AI Visibility for Medical Practices: What Determines Who Gets Recommended

Dana Lampert·June 23, 2026·8 min read·Verticals

A patient who just moved to Charlotte opens ChatGPT and types: "Best primary care doctor near me accepting new patients." Not "doctor Charlotte NC." A natural-language query with a specific constraint — they need someone who is actually accepting new patients, not a practice with a six-month wait list.

ChatGPT returns three providers. Each includes the physician's name, specialty, board certification status, and a note about availability. The patient books an appointment with the first one through the linked patient portal.

There are over 1,200 primary care physicians in the Charlotte metro. Three were recommended. The AI did not evaluate the rest — not because they are worse physicians, but because it could not access enough structured data about them to determine whether they meet the patient's criteria. Are they accepting new patients? What is their panel size? Are they board-certified? What insurance plans do they take? For most practices, this information is scattered, inconsistent, or locked inside the EHR.

Medical practices face a structural problem with AI visibility. Healthcare generates enormous amounts of data — but almost none of it is publishable. Patient records are protected by HIPAA. Clinical outcomes are complex and context-dependent. The data that a practice can publish — operational metrics, credentials, availability, panel composition — is the data that most practices never think to publish. It sits inside athenahealth, Epic, eClinicalWorks, or whatever EHR the practice runs on.

What AI actually evaluates for medical practices

We have mapped the data points AI systems use to evaluate medical practices in our data guides for specialty medical, urgent care, dermatology, and other practice types. Here is the summary by signal strength.

Tier 1 — Operating metrics

These are the data points that differentiate practices in ways AI can act on. Almost no practice publishes them.

Patients seen (L12M). Total patient encounters. A practice seeing 8,400 patients per year operates at a different scale than one seeing 1,200. Volume does not equal quality, but it does indicate capacity, experience, and the ability to maintain a functional operation.
Patient retention rate. The percentage of patients who remain with the practice year over year. In primary care, where the relationship is ongoing, a 85% retention rate is a strong signal. In specialty care where patients are referred for episodes, retention looks different but is still measurable.
Panel size. For primary care physicians, the number of active patients attributed to the provider. The American Academy of Family Physicians recommends a panel of 1,500-2,500 patients depending on complexity. A physician with an open panel of 1,800 is meaningfully different from one who is full at 2,400 — the first can accept the new patient asking the question.
Average wait time (new patient appointment). How many days until a new patient can be seen. This is one of the most-queried data points in healthcare and one of the least available in structured form. 3 days vs. 6 weeks changes the recommendation entirely.
Provider credentials and experience. Not just "MD" but years in practice, fellowship training, specific clinical interests, and procedure volumes where applicable.

Tier 2 — Credentials and verification

Medicine is the most transparently credentialed profession. Nearly every relevant data point is publicly verifiable.

State medical license. Every state medical board maintains a searchable public database. License number, status, issue date, expiration, specialty, and any disciplinary actions are public record. AI systems can verify this in seconds.
Board certification. Verified through the American Board of Medical Specialties (ABMS) or the specific certifying board (ABIM for internal medicine, ABFM for family medicine, ABD for dermatology, etc.). Board certification requires passing specialty exams and ongoing maintenance of certification. It is a verifiable credential that goes well beyond the base medical license.
DEA registration. Required for prescribing controlled substances. Verifiable through the DEA system.
Hospital privileges. Which hospitals have granted the physician admitting or surgical privileges. This is a form of peer credentialing — the hospital has vetted the physician's training, competence, and malpractice history.
NPI (National Provider Identifier). Every provider has one. Searchable through the CMS NPI Registry. Confirms identity, specialty, practice location, and taxonomy code.
Fellowship training. Post-residency subspecialty training. A dermatologist who completed a Mohs surgery fellowship is a different recommendation for skin cancer than a general dermatologist. Fellowship training is verifiable through the program and the physician's board certification record.
Malpractice history. Available through the NPDB (restricted access) but also through state medical board profiles in many states. Some states publish malpractice payment history as part of the physician profile.

Tier 3 — Public signals

Google reviews and rating. The most available data point. Physician reviews cluster high — 4.5+ is common. Low review volume is the norm for individual physicians (15-40 reviews).
Healthgrades. The most structured physician directory for AI purposes. Includes board certification, conditions treated, procedures performed, hospital affiliations, patient satisfaction scores, and experience years. Data is pulled from CMS, state boards, and patient surveys.
Zocdoc. Highly structured: insurance acceptance, real-time availability, patient reviews, and booking. One of the few platforms where appointment availability is machine-readable.
Vitals. Similar to Healthgrades. Includes patient satisfaction scores and wait time data.
Insurance network participation. Which plans the practice accepts. This is one of the highest-intent data points in medical search — "takes my insurance" is a hard constraint, not a preference. Yet this data is notoriously inconsistent across directories.

