2026 Patient Choice Report
How AI, Online Reviews, and Rising Standards Have Redrawn the Path to Care
In 2025, we learned AI was changing how patients find providers. Now, new for 2026, we learned that patients have already set their standards when it comes to selecting healthcare providers. Three in four won’t book with a doctor rated below 4.0 stars,1 and over half have already walked away from a doctor because of online reviews.2
Patient choice has hardened into patient standards, and trust starts with what AI says about you, how you show up online, and whether patients see you engaging.
Introduction
Research on patient choice behaviors has historically come from one of three places:
- Healthcare advisory firms survey their own consulting clients, producing data weighted toward the executive perspective.
- Software platforms report on the activity of their own customer base, producing data weighted toward platform behavior.
- General consumer research lumps healthcare alongside restaurants, retailers, and home services, producing data weighted toward markets where the stakes are very different from healthcare’s.
None of these approaches specifically focus on how patients search for and find healthcare providers, and how that behavior changes over time.
This is the third time rater8 has surveyed U.S. adults about how they search for, evaluate, and choose healthcare providers.
In 2024, 84% of patients told us they checked online reviews before booking. In 2025, AI tools like ChatGPT and Google’s AI Overviews flipped the script, with 31% of patients adopting them to conduct research on providers.
In each survey since, the data have moved in the same direction: more digital, more selective, more decisive.
Google’s May 2026 search redesign, its biggest in 25 years, further reduces the role of traditional link browsing in favor of AI-generated answers. This year’s findings show that patients are already there.
In 2026, the trend became the thresholds:
For two years, online reviews played a growing role in how patients evaluated providers. In 2026, reviews became a deciding factor in whether patients book, cancel, or move on.
- 75% of patients now refuse to book with a provider rated below 4.0 stars.1
- 55% have already walked away from a provider, either avoiding or canceling an appointment because of online reviews, up from 40% since our 2025 survey just 9 months ago.2
- 66% now say that seeing a provider’s response to online reviews directly influences whether they trust the provider, the largest year-over-year movement across all three editions of this survey.3
- 47% have used AI to research healthcare providers, up 16 percentage points in 9 months.4
1. Reaching 4-Stardom
The biggest shift in this year’s data is the speed at which patient standards became a barrier to booking appointments. In 2025, 40% of patients canceled or avoided booking an appointment with a provider because of negative online reviews. In 2026, that number is 55%, an increase of 15 percentage points.2
of patients have canceled or avoided booking with a provider because of online reviews.
Up from 40% in 2025. More than half of all patients have walked away from at least one doctor based on what they read online.
Chart data
Patients who canceled or avoided booking with a provider because of online reviews.
| Metric | 2025 | 2026 | Change |
|---|---|---|---|
| Patients who canceled or avoided booking because of online reviews | 40% | 55% | +15 percentage points year over year |
When asked to indicate the lowest star rating they would willingly accept before booking, 75% of patients said 4.0 or higher, and 44% said they wouldn’t book below 4.5 stars. Only 11% said star rating doesn’t factor into their decision at all.1
The 4.0-star threshold creates a cliff effect for any practice or provider rated below it. A practice with a 3.8 average isn’t just losing patients to better-rated competitors, but is being filtered out of consideration altogether before any other factor comes into play.
The bar that healthcare providers and practices have to clear to gain patient trust is measurably higher than for other businesses.
How does healthcare compare? BrightLocal’s 2026 Local Consumer Review Survey gives us the benchmark:
For practice leaders, the implication is direct: patients aren’t making finely nuanced judgments about clinical excellence the first time they see your reputation reflected online. They’re checking first whether you clear the 4.0-star hurdle before they go any further in their research journey. Most of what determines whether you clear that bar, from front-desk interactions to wait times to billing communication, has more to do with how your organization runs than with the medicine you practice.
