• AI visibility

Guide · AI search

Can AI search help a healthcare software company win health system deals?

It can help you reach the shortlist, which is where most health system deals are decided, but only if AI answers can find independent proof that you integrate with the hospital’s EHR, protect patient data and deliver results for peers. Health systems are buying faster and evaluating more vendors than before, so the first narrowing happens earlier and with less help from your sales team. No study has yet measured how often health IT buyers use AI assistants, so treat what follows as a well-supported bet, not a proven channel.

By Underneath Team Guide Updated

The short version

  1. Healthcare is buying software fast: Menlo Ventures (opens in a new tab), surveying more than 700 healthcare executives, estimates $1.4 billion of healthcare AI spending in 2025, with health systems’ buying cycles down from 8.0 months to 6.6 months.
  2. The field of vendors is crowded: one health system, Advocate Health, evaluated over 225 AI solutions to select 40 use cases, Menlo reports, and 85% of generative AI spend went to startups rather than incumbents.
  3. The EHR shapes every purchase. Epic held 43.7% of the US acute care EHR market in 2025, per KLAS data reported by Fierce Healthcare (opens in a new tab), and in a Bain and KLAS survey (opens in a new tab) nearly two-thirds of providers looked first to existing vendors before evaluating new ones, though 94% were open to looking elsewhere.
  4. Security is a gate, not a feature: HIPAA Journal (opens in a new tab) counts a record 772 large healthcare data breaches in 2025, and IBM (opens in a new tab) puts the average healthcare breach at $7.42 million.
  5. Clinicians are already AI users and want a vote: 81% of physicians use AI professionally, and 85% want a say on AI adoption in their practice, according to the American Medical Association’s 2026 survey (opens in a new tab).

Who signs a health system software contract, and what is the account worth?

A committee buys it, with IT, finance, clinicians and security all holding a veto. A won health system pays for years.

No single executive signs a health system deal alone. A chief information officer or chief medical information officer may lead, but finance wants a return, security wants a risk review, and clinicians want to know the tool fits their day. The AMA’s 2026 survey (opens in a new tab) of nearly 1,700 doctors found 55% want to be involved in AI implementation decisions to evaluate the clinical evidence, and Fierce reports that 85% want a say on AI adoption in their practice.

Budgets are moving toward software. In the 2023 Bain and KLAS survey of more than 200 provider executives, nearly 80% said they had increased IT spending materially over the past year. G2’s healthcare survey (opens in a new tab) of 208 buyers at health systems, hospitals and practices found 80% planned to raise their technology budget within 12 months.

The money is concentrated. Menlo estimates US healthcare administration spending at $740 billion a year, of which healthcare IT is only $63 billion. Health systems supplied $1 billion of the $1.4 billion flowing into healthcare AI in 2025. In practice, that means a small number of large systems decide which vendors grow. Vendors selling to independent practices face a different buyer, covered in how practice software reaches specialty clinics.

How much a health system account is worth depends on what you sell:

  • Systems of record are sticky. Oracle Health held 21.9% of the acute care EHR market in 2025, and KLAS found 35% of sampled customers say they might leave or “want to leave but can’t.” Once a core platform is installed, it tends to stay for years.
  • Point AI tools are not yet sticky. Menlo found large health systems using ambient scribes were just as likely to switch vendors as to stay, and among outpatient providers the likelihood to switch rose to 67%. For these vendors, a customer’s value depends on being named again at every review.

Virtual care platforms sell into the same health systems; see how virtual care platforms win contracts.

How far have AI assistants entered health IT vendor research?

Mostly as a research and narrowing tool around the first shortlist; no published study isolates health IT buyers yet.

The people who sign health IT contracts already use AI daily. The AMA found 81% of physicians use AI in their practices, up from 38% in 2023, and the share using it to summarize medical research rose to 39% in 2026 from 13% in 2024. Among health systems, an Eliciting Insights survey reported by Fierce Healthcare (opens in a new tab) found 75% now use at least one AI application, up from 59% in 2025. And KLAS (opens in a new tab), drawing on 228 executives, reports that 70% of providers and 80% of payers have AI strategies underway.

None of these surveys asks whether those executives use ChatGPT or Gemini to research vendors. For vendor research itself, the nearest data point comes from outside healthcare: G2’s March 2026 survey (opens in a new tab) of 1,076 software buyers found that 51% started research with an AI chatbot more often than with Google. G2 runs a review marketplace and has an interest in this finding, and its sample is not healthcare-specific.

What is documented is the scale of the narrowing job. Menlo reports that Advocate Health evaluated over 225 AI solutions to select 40 use cases, and that SimonMed, a radiology group, went from co-building with fewer than 10 vendors to piloting solutions from more than 50. A team screening hundreds of vendors needs a fast first pass. We infer that AI assistants increasingly do some of that first pass, alongside peers, KLAS reports and conferences.

A health IT leader who sticks to Google will probably meet an AI answer anyway. Across the 1,248 US searches in our AI Overview study, keywords in B2B software and technology triggered an AI Overview on 96.0% of searches, the highest rate among the eight industries we sampled.

