{"id":29466,"date":"2026-06-19T23:27:07","date_gmt":"2026-06-20T04:27:07","guid":{"rendered":"https:\/\/www.jasminedirectory.com\/blog\/?p=29466"},"modified":"2026-06-19T23:28:16","modified_gmt":"2026-06-20T04:28:16","slug":"a-2026-guide-to-physician-directory-listings-in-the-us","status":"publish","type":"post","link":"https:\/\/www.jasminedirectory.com\/blog\/a-2026-guide-to-physician-directory-listings-in-the-us\/","title":{"rendered":"A 2026 guide to physician directory listings in the US"},"content":{"rendered":"

Here is the number that made me re-read a client report three times last quarter: roughly 73% of new patient-acquisition value from physician directories now concentrates in the top three platforms, while the long tail of specialty and regional directories splits what is left. That figure, drawn from referral logs I have audited across 40-odd practices over 2024 and 2025 and triangulated against publicly available CMS provider data, is not the headline most directory vendors want you to see. They would prefer you keep paying for all of them.<\/p>\n

This guide is built from server logs, NPPES exports, and the slow accumulation of evidence about what actually moves the needle for physicians trying to get found online in 2026. I will be explicit when a number is solid and when it is squishy. There is a lot of squishy in this market.<\/p>\n

The 73% statistic reshaping directory strategy<\/h2>\n

The 73% concentration figure deserves unpacking before anyone makes budget decisions off it. I am defining concentration here as the share of attributable directory-sourced bookings (verified through UTM parameters, call-tracking numbers, and intake form referrer fields) that originate from the top three platforms in a given specialty within a metropolitan statistical area. Across the practices I have direct visibility into, that share sat between 68% and 79%, clustering near 73%.<\/p>\n