The top deal-breakers patients cite in reviews aren’t clinical. They’re rude staff (52%), “the doctor didn’t listen” (52%), long wait times (41%), and billing issues (40%).5
2. The New Path to Care
Over the past year, 72% of patients were in the market for a new provider, either choosing a new doctor (47%) or searching without ultimately switching (25%).6 One in four patients went looking for a replacement and decided to stay, at least for now. They’re still part of your patient base, but were open to leaving. Meaning, if their next experience is less than satisfactory, or if they see negative reviews online that reinforce what frustrated them, they’re less likely to come back for follow-ups or ongoing care.
The path to care extends beyond new patient acquisition to retention. Every search a current patient runs is a moment where your online reputation either reinforces their decision to stay or gives them permission to go.
When asked what they search for first, patients overwhelmingly start local, with 55% typing some version of “specialty near me.”7
When searching for a new doctor online, what do you search for first?
Chart data
First search queries patients use when searching for a new doctor online.
| Search query | Percentage |
|---|---|
| "Specialty near me" | 55% |
| "Best/top-rated specialty" | 34% |
| Specific doctor's name | 32% |
| Reviews for a practice | 26% |
| "Specialty + insurance" | 23% |
What’s changed is everything that happens after that first search. When patients answered which sources most influenced their final decision, the hierarchy looked very different from their answers 9 months ago.
Which sources most influence your decision when choosing a new doctor?
Chart data
Sources that most influence patient decisions when choosing a new doctor.
| Source | Percentage |
|---|---|
| Friends and family | 43% |
| Insurance website | 43% |
| Review sites | 40% |
| AI tools (ChatGPT, Claude, etc.) | 36% |
| Google search results | 34% |
| Doctor referral | 32% |
| Social media | 30% |
| Voice assistant | 8% |
AI tools like ChatGPT and Claude are now cited by 36% of searchers, ahead of Google search results at 34% and ahead of doctor referrals at 32%.8 Among patients who actually switched providers, AI’s influence was even higher at 39%.8
Patients doubled down on this later in the survey when asked which section of Google search results they trust most. 37% of patients chose AI Overview,13 the AI-generated summary that now appears at the top of most Google search results pages when searching for a healthcare provider. Notably, only 20% picked the organic blue links, the local map pack came in at 13%, and sponsored ads were 7%.
That ranking should concern any practice leader looking at their marketing budget. A 2025 Society for Health Care Strategy & Market Development (SHSMD) seminar on AI in healthcare marketing highlighted how AI-powered search is eroding organic website traffic for health systems, pushing practices toward an over-reliance on paid media spend.
Meanwhile, the broader marketing industry is moving in the opposite direction. According to Clutch’s 2025 GEO vs. SEO & PPC report, 63% of marketers plan to increase their generative engine optimization (GEO) budgets in the year ahead, and 78% of companies now fund GEO at near-parity with traditional SEO and paid search. Practices are spending the most on the channel patients trust the least (sponsored ads, 7%), and the least on the channel patients trust the most (AI Overviews, 37%).9
Today's Patient Journey
1. AWARENESS
Patients begin their search online. 72% have actively searched for or chosen a new provider in the past 12 months.6 Local searches dominate, with 55% starting at “[specialty] near me.”7
2. RESEARCH
Patients now layer their research across multiple channels. 47% have used AI tools to research providers.4 AI Overviews are the most trusted section of Google at 37%.9 Star ratings are a threshold filter, with 75% of patients requiring 4.0 stars or higher.1
3. DECISION
Reviews are determinative. 55% of patients have canceled or avoided booking with a provider because of negative online reviews.2 66% say a provider’s response to reviews directly influences their trust.3
4. EXPERIENCE
The in-office experience drives review content. The top deal-breakers in patient reviews are operational, not clinical: rude or unhelpful staff (52%), “the doctor didn’t listen” (52%), substandard care (45%), long wait times (41%), and billing issues (40%).5
5. ENGAGEMENT
The participation gap is closing. 42% of patients rarely or never leave reviews,10 down from 56% in 2025. 68% say they would leave a review if their doctor asked.11
3. AI is Unavoidable
Over the last couple of years, AI has been the emerging story. But in 2026, it’s the main character. 47% of patients have used an AI tool to research healthcare providers, up from 31% in 2025.4 Another 40% are aware of these tools, but haven’t used them yet, and only 13% say they’re not familiar with AI for healthcare research at all.
