For years, healthcare marketers have treated online reputation as a fairly straightforward equation.
A patient searches for a physician. Google serves up a profile. The patient sees a star rating, reads a few reviews, visits the website, and starts forming an opinion.
Except that first impression was never quite as neutral as it looked.
James Gardner recently wrote about preliminary research examining how Google selects which healthcare reviews appear as “Most Relevant.” The researchers compared the reviews Google surfaced by default with the broader body of reviews patients had actually written.
They found something worth paying attention to: the algorithmically curated version did not necessarily tell the same story as the full review set.
Different themes were emphasized. Different themes were buried. The overall impression could change.
I was fortunate to contribute to the framing of that work around trust and anchoring, and it left me thinking about what comes next.
Because Google may no longer be the only algorithm sitting between a physician and a prospective patient.
Increasingly, the patient may not read the reviews at all.
They may ask AI.
From search results to answers
Imagine someone looking for a new primary care physician.
Instead of opening five browser tabs and comparing websites and Google profiles, she asks:
“Who is a good primary care doctor near me who actually listens and won’t rush my appointments?”
Or:
“Which of these three doctors seems best for someone with a complicated medical history?”
Or even:
“Summarize what patients say about Dr. Smith.”
The patient’s decision is now potentially being mediated twice.
First, algorithms determine which information about the physician is visible and retrievable.
Then an AI system interprets that information and turns it into an answer.
That distinction matters.
Healthcare reputation strategy has historically focused heavily on what people can see: star ratings, review volume, recency, search rankings and the first page of results.
We may also need to start thinking about what machines can infer.
Five stars doesn’t tell an AI very much
Consider two reviews.
“Great doctor! Highly recommend.”
And:
“Dr. Smith spent almost an hour with me, explained why she didn’t think I needed another medication, answered every question I had, and called two days later to see if my symptoms had improved.”
Both might come with five stars.
But they contain radically different amounts of information.
The second provides evidence of listening, communication, clinical judgment, accessibility and follow-through.
Those details have always mattered to prospective patients.
What’s changing is that they may also become the raw material from which AI systems construct an understanding of the physician.
A study published in npj Digital Medicine in August analyzed 4.1 million reviews of more than 226,000 U.S. physicians using large language models. The researchers were able to extract specific patient-perceived traits from the review text, including dimensions related to competence and patient-centeredness.
In other words, the text underneath a star rating contains enough signal for an AI system to build a much richer picture of a physician than “4.8 stars.”
That is exactly why the substance of a physician’s digital reputation may matter more in an AI-mediated search environment.
This matters more when patients actually have a choice
There is another reason I think this deserves attention in independent and direct-pay medicine.
When insurance networks, health systems or referral pathways heavily constrain where someone receives care, reputation certainly matters. But it operates within a limited choice set.
Direct-pay and membership-based models change that equation.
A patient deciding whether to pay a physician directly has to answer questions that insurance sometimes answers for them:
Why should I go outside my insurance?
Why this physician?
Why is this relationship worth paying for?
And, in a subscription model, why should I keep paying every month?
Those are trust and value questions.
For an independent physician whose growth depends on reputation, referrals and meaningful differentiation, having a strong digital reputation may therefore mean something different than accumulating positive reviews.
The internet needs enough evidence to explain why patients choose you.
Reputation management may become reputation legibility
This is where I think healthcare marketing has to evolve.
The goal cannot be to manufacture reviews that contain the “right” keywords for an algorithm. Besides being a terrible patient experience, that would quickly turn reputation management into another version of SEO gaming.
The better question is whether a physician’s genuine differentiation is legible online.
If patients consistently value that their physician listens, explains, follows up, remembers their history or makes care unusually accessible, does the physician’s digital footprint actually reflect that?
Could someone who has never met that physician understand what makes the practice different?
And increasingly:
Could an AI?
Healthcare marketers have spent years optimizing physician reputation for the search results page.
The next challenge may be making sure the physician’s actual reputation survives the algorithms interpreting it.
Because the prospective patient may still be making a deeply human decision about whom to trust.
They may just be asking a machine to help them make it.
SOURCES & EVIDENCE
Primary evidence
Luo J, Han R, Welivita A, et al. Mapping patient-perceived physician traits from nationwide online reviews with LLMs. npj Digital Medicine. Published August 19, 2026.
Context source
James A. Gardner. The Algorithmic Editor. The AI Journal. May 4, 2026.
Evidence boundary
The npj Digital Medicine study supports the claim that review text contains extractable patient-perceived trait information. The “second algorithm” thesis is a forward-looking interpretation. James Gardner’s Google review-curation work is described as preliminary context and should not be presented as settled evidence.
When a piece rests on my own data, I say so. When it rests on someone else’s, I say whose, and whether they funded it.