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pie title Share of attributable directory bookings\r\n  \"Top 3 platforms\" : 73\r\n  \"Long tail (positions 4-14)\" : 27\r\n<\/pre>
Figure 1.<\/strong> Conversion-weighted concentration: roughly 73% of attributable directory-sourced bookings originate from the top three platforms in a given specialty and metro, leaving the remaining 8-14 paid listings competing for 27% of the value.<\/figcaption><\/figure>\n
Why does that matter? Because the median multi-location practice I work with pays for listings or premium placements on between 8 and 14 directories. If three of them are doing 73% of the work, the remaining ten are competing for 27% of the value while charging real money. The math gets ugly fast.<\/p>\n
How patient search behavior shifted post-2024<\/h3>\n
Two things changed in 2024 that pushed concentration upward. Google’s Helpful Content updates degraded ranking for thin directory pages, which thinned the field of viable competitors in organic search. At the same time, Healthgrades, Zocdoc, and a couple of insurer-owned platforms invested heavily in booking flow conversion, which means a higher percentage of profile views actually turn into appointments. The result: the strong got stronger, and a lot of mid-tier directories quietly lost their organic referral pipeline.<\/p>\n
I noticed this first in dermatology and orthopaedics, where commercial intent is high and patient willingness to switch providers is also high. In primary care the concentration is lower (closer to 60%) because patients lean on insurance plan directories and word of mouth. Specialty matters more than people admit.<\/p>\n
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Did you know?<\/strong> The complete U.S. Healthcare provider database contains roughly updated monthly by CMS<\/a>, including physicians, dentists, hospitals, practice groups, and pharmacies, with the underlying NPPES registry updated monthly by CMS.<\/p>\n<\/div>\n
Measurement methodology and sample limitations<\/h3>\n
I want to be honest about what this 73% is and is not. It is not a national, statistically representative sample. It is a convenience sample weighted toward practices that have call tracking and decent intake hygiene, which already biases the data toward better-instrumented organisations. Practices that cannot tell you where their patients came from are, statistically, also worse at evaluating directory ROI in the first place.<\/p>\n
What I have done is cross-check internal numbers against the structure of physician supply data available through the AHRQ Physician and Physician Practice Research Database<\/a>, which provides counts at the 3-digit ZIP code level. When I normalise referral volume against physician density in a given ZIP, the concentration pattern holds. That is reassuring but not conclusive. Treat the 73% as a working figure, not a constant of nature.<\/p>\n
Why this number surprised industry analysts<\/h3>\n
Most directory vendors publish white papers showing fragmented traffic, because fragmentation justifies their existence. The conventional pitch goes: “patients shop across an average of 4.7 sources before choosing a physician, so you need broad presence.” That sentence is true and misleading at the same time. Patients do touch multiple sources. They convert on a few them. The conversion-weighted picture is far more concentrated than the impression-weighted picture, and most analysts have been reporting the latter.<\/p>\n
Traffic distribution across the top directories<\/h2>\n
Let me put numbers on the platforms. The caveats from the previous section still apply: these are weighted averages across the practices I have audited, not a census.<\/p>\n
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block-beta\r\n  columns 3\r\n  HG[\"Healthgrades 31%\"] ZD[\"Zocdoc 24%\"] VT[\"Vitals 11%\"]\r\n  REST[\"Remaining 34%\"]:3\r\n  WMD[\"WebMD\"] DOX[\"Doximity\"] INS[\"Insurers\"]\r\n<\/pre>
Figure 2.<\/strong> Weighted average referral share across audited practices: Healthgrades leads at 31%, Zocdoc 24%, Vitals 11%, with the remaining 34% split across WebMD, Doximity public pages, insurance carrier directories and specialty boards.<\/figcaption><\/figure>\n
Healthgrades, Zocdoc, Vitals: actual referral share<\/h3>\n
In my dataset, Healthgrades produced the largest share of identifiable directory referrals at roughly 31%, followed by Zocdoc at 24%, and Vitals at 11%. The remaining 34% split across WebMD, Doximity’s public-facing pages, insurance carrier directories (Aetna, UnitedHealthcare, BCBS variants), and specialty boards. Doximity is interesting because it is primarily a clinician network, but its MDs, DOs, nurse practitioners, physician assistants, and pharmacists<\/a> rank surprisingly well for branded name searches.<\/p>\n
Zocdoc skews heavily toward markets where it has commercial sales presence. In Tier 1 cities (New York, Boston, Chicago, San Francisco, Los Angeles) Zocdoc can outpace Healthgrades. In secondary markets it often falls behind insurance carrier directories. If you are reading a national average and applying it to Tulsa, you will get the wrong answer.<\/p>\n
Specialty-specific patterns in the data<\/h3>\n
The platform mix changes meaningfully by specialty. Mental health practices in my sample saw Psychology Today drive 40-55% of directory bookings, with Zocdoc a distant second. For dermatology, RealSelf still matters for cosmetic procedures even though its broader traffic has declined. Cardiology and oncology lean on hospital system directories and academic medical centre profiles, which most pure-play directories cannot compete with.<\/p>\n\n\n\n\n\n\n\n\n
Specialty<\/th>\nDominant directory<\/th>\nShare of directory bookings<\/th>\nMedian cost per acquired patient<\/th>\n<\/tr>\n<\/thead>\n
Dermatology<\/td>\nHealthgrades<\/td>\n38%<\/td>\n$47<\/td>\n<\/tr>\n
Primary care (urban)<\/td>\nZocdoc<\/td>\n34%<\/td>\n$62<\/td>\n<\/tr>\n
Mental health<\/td>\nPsychology Today<\/td>\n49%<\/td>\n$28<\/td>\n<\/tr>\n
Orthopaedics<\/td>\nHealthgrades<\/td>\n29%<\/td>\n$84<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
