practice's listings on major directories and search results and compare the address, phone number, and hours against the website. Any mismatch, even a small one like a missing suite number, should be corrected everywhere it appears.

How to check your own progress without waiting on anyone else's report

The most reliable way to track whether these changes are working is to run the same comparison a patient would, on a regular schedule, using your own devices. Open ChatGPT, Gemini, or Perplexity and ask the kind of question a prospective patient would ask: "pulmonologist near your city for sleep apnea" or "best clinic for COPD treatment in your area." Note whether your practice appears, what it's described as treating, and whether the description matches what your practice actually offers.

Do this once a month, and keep a simple log of the query, the date, and what came back. Cross-check the address and phone number the AI engine cites against your current website and directory listings. This kind of direct check takes a few minutes, requires no outside report, and gives a clearer picture of where the practice stands than any dashboard summary could.