A family in Dhaka or Kathmandu weighing cardiac surgery in Chennai, Kolkata or Delhi now often starts by asking ChatGPT or Gemini which hospital to choose. For Indian hospital chains, AI visibility across Asia decides whether your name appears in that first shortlist, and whether the answer gets your cities, specialities and accreditation right. Many chains find that a rival, a facilitator portal or a hospital in Malaysia or Thailand is named instead.
This article is for international patient, digital and brand leads at multi-city Indian hospital groups. It explains why the gap happens, the prompts cross-border patients actually type, how to audit your position this week, and how to fix it without breaking the National Medical Commission's advertising rules.
Why AI assistants skip or misdescribe Indian hospital chains
AI assistants describe a hospital chain using whatever they can retrieve and reconcile about it, so a chain whose facts are fragmented gets a fragmented answer. The usual causes are structural, not a lack of clinical reputation.
Google's documentation on AI features says AI Overviews and AI Mode may use "query fan-out", issuing several related searches across subtopics to build a response. A prompt like "best hospital in India for liver transplant for a patient from Bangladesh" fans out into searches about the procedure, the cities, visas and costs. Your chain needs a clear, indexed page for each of those subtopics, or another source fills the gap. Google also says there are no special requirements for AI features beyond being indexed and eligible for a snippet, so ordinary crawlability problems carry straight through.
ChatGPT search works through OpenAI's crawler. OpenAI's crawler documentation says OAI-SearchBot is used to surface websites in ChatGPT's search features, and that sites which opt out will not appear in ChatGPT search answers. Some hospital IT teams block every AI bot by default, which quietly removes the chain from those answers.
Chains have specific weaknesses that make this worse:
- City microsites and separate domains. One hospital sits on the group domain, another on a legacy domain from an acquisition, a third on a city subdomain. AI systems may treat them as unrelated organisations.
- Old and new brand names. After a merger or rebrand, press coverage and directories keep the old name for years. Answers may mix the two or describe a hospital under a name it no longer uses.
- Doctors listed three times. The same consultant appears on the group site, the hospital site and a booking platform, with different titles and departments.
- Facilitator portals with stronger pages. Medical travel aggregators often publish tidy "top hospitals in India for X" pages. If your own pages are thinner, the portal becomes the cited source and its wording becomes your description.
What patients across Asia actually ask AI
Cross-border patients ask about cities, procedures, visas and trust before they ask about any single hospital. These prompts are typical of what international desks hear and what patients type, grouped by stage.
Early research
- "Which Indian city is best for heart bypass surgery for a patient from Bangladesh?"
- "চেন্নাইতে বাইপাস সার্জারির জন্য কোন হাসপাতাল ভালো?" (Bengali: "Which hospital in Chennai is good for bypass surgery?")
- "दिल्लीमा क्यान्सर उपचारका लागि राम्रो अस्पताल कुन हो?" (Nepali: "Which is a good hospital in Delhi for cancer treatment?")
Comparing options
- "Kidney transplant in India or Malaysia, which is better for a Sri Lankan patient?"
- "Is [chain name] Kolkata NABH accredited, and does it have a paediatric cardiac unit?"
- "Which hospitals in Hyderabad have international patient services for patients from the Maldives?"
Ready to act
- "How do I get an Indian medical visa letter from [hospital name]?"
- "Can two family members travel with me on a medical attendant visa to India?"
Notice how many prompts need practical, factual answers. The Government of India's e-Visa page lists e-Medical and e-Medical Attendant visas, says the e-Medical visa requires a letter from the hospital in India on its letterhead, and says only two e-Medical Attendant visas are granted against one e-Medical visa. A hospital that explains its own letter process clearly gives AI something accurate to quote.
How to check where you stand
You can run a useful baseline audit in a week with a spreadsheet and a fixed prompt list. Our how we measure page explains the method we use for repeatable testing.
Step 1: Build a prompt set by country and speciality
Take your top five specialities for international patients and your top four source countries. Write two or three prompts per pair, in English and in the language patients use. Keep the wording fixed so you can repeat it next month.
Step 2: Run each prompt several times across engines
Run each prompt in ChatGPT, Gemini, Perplexity, Copilot and Google AI Overviews, at least three times each. Answers vary between runs, users and locations, so a single run proves little. Record whether your chain is named, which hospital in the chain, and which sources are cited.
Step 3: Check every fact the answers state
For each mention, check the hospital name, city, departments, accreditation, contact route and visa guidance against your own records. Mark each answer accurate, partly wrong or wrong. Incorrect accreditation and outdated names are the errors with the most compliance risk.
Step 4: List the sources that win
Collect the URLs the engines cite. Group them: your own pages, facilitator portals, news, directories, competitors. This tells you whether you have a content problem (your pages are missing) or an authority problem (others are trusted more).
Step 5: Check crawler access and indexing
Ask IT for the robots.txt rules on every domain the chain uses. Check that OAI-SearchBot and Googlebot can reach the international patient pages, and that those pages are indexed, not hidden behind enquiry forms or scripts.