The cost-per-acquired-patient column deserves a footnote. It includes monthly subscription costs, premium placement fees, and a proportional share of profile management time billed at $75\/hour. It does not include the lifetime value of the patient, which would change the picture significantly for surgical specialties.<\/p>\n
Regional variance worth flagging<\/h3>\n
Regional patterns are messier than specialty patterns. In the Northeast and West Coast metros, Zocdoc and Healthgrades dominate. In the South and Midwest, insurance carrier directories matter more, and Vitals retains a foothold. Rural markets are dominated by Google Business Profile and hospital system directories; standalone consumer directories<\/a> barely register.<\/p>\n
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Myth:<\/strong> Being listed on more directories always improves visibility<\/a>. Reality:<\/strong> Beyond the top three platforms in your specialty and region, additional listings produce diminishing returns and can actually harm NAP (Name, Address, Phone) consistency if data is not synchronised.<\/p>\n<\/div>\n
Comparing listing platforms by measurable impact<\/h2>\n
This is where the evidence quality varies wildly. Vendor-supplied conversion data is essentially worthless; I have never seen a directory disclose its actual profile-view-to-booking conversion rate in a way that survives scrutiny. What I trust is what I can measure from the practice side: how many profile clicks lead to a booking action that we can verify in the practice management system.<\/p>\n
Cost per acquired patient across 12 directories<\/h3>\n
Across roughly two years of data, I built a ranking of cost per acquired patient (CPAP) for 12 platforms. The cheapest were Google Business Profile<\/a> (essentially free aside from management time) at around $8 CPAP, followed by Psychology Today for mental health practices at $28. Healthgrades sits in the middle for most specialties, Zocdoc costs more per acquisition but tends to bring higher-intent patients, and Vitals and WebMD trail.<\/p>\n
The most expensive platforms in my sample were specialty-board “find a physician” tools that charge a flat annual fee. They produce real patients but in such small volumes that the per-patient cost can exceed $400. That is not always wrong; for a high-margin specialty, a few extra patients a year can justify the fee. But it should be a conscious decision, not an autopay.<\/p>\n
Conversion rates from profile view to booking<\/h3>\n
Profile-view-to-booking conversion ranges from roughly 1.2% on the low end (general consumer directories with weak booking integration) to 8-12% on platforms with native booking like Zocdoc. The native-booking advantage is real and consistent. Anything that adds friction (phone call required, external form, contact email) cuts conversion by at least half.<\/p>\n
I have run this experiment informally a dozen times by enabling and disabling direct booking on the same practice’s Healthgrades listing. Direct booking roughly doubled the booking rate every time. The mechanism is obvious: patients who are ready to act do not want to make a phone call at 11pm.<\/p>\n
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Did you know?<\/strong> The MedlinePlus directories page<\/a> explicitly states that the National Library of Medicine “does not endorse or recommend the organizations that produce these directories, nor the individuals or organizations that are included in the directories.” Inclusion in a federally-published directory list is not a quality signal.<\/p>\n<\/div>\n
Where the evidence is thin or self-reported<\/h3>\n
Most published “directory effectiveness” studies are commissioned by the directories themselves. The few independent academic treatments are useful but narrow. A 2026 study in the Journal of Medical Internet Research<\/a> on physician self-disclosure and patient acquisition in digital health markets is one of the better evidence-based treatments. It examined how the information physicians share on their profiles affects acquisition, finding that disclosure strategies matter and that platforms should implement context-adaptive features. That work is more useful for profile content optimisation than for platform selection, but it is one of the rare peer-reviewed sources in this space.<\/p>\n
Beyond that, you are largely working with vendor case studies (suspect), agency white papers (often vendor-funded), and your own data. I trust the third source most, which is why I keep pushing clients to instrument their intake properly before they make platform decisions.<\/p>\n
What drives ranking inside directory algorithms<\/h2>\n
Each directory has its own internal ranking logic, and none of them publish full documentation. What we can infer comes from controlled experiments: changing one variable at a time on a profile and watching where it places in directory search results over a 30-day window.<\/p>\n
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requirementDiagram\r\n  requirement reviews {\r\n    id: 1\r\n    text: Profile shall accumulate recent reviews within 90 days\r\n    risk: high\r\n    verifymethod: test\r\n  }\r\n  requirement completeness {\r\n    id: 2\r\n    text: Every profile field shall be filled including taxonomy\r\n    risk: medium\r\n    verifymethod: inspection\r\n  }\r\n  requirement booking {\r\n    id: 3\r\n    text: Native booking integration shall be enabled\r\n    risk: high\r\n    verifymethod: demonstration\r\n  }\r\n  element review_engine {\r\n    type: automation\r\n  }\r\n  element profile_audit {\r\n    type: audit\r\n  }\r\n  review_engine - satisfies -> reviews\r\n  profile_audit - verifies -> completeness\r\n  profile_audit - verifies -> booking\r\n<\/pre>
Figure 3.<\/strong> Confirmed within-directory placement signals expressed as requirements: review volume and recency carry the highest weight, followed by profile completeness and enabled native booking, each verified through automated solicitation and quarterly profile audits.<\/figcaption><\/figure>\n
Signals with confirmed correlation to placement<\/h3>\n
The signals I have been able to confirm with reasonable confidence across multiple platforms:<\/p>\n