And the adopters aren’t who most people would expect. The age group leading AI use for provider research is adults ages 45 to 60 at 64%, ahead of every other demographic including 18-to-29- year-olds at 28%.12
But those numbers only capture patients who are actively choosing to use AI tools. On Google, AI suggestions don’t wait to be chosen. According to BrightEdge research, 89% of Google searches that include a healthcare keyword now trigger an AI Overview. Patients aren’t necessarily choosing to use AI; rather, it’s being placed in front of them.
The AI Hallucination Hang-Up
The biggest risk is also the biggest opportunity. Patient trust in AI isn’t slowing down despite its accuracy problems. 43% of patients say they trust AI more than they did a year ago,13 while only 20% trust it less.
Compared to 12 months ago, do you trust AI-generated health information more, less, or about the same?
Chart data
Patient trust in AI-generated health information compared to 12 months ago. Survey year: 2026. Sample size: n=992.
| Response | Percentage | Sample |
|---|---|---|
| Trust AI more | 43% | 2026 | n=992 |
| About the same | 37% | 2026 | n=992 |
| Trust AI less | 20% | 2026 | n=992 |
When asked how they trust AI-generated search results compared to standard Google results for provider research, 27% said they trust AI more (up from 19% in 2025).14 Combined with those who trust AI and Google equally, 55% of patients now rate AI results as equal to or better than traditional search.
How much do you trust AI-generated search results compared to standard Google search results when researching a healthcare provider?
Chart data
Trust in AI-generated search results compared to standard Google search results when researching a healthcare provider. Sample size: n=992.
| Year | Response | Percentage |
|---|---|---|
| 2025 | I trust AI more | 19% |
| 2025 | I trust them about the same | 33% |
| 2025 | I trust AI less | 16% |
| 2025 | I don't trust AI | 11% |
| 2025 | Not sure | 21% |
| 2026 | I trust AI more | 27% |
| 2026 | I trust them about the same | 28% |
| 2026 | I trust standard results more | 25% |
| 2026 | I don't trust either | 9% |
| 2026 | Not sure | 11% |
That growing trust makes the next finding urgent. Among the 465 patients who have used AI to research providers, 66% said they have encountered incorrect provider information from an AI tool.15 Wrong addresses, phone numbers, insurance details, or office hours. These aren’t harmless errors. They’re the kind of details that determine whether a patient shows up, calls, or books at all.
But the problem runs deeper than AI. A 2024 study published in BMC Health Services Research examined over 449,000 U.S. physicians across 5 major health insurer directories and found that physician address information was consistent only 17% to 28% of the time, and phone numbers were consistent only 16% to 27% of the time. The source data feeding AI tools is already broken. When the information AI tools are scraping is wrong, the outputs are also wrong.
And despite all of this, 60% of AI users said they trusted the summary they received without verifying it.15 Patients know AI can get things wrong, but they use it anyway. For practice leaders, a takeaway like this is uncomfortable but fixable.
AI tools pull from the same sources that have always shaped online reputations: directories, review sites, practice websites, third-party listings, and social media. When those sources are inconsistent or outdated, AI confidently passes the inconsistencies along to patients who don’t check them. Practices with consistent, well-distributed information across every listing and directory are more likely to be represented accurately when AI assembles its answers.
This is the reputation management discipline that didn’t exist two years ago: managing what AI says about you when no human is looking.
4. Trust Goes Both Ways
Two of the most important findings in this year’s data work in tandem. The first is that patients are paying closer attention to how providers respond to reviews than ever before. The second is that more patients are willing to leave reviews in the first place.
In 2025, 42% of patients said that seeing a healthcare provider respond to online reviews influenced their trust in that provider. In 2026, that number is 66%,3 a 24-point year-over-year jump. Two-thirds of patients now factor a provider’s response behavior into their trust assessment, and the implication is the same whether the response is to praise or to criticism. Patients want to see that providers are paying attention to what they’re saying.
of patients say seeing a provider respond to online reviews influences their trust.