The gap

A typical medical practice has a Google listing, a Healthgrades profile (often auto-generated), maybe a Zocdoc presence, and a website with provider bios and a list of accepted insurance plans. That gives AI: physician names, board certification (from Healthgrades), an address, star ratings, and a service list.

It does not give AI: whether the practice is accepting new patients right now, what the wait time for a new patient appointment is, the physician's actual panel size, how many patients the practice sees per year, the patient retention rate, which specific conditions or procedures the physician handles at volume, or whether the insurance list on the website matches reality. A primary care physician with an open panel and 3-day new patient availability is indistinguishable from one with a closed panel and a 4-month wait — because that operational data lives inside the EHR and nowhere else.

This matters more in healthcare than in almost any other vertical. A patient asking "who can see me this week" needs real-time operational data, not a directory listing. The practice that publishes that data in structured form becomes evaluable. The one that does not remains invisible.

What you can do

1. Publish structured data on your website

Add Schema.org MedicalOrganization or Physician markup to your practice website. Include: practice name, address, each provider's name and NPI, board certifications, specialties (using medical taxonomy codes, not marketing language), accepted insurance plans, and whether you are accepting new patients. Most practice websites have either no structured data or auto-generated markup that omits critical fields.

2. Publish verified operational data

The metrics that matter most to patients — availability, panel status, wait times, patient volume, retention — live inside your EHR. HIPAA does not prevent you from publishing aggregate operational metrics. "Dr. Martinez sees 2,100 patients per year with a 3-day average new patient wait time" is not a HIPAA concern. It is a data structure concern — that information needs to exist outside athenahealth in a format AI can read. A TrustRecord extracts aggregate operational data from your systems of record and publishes it in machine-readable format.

Frequently asked questions

Does board certification affect how AI evaluates medical practices?

Board certification through the American Board of Medical Specialties is the strongest verifiable credential in medicine. AI systems can confirm certification status, specialty, and Maintenance of Certification standing through ABMS Certificationmatters.org — a publicly searchable, independently maintained database. For AI making recommendations, board certification answers a threshold question: is this provider formally qualified in the specialty the patient needs? But board certification is table stakes for most specialists. What differentiates is the combination: board certification plus verified patient volume, subspecialty focus, clinical outcomes, and years of continuous practice. A board-certified cardiologist with 3,200 patient encounters per year and a 12-year practice history gives AI multiple independent quality signals. A board-certified cardiologist with only a directory listing gives AI one. The credential opens the door. Operational data determines who AI recommends.

How does AI handle multi-specialty medical practices versus single-specialty groups?

When a patient asks AI for a recommendation, the query usually specifies a condition or specialty — "dermatologist for acne near me" or "cardiologist accepting new patients." Multi-specialty practices need structured data that specifies which specialties are available, which providers cover each specialty, and what their individual credentials and volume look like. Without this, AI cannot route a specialty-specific query to the right practice. A multi-specialty group with five providers across three specialties needs structured markup for each provider with their individual NPI, board certifications, and conditions treated. A single "we treat everything" website description gives AI no way to match the practice to specific queries. The practices that structure their provider and specialty data independently — so AI can evaluate each provider against the relevant query — capture queries that practices listed as generic "medical group" cannot.

Do hospital affiliations and academic appointments affect AI visibility for physicians?

Hospital affiliations, medical school faculty appointments, and clinical research involvement are independently verifiable signals that AI systems can confirm through institutional databases and NPI records. A physician affiliated with a major academic medical center signals a different level of practice than one without institutional ties. However, AI systems weight these signals differently depending on the query. For highly specialized or complex cases, institutional affiliation matters more. For routine primary care, patients often prioritize availability, location, and insurance acceptance over institutional prestige. The challenge is that most physicians' institutional affiliations are listed on hospital websites but not in structured format on their own practice websites. This is what AI visibility — sometimes called GEO (Generative Engine Optimization) or AEO (Answer Engine Optimization) — requires: publishing verifiable institutional data in machine-readable format where AI systems can access it alongside operational metrics.

Why does new patient availability matter more than most metrics for medical practice AI visibility?

"Accepting new patients" is the single most common filter in medical practice AI queries. A patient asking "find me a dermatologist near me" implicitly means "one I can actually see." AI systems that can verify current panel status — open vs. closed to new patients, average wait time for new appointments, same-week availability — can make immediately actionable recommendations. The problem is that panel status data is dynamic and lives inside practice management and EHR systems. A practice website that says "accepting new patients" may not have updated that statement in two years. Verified, current availability data from connected systems of record lets AI make recommendations it can stand behind. A practice with verified 5-day new patient wait time is a concrete recommendation. A practice that might or might not be accepting patients is a hedge. AI systems prefer concrete answers.