A
New Front Door to Healthcare

The way people find healthcare providers is changing in a way that
is happening faster than most health systems have noticed. A growing number
of patients are bypassing traditional search engines, insurance portals, and
word-of-mouth entirely when they need to find a doctor. They are opening
ChatGPT, Gemini, or a similar generative AI tool and asking in the way you
would ask a knowledgeable friend: someone who can filter by location, insurance
coverage, and specialty without making you read through ten pages of results
first.

The scale of this shift is documented in new survey data that the
healthcare industry should be reading carefully. TechTarget
reports that nearly one third of patients now rely on AI-powered search to
identify healthcare providers. They use conversational prompts to filter
candidates by location, specialty, insurance acceptance, and patient reviews,
and they expect answers in seconds rather than the sustained effort that
traditional provider searches require. The appeal is the same as every
successful AI application: an enormous reduction in the cognitive work
required to get to a useful result.

What Patients Are Actually Asking AI

The conversations patients are having with AI tools when searching
for healthcare providers are more varied than the headline statistic
suggests. Some are doing straightforward directory searches, asking for
dermatologists within five miles who accept a particular insurance plan.
Others are asking more nuanced questions that would previously have required
either prior knowledge or a conversation with a nurse or GP: which type of
specialist is appropriate for a given cluster of symptoms, or how to
distinguish between the training and focus areas of different practitioners
in the same field.

An Ipsos
survey found that 31 percent of consumers are already using
generative AI for healthcare queries of this type. The trust picture is
mixed. A study from the Annenberg Public Policy Center found that 75 percent
of respondents considered AI-generated health responses useful and 63 percent
judged them reliable, yet a significant majority still confirmed their
AI-sourced information with a doctor or another authoritative source before
acting on it. The pattern that emerges is one of AI as a research accelerant
rather than a final authority: it narrows the field and frames the question,
but most patients are not yet willing to let it make the decision.

The Reliability Problem

The caution that most patients exercise is warranted. Generative
AI systems do not have access to real-time provider databases, and their
training data has a cutoff date that means specific information about
individual practitioners, such as whether they are currently accepting new
patients, what insurance plans they participate in, or whether they have
moved or retired, may be out of date by months or years. A patient who finds
a GP through an AI recommendation and then calls the practice to discover the
doctor retired two years ago has not been served well by the
technology.

The hallucination problem compounds this. Generative AI systems
produce plausible-sounding outputs even when the underlying facts are wrong
or absent from their training data. In a healthcare context, a
plausible-sounding but incorrect recommendation is not merely an
inconvenience. It can delay access to care, send patients to practitioners
who are not appropriate for their needs, or undermine trust in what might
otherwise have been a genuinely useful tool. A BMJ analysis of how patients
integrate AI into primary care decisions identified uneven adoption across
demographic groups, with older patients and those with lower digital literacy
at particular risk of misinterpreting AI confidence as clinical
reliability.

Economic Drivers Behind the Shift

The move toward AI-assisted provider search is not happening in a
vacuum. Healthcare costs are rising in most developed markets, and the time
required to navigate complex insurance systems and opaque provider
directories is itself a cost that patients bear without compensation. For
someone managing a chronic condition across multiple specialists,
coordinating referrals, insurance authorisations, and appointment timing can
consume hours that most people cannot easily spare.

AI reduces that friction substantially. The same economic logic
that is driving AI adoption across other sectors of the economy applies here.
Just
as AI therapy tools are expanding access to mental health support
for people who cannot navigate the traditional system, AI-assisted provider
search is expanding the practical accessibility of healthcare navigation for
people who would previously have given up or made suboptimal choices because
the information was too hard to obtain. The technology is not solving the
underlying access and cost problems in healthcare, but it is reducing the
informational barriers that compound them.

What Healthcare Systems Need to Acknowledge

Health systems and regulators have been slow to engage with the
fact that a significant proportion of patients are now forming their first
impression of a provider or service through an AI interface that the system
has no control over and no visibility into. If a patient’s decision about
whether to seek care, and from whom, is being shaped by an AI tool, the
accuracy and completeness of that tool’s outputs becomes a public health
question, not merely a product quality issue.

The AAFP has flagged risks of bias and misinformation in
AI-generated healthcare guidance, noting that if AI is trained on healthcare
data that reflects existing systemic inequities, it may reinforce those
inequities in the recommendations it produces. A system trained predominantly
on data from urban, well-resourced health markets may provide less accurate
or less useful guidance for patients in rural areas or in markets with
different provider profiles. These are not hypothetical concerns. They are
the kinds of systemic bias that have been documented in AI systems across
many domains, and there is no reason to assume healthcare is
exempt.

What Responsible Integration Looks Like

The constructive response to AI’s growing role in healthcare
navigation is not to discourage patients from using these tools but to
improve the conditions under which they use them. Healthcare providers and
systems can make their own data more accessible to AI platforms through
standardised, real-time APIs that allow accurate information about provider
availability, specialties, and insurance participation to be integrated into
AI responses rather than relied on from potentially stale training
data.

Health literacy initiatives can help patients understand what AI
can and cannot reliably provide, giving them the conceptual framework to use
these tools well rather than simply assuming that a confident-sounding
response is an accurate one. And regulators can begin developing standards
for how AI tools operating in healthcare-adjacent contexts should handle the
unique responsibility that comes with being the first point of contact for
someone seeking medical help. The
regulatory frameworks taking shape around AI more broadly will need
healthcare-specific extensions if they are to address what is already
happening in patient behaviour. The patients are not waiting for the
frameworks to catch up. They are already asking their questions, and the AI
is already answering them, with varying degrees of accuracy and no formal
accountability for the consequences when the answers are wrong. Building that
accountability into the system before the behaviour becomes too entrenched to
redirect is the challenge that health regulators and technology companies now
share, whether they have acknowledged it formally or not.

About the Author

By Stuart Kerr, Technology Correspondent, LiveAIWire. Stuart
covers artificial intelligence, health technology, and the ways AI is
changing how people interact with complex systems. About
LiveAIWire.