Up from 42% in 2025. Two-thirds of patients now factor a provider’s response behavior into their trust assessment, whether the response is to praise or to criticism.
Chart data
Patients who say seeing a provider respond to online reviews influences their trust.
| Metric | 2025 | 2026 | Change |
|---|---|---|---|
| Seeing a provider respond to online reviews influences patient trust | 42% | 66% | +24 percentage points year over year |
The trust works in both directions, however. A glowing review with no response feels like a missed opportunity, and a negative review with no response feels like an admission. A negative review answered with care, professionalism, and accountability often does more for prospective patient trust than the negative content itself can take away.
Review responses aren’t just being read by prospective patients scrolling through Google or Healthgrades, either. They’re being ingested by AI tools. When a patient asks ChatGPT or Claude about a provider, the response those tools generate is shaped by everything available on the public web, including how — and whether — a provider has responded to reviews.
A thoughtful response to a negative review not only reassures the next prospective patient who reads it, but also improves the information AI has available when it summarizes your practice for the next person who asks.
The Participation Gap is Closing
Our previous reports have documented one of the central paradoxes of online reputation: patients rely heavily on reviews to choose providers, but rarely leave reviews themselves. In 2025, 56% of patients said they rarely or never left reviews for healthcare providers. In 2026, that number dropped to 42%.10
23% of patients now say they leave a review after every visit, up from 16% in 2025. 35% say they leave reviews sometimes. The combined “engaged reviewer” group has grown by double digits in 9 months.10
The participation gap isn’t gone. Four in ten patients still rarely or never share their experiences online. When more patients participate, good service gets recognized faster, and bad service gets called out faster. The window during which a single negative review can dominate a profile shrinks, and so does the window during which an unaddressed reputation problem can grow.
The good news is that the single most powerful tool for further closing the gap is also the simplest. When asked the likelihood they would leave a review if their doctor asked, 68% of patients said they’d be likely or very likely to do so, and only 12% said they wouldn’t.11
Closing the gap between patient willingness and patient action is what automated review request systems are built to do.
5. What Keeps Patients Coming Back
The survey responses show what drives patients to speak up, but also what keeps them coming back. When asked the main reason they continue to see their current primary care provider, patients’ top answer was straightforward: “they know my medical history” at 39%. Insurance acceptance and trust/bedside manner tied at 17% each, and easy communication through a portal or app came in at 9%.16
The number-one reason patients stay is continuity. Every visit adds to the history they’re fostering with their provider. But that loyalty is not unconditional. 28% of patients are actively looking for a new provider, and another 31% say they’re open to it.17 Combined, 59% of patients are potentially in play.
Patients stay because of compounded trust and medical history, but they leave when the experience breaks down and the online evidence confirms it. The practices that protect both sides of that equation, delivering a stellar in-office experience while maintaining a strong online reputation, are the ones that keep their schedules full and reputations an honest reflection of the great care they provide.
Conclusion
Meet Patients Where They Are
The results of three surveys tell a clear story. The standards patients now hold are higher. The channels they use to evaluate providers have changed. And the gap between practices that manage their online reputation and those that don’t is widening in ways that directly affect schedules, retention, and revenue.
Key Takeaways
1. 75% of patients won’t book with a provider rated below 4.0 stars.
And 44% draw the line at 4.5.1 More than half have already walked away from a provider because of online reviews.2 Your star rating is no longer a vanity metric, but a filter that determines whether patients ever see the rest of what you offer.
2. AI is now a top influence on provider choice.
AI tools like ChatGPT and Google’s AI Overviews are cited by 36% of searchers,8 ahead of Google search and doctor referrals. Among patients who switched providers, AI was the number-one influence at 39%.8
3. AI is answering questions about your practice.
66% of AI users have encountered incorrect provider information, but 60% trusted it anyway without verifying.15 The data feeding AI, from directories to listings to review sites, can be incorrect. Practices with consistent, well-distributed information across every channel are the ones AI gets right.
4. Patients want to hear from you.
66% of patients now say a provider’s response to reviews influences their trust,3 up 24 points year-over-year. Plus, 68% would leave a review if their doctor asked.11 The willingness to provide feedback is there; practices just have to ask (and respond!).