Which questions do health IT buyers ask AI assistants?

Questions about fit with their EHR, HIPAA and security, peer results and return on investment. We wrote the prompts in this table ourselves to show how a CIO or CMIO might phrase these concerns; none was collected from an actual hospital buyer.

Buying concernIllustrative prompt
Category“What are the leading ambient documentation tools for a 12-hospital health system?”
EHR fit“Which prior authorization tools integrate natively with Epic?” or “…with Oracle Health?”
Alternatives“What are the alternatives to Nuance DAX Copilot for an academic medical center?”
Security and compliance“Which patient engagement platforms will sign a business associate agreement and have HITRUST certification?”
Peer proof“How do these revenue cycle vendors score in KLAS?”
Return“What denial reduction have health systems reported from AI coding tools?”
Interoperability“Which care coordination platforms support FHIR APIs and TEFCA exchange?”

A single question from a hospital IT team can trigger several searches behind the scenes. Google’s documentation says AI Overviews and AI Mode may use a “query fan-out” technique (opens in a new tab), running multiple related searches on subtopics; for a health IT question, those could cover EHR fit and certifications separately. OpenAI, for its part, explains that ChatGPT search typically rewrites a question (opens in a new tab) as one or more targeted queries that go to its search partners.

When our hidden-searches study logged ChatGPT’s queries, it went looking for reviews or ratings in 46.2% of its answers and went after a named publication, ranking or award in 43.8%. In health IT, a reasonable expectation is that the named sources would include KLAS, analyst coverage and trade publications.

What path runs from an AI answer to a signed health system contract?

By getting you onto the first vendor screen, which is where hospital pilots and system-wide contracts begin.

As we understand the evidence, the path runs like this:

  1. A CIO, CMIO, revenue cycle leader or innovation team asks an assistant a category, EHR-fit or alternatives question.
  2. The assistant returns a short list of health IT vendors, with links to some of the pages it drew on.
  3. The team checks those names against peers and KLAS. KLAS says 95% of its data comes from in-depth phone conversations with providers and payers.
  4. Survivors face an integration and security review: EHR connection, business associate agreement, risk questionnaire.
  5. A pilot follows, then a system-wide contract and, often, expansion to more sites.

Two features make healthcare different from general software. First, the incumbent EHR competes at every step: Menlo found most customers prefer to buy AI from their incumbent EHR for everything except ambient scribes and chart review. Second, the pace has changed. Menlo says buying cycles have compressed from 12 to 18 months to under six for many providers, which leaves less time to get noticed once a search starts. Payers are the exception: their cycles lengthened from 9.4 months to 11.3 months.

A hospital contract that began with an AI answer almost never carries a referral tag in your analytics. A buyer who met you in an AI answer may appear months later through a conference meeting, a peer referral or an EHR app marketplace. We cover the measurement problem in how to prove GEO caused sales.

Why does an AI assistant list one health IT vendor and skip another?

The assistants keep their selection rules private; research favors independent sources, and hospitals favor proof they can check.

What Google and OpenAI disclose. Both companies confirm that their AI answers run web searches and show links to what they drew on. Neither explains why a particular EHR add-on or revenue cycle vendor gets named.

What researchers have measured. On US software questions, AI search drew 72.7% of its sources from earned sites such as reviews and independent publications, Chen and colleagues (opens in a new tab) report, compared with 45.4% for Google; in health IT, KLAS and the trade press are the obvious earned sites. In our four-assistant study, the overlap between assistants’ brand picks was highest for B2B software, at 0.543 on a scale from 0 to 1, so being named by one assistant is no guarantee of the others.

What health buyers check. Several trust factors are specific to this industry:

  • EHR integration. The Bain and KLAS survey called seamless EHR integration a key purchasing criterion for all providers. With Epic at 56.9% of US hospital beds, per KLAS, “works with Epic” is often the first filter.
  • Security and privacy. Hacking and IT incidents caused more than 80% of large breaches in 2025, HIPAA Journal reports, and the largest that year was at a business associate, Conduent, exposing the health information of more than 62 million Americans. Vendors are part of the risk hospitals screen for. Physicians feel it too: on patient privacy, 41% expected AI to cause harm against 13% expecting help.
  • Interoperability. In 2023, 70% of US hospitals at least sometimes exchanged data across all four domains of interoperability (send, find, receive, integrate), according to federal data reported by TechTarget (opens in a new tab). Buyers expect vendors to fit that exchange.
  • Peer evidence. Health executives buy on outcomes they can verify with a peer. In G2’s survey, 83% said it is important that software they buy incorporates AI, but KLAS reports leaders “increasingly seeking investments that provide measurable ROI,” with providers refocused on revenue cycle tools.

Our inference. Most of what a health system verifies is public or could be: integration listings, certifications, security documentation, peer ratings, published outcomes. An assistant can read and cite the same pages a hospital’s security reviewer opens. We would expect a health IT vendor whose integration, security and outcome proof is public, up to date and consistent to give both the buying committee and the assistant more to go on. No study has yet checked it with EHR add-ons, scribes or revenue cycle tools.