How to fix it
The fix is to make one accurate, factual version of the chain easy to find and repeat everywhere. Conveniently, this is also what Indian advertising rules now expect.
Work inside the NMC's October 2026 guidelines
The NMC issued guidelines on ethical advertising and public communication for doctors, hospitals and medical institutions in October 2026. Medical Dialogues' report on the guidelines says clinical establishments may advertise only factual, objective and verifiable particulars such as name, location, departments, facilities, equipment, accreditation status and charges. It also reports prohibitions on testimonials and patient stories for promotion, advertised surgical results, unverifiable superiority claims such as "best" or "100% success", fake reviews or manipulated rankings, commissions tied to patient procurement, and AI-generated content that creates misleading claims or synthetic endorsements.
Read the notice itself with your legal team before changing anything. The practical point for AI visibility is that the content you are allowed to publish, verifiable facts, is exactly the content AI systems can quote without distortion. Strip "No.1" language and outcome boasts from international pages, and review any facilitator arrangements that pay per referred patient.
Build one international patient hub for the whole chain
Create a single hub on the group domain that lists every hospital, its city, its main departments for international patients and the international desk contact for each. Use the same hospital names everywhere, including any former names stated once ("formerly known as..."). Link from each city site back to this hub and from the hub to each hospital page.
Give each hospital and centre a page AI can quote
Each hospital needs a page that opens with a plain summary: what it is, where it is, which departments it offers, its accreditation status with the date it was verified, and how international patients get in touch. Each centre, such as a transplant or oncology unit, needs its own page with the procedures offered, equipment and the consultants who lead it. Write the first paragraph so it can stand alone as an answer.
Make accreditation verifiable
AI answers often state accreditation, and get it wrong when chains describe it loosely. NABH runs a public search of accredited healthcare organisations and a certificate verification tool. Name the exact accredited entity, link to the verification route, and remove accreditation claims for hospitals whose accreditation has lapsed. Apply the same discipline to any international accreditation you mention.
Explain the visa and arrival process in your own words
Publish a page per hospital on how to request the visa letter, what documents the desk needs, typical response time if you can commit to one, and what attendants need. Link to the official e-Visa site rather than restating rules that may change. This answers the "ready to act" prompts directly and keeps patients away from unofficial agents.
Clean up doctor profiles and structured data
Pick one canonical profile URL per doctor, with the qualifications, specialities and registration details the NMC guidelines treat as patient information. Redirect duplicates. Add schema.org markup such as Hospital, MedicalOrganization and Physician, and link each hospital to the parent organisation. Google's guidance says structured data should match the visible text on the page, so do not mark up facts you have not published.
Correct the third-party sources AI already reads
| Source type | Why AI may lean on it | What to do |
|---|---|---|
| Facilitator and medical travel portals | Detailed, comparison-style pages that match patient prompts | Ask listed portals to correct names, departments and accreditation; avoid paid ranking or commission arrangements the NMC guidelines restrict |
| Google Business Profiles | Used for city, address and contact facts | One profile per hospital, consistent names, international desk number where appropriate |
| NABH directory | Independent confirmation of accreditation | Check the listed entity name matches what your pages say |
| News and trade press | Explains mergers, new centres and rebrands | Issue factual announcements when names, ownership or centres change |
| Country-language content | Answers prompts typed in Bengali, Nepali or Dhivehi | Translate the hub and key centre pages properly, reviewed by a native speaker |
What to measure
Track four numbers monthly against the same prompt set, and treat month-to-month movement of a few points as noise until it persists.
- Mention rate: the share of prompt runs where any hospital in the chain is named, by country and speciality.
- Citation share: the share of cited URLs that are your own pages rather than portals or competitors.
- Accuracy: the share of mentions with correct name, city, departments, accreditation and contact route.
- Framing: whether answers describe you in factual terms or repeat unverifiable claims from third parties.
Run the full set monthly and a short set of ten priority prompts weekly during big changes such as a rebrand. Record the engine, date, language and location for every run, because answers vary between runs and users and you will need that context to explain a change.
Common mistakes
- Publishing patient stories or "success rates" on international pages because AI "likes stories", which conflicts with the NMC guidelines as reported.
- Letting each hospital in the chain describe its accreditation, departments and international desk differently.
- Blocking all AI crawlers by default, then wondering why ChatGPT search never cites the chain.
- Testing only English prompts from an Indian location, when patients type in Bengali or Nepali from their own country.
- Leaving acquired hospitals on legacy domains with no visible link to the parent brand.
- Treating a single good answer as success instead of tracking results over many runs.
How Shubi helps
Shubi tracks how AI assistants mention and describe brands across Asia, using fixed prompt sets in the countries and languages your patients use, and reports mention rate, citations and factual accuracy over time. See AI Visibility Intelligence for how the measurement works, and content and technical services for the fixes. We do not promise mentions or rankings, and we keep recommendations within NMC advertising rules.