Patients are bringing their consumer instincts to healthcare, and increasingly, their research is shaped by AI tools like Google’s AI Overviews, ChatGPT, Claude, and Perplexity. The practices that show up accurately, respond visibly, and earn strong ratings across every channel where patients search are the ones best positioned to earn and keep patient trust.
Download a free PDF of the report!
Contents
Survey Methodology
This report is based on a survey conducted in April 2026 by rater8 via SurveyMonkey, which collected responses from over 1,000 U.S. adult patients aged 18 to 65 and older, representing multiple regions, incomes, and genders. The survey was designed to track changes from rater8’s two prior Patient Choice Surveys. Eighteen questions were maintained year-over-year for direct comparison, and additional questions were added in 2026 to measure new behaviors around AI search, the booking experience, and specialist referral patterns.
Age
Chart data
Age distribution of survey respondents. Question: Q1. Base: n=992.
| Age group | Percentage | Base |
|---|---|---|
| 65+ | 12.1% | Q1 | Base: n=992 |
| 55–64 | 11.5% | Q1 | Base: n=992 |
| 45–54 | 24.9% | Q1 | Base: n=992 |
| 35–44 | 30.5% | Q1 | Base: n=992 |
| 25–34 | 16.1% | Q1 | Base: n=992 |
| 18–24 | 4.9% | Q1 | Base: n=992 |
Gender
Chart data
Gender distribution of survey respondents. Question: Q2. Base: n=992.
| Gender | Percentage | Base |
|---|---|---|
| Non-binary | 0.3% | Q2 | Base: n=992 |
| Prefer not to say | 0.4% | Q2 | Base: n=992 |
| Male | 46.1% | Q2 | Base: n=992 |
| Female | 53.2% | Q2 | Base: n=992 |
Household Income Distribution
Chart data
Household income distribution of survey respondents. Question: Q4. Base: n=992.
| Household income | Percentage | Base |
|---|---|---|
| Prefer not to answer | 4.3% | Q4 | Base: n=992 |
| $200,000+ | 8.1% | Q4 | Base: n=992 |
| $175,000 to $199,999 | 10.5% | Q4 | Base: n=992 |
| $150,000 to $174,999 | 13.3% | Q4 | Base: n=992 |
| $125,000 to $149,999 | 10.2% | Q4 | Base: n=992 |
| $100,000 to $124,999 | 13.6% | Q4 | Base: n=992 |
| $75,000 to $99,999 | 10.7% | Q4 | Base: n=992 |
| $50,000 to $74,999 | 9.3% | Q4 | Base: n=992 |
| $25,000 to $49,999 | 10.1% | Q4 | Base: n=992 |
| $10,000 to $24,999 | 8.1% | Q4 | Base: n=992 |
| $0 to $9,999 | 1.8% | Q4 | Base: n=992 |
Health Insurance
Chart data
Health insurance distribution of survey respondents. Question: Q5. Base: n=992.
| Health insurance | Percentage | Base |
|---|---|---|
| Military/VA | 1.0% | Q5 | Base: n=992 |
| Prefer not to answer | 1.6% | Q5 | Base: n=992 |
| Uninsured | 2.2% | Q5 | Base: n=992 |
| Marketplace (ACA) | 5.1% | Q5 | Base: n=992 |
| Family plan | 8.1% | Q5 | Base: n=992 |
| Medicaid | 8.1% | Q5 | Base: n=992 |
| Medicare | 23.5% | Q5 | Base: n=992 |
| Employer-sponsored | 50.4% | Q5 | Base: n=992 |
Geographic Distribution
Chart data
Geographic distribution of survey respondents by U.S. Census Division. Question: Q3. Base: n=992.