How much can a missing AI mention cost an EHR add-on or scribe vendor?

Mainly seats in health system evaluations rather than website visits, though nobody has yet priced that loss.

  • Shorter cycles shrink the window. If a health system decides in 6.6 months instead of 8.0, a vendor that is not in the first screen has less time to be added later.
  • The incumbent fills the gap. When buyers look first to existing vendors, as nearly two-thirds did in the Bain and KLAS survey, a startup missing from AI answers loses by default to the EHR’s own module, we infer.
  • Weak stickiness raises the cost. In categories where customers are as likely to switch as stay, being absent at renewal reviews can mean losing existing revenue, not only new deals.
  • One good answer is not a fixed place. When our consistency study put the same question to ChatGPT five times, only 25.2% of the brands ChatGPT named came back in every run.

Which GEO work matters most when your buyers are hospitals?

Making EHR, security and outcome proof easy for assistants to find and repeat, though no mention is guaranteed.

  1. One clear identity. Say what you do, for which care setting and with which EHRs, the same way on your site, app marketplace listings, KLAS profile and company databases. When an assistant gets your EHR support or care setting wrong, our guide to fixing wrong brand information in AI answers sets out the corrections.
  2. Public integration and security pages. Publish which EHRs you connect to and how, which standards you support, your certifications, and whether you sign business associate agreements, on plain web pages rather than only in sales decks.
  3. Independent validation. Take part in KLAS interviews, publish outcomes with named health system customers who agree to it, and earn coverage in trade publications. KLAS profiles and health IT trade press fit the wider method we describe in how brands build authority for AI search.
  4. Content for real buyer questions. Write honest comparison, alternatives and return-on-investment pages for specific care settings. No medical advice and no unsupported clinical claims. Third-party rankings of health IT vendors tend to carry more weight than your own site; which pages to target explains why.
  5. Newness is a handicap to plan for. With 85% of AI spend going to startups, many vendors are young. Assistants often miss recent launches, as we explain in why ChatGPT misses new products.
  6. Tracking every assistant a hospital buyer might open. Put your health system buyers’ questions repeatedly to ChatGPT, Google AI Overviews and AI Mode, Gemini, Perplexity, Copilot and Claude; how many prompts to track covers the sample size.

Stay honest. Planted reviews or invented outcomes are a regulatory and reputational risk in healthcare; see legitimate GEO versus manipulation. Much of this adapts the approach in B2B SaaS revenue from AI search to hospital procurement. School districts buy in a similar way, through committees, evidence reviews and privacy checks, as how EdTech companies reach schools shows.

Which questions about AI search and hospital purchasing remain open?

Nobody has yet shown that appearing in AI answers grows a health IT vendor’s pipeline.

  • No survey of health IT buyers’ AI search use. The AI adoption surveys above measure use of AI products, not use of assistants to choose vendors.
  • The sources have interests. Menlo Ventures invests in healthcare AI companies, G2 sells review visibility, and Sage Growth Partners, which found 57% of executives rank AI clinical tools as their top technology initiative (opens in a new tab), advises health tech marketers.
  • Some data are dated. The Bain and KLAS finding on looking first to existing vendors comes from 2023.
  • The link to revenue is the thinnest evidence of all. The general case is reviewed in does AI visibility drive business results; health IT has no data of its own yet.

How can a health IT vendor tell whether AI answers are costing it evaluations?

Ask what health system buyers ask, see which vendors come back, then publish the missing proof.

Write down what a CIO, CMIO or revenue cycle director would ask at each stage and in each care setting: category, EHR fit, alternatives, security and return. Repeat every question several times in ChatGPT, Gemini, Perplexity and Google’s AI answers, because one run can mislead. Note which vendors are named, which sources are cited, and whether your EHR integrations, certifications and outcomes come back accurately. In health IT, the gaps usually trace to proof kept in sales decks rather than on public pages.

We can run that check with you: ask us for a review of your health system shortlist visibility. It shows where AI answers place you on, or leave you off, those shortlists, and which gaps in your integration, security and outcome evidence are most likely costing you pilots, contracts and renewals. The generative engine optimization service page lays out how we diagnose those gaps, publish the missing EHR and security proof, and track what assistants then tell hospital buyers.

Frequently asked questions

Do hospital CIOs use ChatGPT to find vendors?

No published study measures it. What is known: 75% of US health systems use at least one AI application, and 81% of physicians use AI professionally.

Does a KLAS rating help AI visibility?

Plausibly, but untested. ChatGPT targeted a named publication, ranking or award in 43.8% of answers in our study, and KLAS is health IT’s best-known rating source.

Should we publish our security and HIPAA documentation?

Publish the facts buyers verify first, such as certifications and whether you sign business associate agreements. Keep sensitive detail behind a review process.

Can we compete with the EHR vendor’s own AI module?

Yes, where you are clearly better: Menlo found buyers favor startups for ambient scribes and chart review, but prefer their EHR for most other uses.

How fast can GEO show up in health system pipeline?

Expect quarters, not weeks. Health systems’ buying cycles average about 6.6 months for AI, and payers’ about 11.3 months.

Sources

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