AI engines like ChatGPT, Gemini, and Perplexity commonly get three things wrong about orthopedic surgery practices: the office location (especially after a move or a new satellite clinic), which procedures a surgeon currently performs, and hospital or health system affiliations that changed after a merger or contract update. These errors happen because AI tools pull from older web pages, directories, and review sites rather than verifying against your current, official information.
Common inaccuracies in AI answers about surgeons
When a prospective patient asks an AI assistant "does Dr. Smith perform knee replacements" or "where is this orthopedic surgeon located," the answer is a synthesis of whatever the AI model found across the web, not a live lookup of your practice's current records. That means the response can blend a five-year-old directory listing with a recent review and a hospital bio page that was never updated, producing an answer that sounds authoritative but is wrong in ways that matter to someone deciding whether to book a consultation.
Outdated locations, procedures, and affiliations
The three most common error categories for elective orthopedic practices are stale addresses, outdated procedure lists, and incorrect hospital or network affiliations. Each one can independently cause a patient to choose a competitor because the AI-generated answer made your practice look inaccessible, unqualified for their specific need, or disconnected from the health system they trust.
Locations drift out of date fast. If a practice relocated, added a satellite office, or closed a branch, older listings and articles referencing the previous address can still rank in the sources an AI model draws from. A patient asking "orthopedic surgeon near me who takes new patients" may get an answer pointing to an address you left behind, along with driving directions that lead nowhere useful.
Procedure lists are just as fragile. Surgeons update their scope over time: adding minimally invasive techniques, dropping procedures they no longer perform, or specializing further into areas like sports medicine or joint revision. If the AI's source material is an old bio page or a directory entry from years ago, it may describe a surgeon as a generalist when they've since become a specialist, or list a procedure that's no longer offered, setting up a mismatched expectation before the first phone call.
Affiliations change with contracts, mergers, and hospital system realignments. A surgeon who moved from one hospital network to another, or whose practice was acquired, may still be listed under the old affiliation across multiple sites. Patients who specifically want a surgeon affiliated with a particular hospital system may skip a practice entirely because the AI-generated answer names the wrong one.
How to audit what AI says about your practice
Auditing what AI engines say about your practice means directly asking ChatGPT, Gemini, and Perplexity a set of patient-style questions and recording exactly what each one returns. This surfaces the specific errors patients are seeing right now, rather than guessing at what might be outdated, and gives you a concrete list to correct rather than a vague sense that "something" might be wrong.
Start by asking each AI tool a handful of questions a real patient would type: "who is your surgeon name, orthopedic surgeon," "where is your practice name located," "does your practice name perform your specific procedure," and "is your surgeon name affiliated with your hospital name." Write down the answer word for word, including any hospital names, addresses, or procedure lists mentioned.
Compare each answer against your current, verified facts: the address patients should actually be given, the procedures currently performed, and the hospital or network affiliation as it stands today. Flag every discrepancy, no matter how small it seems, since even a suite number error can send a patient to the wrong floor of a medical building on the day of a consultation.
Repeat this audit across all three major AI engines rather than just one, because each tool draws from a different mix of sources and may have different errors. A location might be correct in Perplexity's answer but wrong in Gemini's, which tells you the underlying source data is inconsistent across the web rather than uniformly outdated.
Updating the underlying sources
Fixing what AI engines say about your practice requires correcting the original sources they draw from, since these tools don't accept direct edits or corrections the way you might message a directory support team. That means updating your website, your Google Business Profile, hospital bio pages, and major medical directories with the same current address, procedure list, and affiliation, so that the AI models researching your practice increasingly encounter consistent, accurate information.
Begin with your own website, since it's the source most likely to be treated as authoritative. Confirm the address, phone number, procedure list, and affiliation statement are current on every page where they appear, not just the homepage, since AI tools may pull from a bio page, a location page, or an "about" page that hasn't been touched in years.
Next, update your Google Business Profile with the same details. This listing feeds into how Google's AI Overviews describe local businesses, and inconsistencies between your website and your Business Profile can create the exact kind of conflicting signals that lead to mixed-up answers.
Contact any hospital, health system, or medical directory pages that list your practice, since these third-party pages often carry more perceived authority than your own site and can persist in AI training data long after they're outdated on the live web. Request corrections to affiliation, procedures, and location wherever your name appears, including physician-finder tools run by hospital networks.
Finally, review major medical and local directories (health system directories, insurance provider listings, and general business directories) for the same three details. These are common sources AI models cite when answering health-related location questions, so an error left uncorrected in one directory can keep surfacing in AI answers long after your own site is accurate.
Monitoring for future drift
Monitoring for AI drift means periodically re-running the same patient-style questions through ChatGPT, Gemini, and Perplexity to confirm your corrections stuck and to catch any new errors introduced after a move, a new hire, or a change in affiliation. AI answers can shift as the web changes underneath them, so a one-time correction doesn't guarantee accuracy months later.
Set a recurring reminder, tied to a calendar rather than memory, to repeat the audit questions across all three engines. Any time your practice makes a real change, such as adding a new surgeon, opening a location, or shifting hospital affiliation, treat that as a trigger to re-check AI answers within a few weeks, since that's often when stale sources have the most room to cause confusion.
Keep a simple record of what each AI tool said at each check-in. Over time, this record shows whether corrections are holding, whether certain sources keep reintroducing the same error, and whether a particular AI engine consistently lags behind the others in reflecting current information.
When a patient considering elective orthopedic surgery asks their AI assistant which surgeon in the area handles a specific procedure, the assistant answers instantly and confidently, often before the patient has opened a single practice website. If that answer names a competitor, whether because the competitor's information is more current or simply more consistent across the web, the patient may never see your practice as an option at all. The consultation that should have been booked with your office happens somewhere else, and the first sign of it is a slow week with no clear explanation, until the pattern repeats and the cause becomes clear: the AI assistant answered first, and it didn't answer with your name.