| Census division | Percentage | Base | Note |
|---|---|---|---|
| Pacific | 18.2% | Q3 | Base: n=992 | Alaska and Hawaii included in Pacific region; not shown on map. |
| Mountain | 5.3% | Q3 | Base: n=992 | States shaded by U.S. Census Division. |
| West North Central | 11.1% | Q3 | Base: n=992 | States shaded by U.S. Census Division. |
| West South Central | 8.2% | Q3 | Base: n=992 | States shaded by U.S. Census Division. |
| East North Central | 17.7% | Q3 | Base: n=992 | States shaded by U.S. Census Division. |
| East South Central | 15.4% | Q3 | Base: n=992 | States shaded by U.S. Census Division. |
| New England | 6.7% | Q3 | Base: n=992 | States shaded by U.S. Census Division. |
| Middle Atlantic | 6.9% | Q3 | Base: n=992 | States shaded by U.S. Census Division. |
| South Atlantic | 10.5% | Q3 | Base: n=992 | States shaded by U.S. Census Division. |
A Note on Data Quality
To protect the integrity of the findings, rater8’s DataLabs team conducted a structured data quality review before reporting on results. The full sample collected 1,210 responses across two batches. After review, 218 respondents were removed for completing the 33-question survey in under 2 minutes, a threshold consistent with SurveyMonkey’s own panel research guidance. The clean, validated sample reported throughout this study is n=992.
Appendix
Have you ever canceled an appointment or avoided booking with a doctor because of their online reviews?
Chart data
Patients who have canceled an appointment or avoided booking with a doctor because of online reviews. Sample size: n=992.
| Year | Yes | No | Sample |
|---|---|---|---|
| 2025 | 40% | 60% | n=992 |
| 2026 | 55% | 45% | n=992 |
Does seeing a healthcare provider’s response to online review influence your trust in them?
Chart data
Patients who say seeing a healthcare provider’s response to online review influences their trust. Sample size: n=992.
| Year | Agree | Disagree | Sample |
|---|---|---|---|
| 2025 | 42% | 58% | n=992 |
| 2026 | 66% | 34% | n=992 |
Have you ever used an AI tool to research or find a healthcare provider?
Chart data
Use of AI tools to research or find a healthcare provider. Sample size: n=992.
| Response | Percentage | Sample |
|---|---|---|
| Yes, I have used AI | 47% | n=992 |
| No, but I'm aware | 40% | n=992 |
| No, and not familiar | 13% | n=992 |
When reading online reviews, which specific complaint is most likely to make you avoid booking with a doctor? (Select up to 3)
Chart data
Specific complaints in online reviews most likely to make patients avoid booking with a doctor. Respondents could select up to three. Sample size: n=992.
| Complaint | Percentage | Sample |
|---|---|---|
| Rude/unhelpful staff | 52% | n=992 |
| Doctor didn't listen | 52% | n=992 |
| Substandard care/errors | 45% | n=992 |
| Long wait times | 41% | n=992 |
| Billing/insurance issues | 40% | n=992 |
| Scheduling difficulty | 20% | n=992 |
| Outdated facility | 11% | n=992 |
In the last 12 months, have you chosen or actively searched for a new doctor?
Chart data
Patients who chose or actively searched for a new doctor in the last 12 months. Sample size: n=992.
| Response | Percentage | Sample | Note |
|---|---|---|---|
| Choose a new doctor | 47% | n=992 | Part of the 72% in market for a new doctor. |
| Searched, didn't switch | 25% | n=992 | Part of the 72% in market for a new doctor. |
| Didn't search | 28% | n=992 | The 72% in-market group totals n=714 and forms the base for footnotes 7 and 8. |
When you begin an online search for a doctor, what do you usually search for first? (Select up to 2)
Chart data
What patients usually search for first when beginning an online search for a doctor. Respondents could select up to two. Searchers only, n=714.
| Search query | Percentage | Sample |
|---|---|---|
| Specialty near me | 55% | Searchers only, n=714 |
| Best/top-rated in a specialty | 34% | Searchers only, n=714 |
| A specific doctor's name | 32% | Searchers only, n=714 |
| Reviews for a practice | 26% | Searchers only, n=714 |
| Specialty + insurance name | 23% | Searchers only, n=714 |
In the last 12 months, which sources have most influenced your decision when choosing a new doctor? (Select up to 3)
Chart data
Sources that most influenced patients’ decisions when choosing a new doctor in the last 12 months. Respondents could select up to three. Searchers only, n=714.
| Source | Percentage | Sample | Note |
|---|---|---|---|
| Friends/family | 43% | Searchers only, n=714 | Respondents could select up to three. |
| Insurance website/portal | 43% | Searchers only, n=714 | Respondents could select up to three. |
| Review sites (HG, Vitals, WebMD) | 40% | Searchers only, n=714 | Respondents could select up to three. |
| AI tools (ChatGPT, Claude, etc.) | 36% | Searchers only, n=714 | Among switchers (respondents who chose a new doctor, n=463), AI was #1 at 39%. |
| Google search results | 34% | Searchers only, n=714 | Respondents could select up to three. |
| Doctor's referral | 32% | Searchers only, n=714 | Respondents could select up to three. |
| Social media | 19% | Searchers only, n=714 | Respondents could select up to three. |
| Voice assistant (Siri, Alexa) | 12% | Searchers only, n=714 | Respondents could select up to three. |
When searching for care on Google, which section of the results do you find most trustworthy?
Chart data
Google results sections that patients find most trustworthy when searching for care. Sample size: n=992.
| Google results section | Percentage | Sample |
|---|---|---|
| AI Overview | 37% | n=992 |
| Organic results (blue links) | 20% | n=992 |
| Don't use Google for care | 14% | n=992 |
| Local map pack | 13% | n=992 |
| Not sure/don't pay attention | 9% | n=992 |
| Sponsored results (ads) | 7% | n=992 |
How often do you leave online reviews for healthcare providers?
Chart data
How often patients leave online reviews for healthcare providers. Sample size: n=992.
| Review frequency | Percentage | Sample | Note |
|---|---|---|---|
| Sometimes | 35% | n=992 | Frequency of leaving online reviews for healthcare providers. |
| After every visit | 23% | n=992 | Frequency of leaving online reviews for healthcare providers. |
| Rarely | 21% | n=992 | Combined with Never: 42%, down from 56% in 2025 (-14pp). |
| Never | 21% | n=992 | Combined with Rarely: 42%, down from 56% in 2025 (-14pp). |
If your doctor asked you to leave a review, how likely would you be to do so?
Chart data
Likelihood of patients leaving a review if their doctor asked them to do so. Sample size: n=992.
| Likelihood | Percentage | Sample | Note |
|---|---|---|---|
| Very likely | 41% | n=992 | Part of the 68% at least somewhat likely. |
| Somewhat likely | 27% | n=992 | Part of the 68% at least somewhat likely. |
| Neutral | 20% | n=992 | Neutral response. |
| Unlikely | 9% | n=992 | Part of the 12% unlikely. |
| Very unlikely | 3% | n=992 | Part of the 12% unlikely. |
Have you ever used an AI tool to research or find a healthcare provider? (by age)
Chart data
AI tool use to research or find a healthcare provider by age group. Sample size: n=992.
| Age group | Used AI | Aware, not used | Not familiar | Sample |
|---|---|---|---|---|
| 18–29 | 28% | 67% | 5% | n=992 |
| 30–44 | 52% | 39% | 9% | n=992 |
| 45–60 | 64% | 26% | 10% | n=992 |
| 60+ | 10% | 54% | 36% | n=992 |
Compared to 12 months ago, do you trust AI-generated health information more, less, or about the same?
Chart data
Trust in AI-generated health information compared to 12 months ago. Sample size: n=992.
| Response | Percentage | Sample |
|---|---|---|
| Trust AI more | 43% | n=992 |
| About the same | 37% | n=992 |
| Trust AI less | 20% | n=992 |
How much do you trust AI-generated search results compared to standard Google search results when researching a healthcare provider?
Chart data
Trust in AI-generated search results compared to standard Google search results when researching a healthcare provider. Sample size: n=992.
| Year | Response | Percentage | Sample |
|---|---|---|---|
| 2025 | I trust AI more | 19% | n=992 |
| 2025 | I trust them about the same | 33% | n=992 |
| 2025 | I trust AI less | 16% | n=992 |
| 2025 | I don't trust AI | 11% | n=992 |
| 2025 | Not sure | 21% | n=992 |
| 2026 | I trust AI more | 27% | n=992 |
| 2026 | I trust them about the same | 28% | n=992 |
| 2026 | I trust standard results more | 25% | n=992 |
| 2026 | I don't trust either | 9% | n=992 |
| 2026 | Not sure | 11% | n=992 |
When you used AI to research providers, how did you use the information? Have you received incorrect provider info from AI?
Trusted AI summary without checking further
Encountered incorrect provider info from AI
Chart data
AI users’ responses to two separate questions about trusting AI summaries and encountering incorrect provider information. AI users only, n=465.
| Question | Response | Percentage | Sample |
|---|---|---|---|
| Trusted AI summary without checking further | Yes | 60% | AI users only, n=465 |
| Trusted AI summary without checking further | Verified or situation-dependent | 40% | AI users only, n=465 |
| Encountered incorrect provider info from AI | Yes | 66% | AI users only, n=465 |
| Encountered incorrect provider info from AI | No or not sure | 34% | AI users only, n=465 |
What is the main reason you continue to see your current primary care provider?
Chart data
Main reason patients continue to see their current primary care provider. Sample size: n=992.
| Reason | Percentage | Sample |
|---|---|---|
| They know my medical history | 39% | n=992 |
| They accept my insurance | 17% | n=992 |
| Trust/bedside manner | 17% | n=992 |
| Easy communication (portal, app) | 9% | n=992 |
| Location is convenient | 6% | n=992 |
| Haven't thought about switching | 5% | n=992 |
| No current PCP | 5% | n=992 |
| No response | 2% | n=992 |
Are you considering changing your healthcare provider in the next year?
Chart data
Whether patients are considering changing their healthcare provider in the next year. Sample size: n=992.
| Response | Percentage | Sample | Note |
|---|---|---|---|
| Yes, actively looking | 28% | n=992 | Apples-to-apples YoY comparison. |
| Yes, open to it if something better comes along | 31% | n=992 | Combined with actively looking, 59% are in market. |
| No, I'm satisfied with my current provider | 37% | n=992 | Not currently in market. |
| I don't currently have a regular provider | 4% | n=992 | No regular provider. |
Year-over-Year Comparison
| Ref | Metric | 2025 | 2026 | Change |
|---|---|---|---|---|
| 3 | Provider responses influence trust (Agree+) | 42% | 66% | +24pp |
| 4 | Used AI to research providers | 31% | 47% | +16pp |
| 2 | Canceled/avoided due to reviews | 40% | 55% | +15pp |
| 14 | Trust AI more than standard Google | 19% | 27% | +8pp |
| 10 | Rarely + Never leave reviews (participation gap) | 56% | 42% | −14pp |
| 17 | Considering switching providers (actively looking) | 28% | 28% | Flat* |
* “Actively looking” is the apples-to-apples comparison. Combined with “open to it,” 2026 = 59%.
Chart data
Year-over-year comparison of key patient survey metrics from 2025 to 2026.
| Ref | Metric | 2025 | 2026 | Change |
|---|---|---|---|---|
| 3 | Provider responses influence trust (Agree+) | 42% | 66% | +24pp |
| 4 | Used AI to research providers | 31% | 47% | +16pp |
| 2 | Canceled/avoided due to reviews | 40% | 55% | +15pp |
| 14 | Trust AI more than standard Google | 19% | 27% | +8pp |
| 10 | Rarely + Never leave reviews (participation gap) | 56% | 42% | −14pp |
| 17 | Considering switching providers (actively looking) | 28% | 28% | Flat* |
About rater8
rater8, the healthcare industry’s leader in reputation management, helps medical practices establish pervasive online visibility. The rater8 Visibility Engine (raVE) effortlessly gathers authentic reviews and real-time feedback from verified patients, all with the support of award-winning customer service. Based in the United States, rater8 is a rapidly growing healthtech innovator serving 25,000+ providers at practices and hospitals of all sizes and specialties.
For a free PDF of the report, please email [email protected].