MedCore Clinic Network — Local AI Visibility for Healthcare
AI Was Sending Patients to Competitors — With Wrong Information
MedCore’s operations team discovered the problem through patient complaints. Multiple patients arrived at clinics during hours listed by ChatGPT that didn’t match actual schedules. Others asked about services discontinued two years earlier.
A comprehensive audit across all 12 locations revealed systemic issues. AI assistants drew from 23 outdated directory listings, 4 abandoned social profiles, and a WordPress site with zero location-specific structured data.
- Incorrect operating hours for 9 of 12 locations in AI responses
- Three locations had moved but AI still cited old addresses from cached directories
- No location-specific pages — all 12 clinics shared a single “Locations” page
- Zero MedicalBusiness, Physician, or HealthcareService schema on the entire site
- AI defaulted to “I don’t have enough information” for service-specific queries
- Competitor networks with better-structured data appeared in 85% of local AI queries
Local Healthcare GEO Across 12 Locations
AI Misinformation Audit
We queried 4 AI platforms with 96 location-specific queries (8 per location), scoring each on mention presence, factual accuracy, position, and sentiment. Traced every error back to its source — 23 outdated directories, 4 abandoned profiles, 7 stale review sites.
Location Pages & Medical Schema
Built 12 dedicated location pages with MedicalClinic schema, geo-coordinates, operating hours, insurance plans, specialties, and Physician schema for every doctor with credentials and languages spoken.
Healthcare FAQ Architecture
Created location-specific FAQ content for symptom-based, service-based, and logistics-based queries. Each FAQ reviewed by MedCore’s compliance team. MedicalCondition schema linked conditions to treating locations.
Review Management Pipeline
Consolidated 3,400+ reviews from Google, Healthgrades, Zocdoc into AggregateRating schema per location. Post-visit solicitation increased monthly volume by 340% while maintaining 4.7-star average.
AI Accuracy Monitoring & Rapid Correction
Twice-daily automated checks across all 12 locations scoring 8 data points. 24-hour SLA for critical inaccuracies (wrong address, emergency hours), 72-hour SLA for secondary errors.
Implementation Details
"""
local_healthcare_schema.py — MedicalBusiness + Physician schema
for multi-location clinic networks.
"""
import json
from typing import List, Dict
class ClinicSchemaBuilder:
def __init__(self, clinic_data: Dict):
self.data = clinic_data
def build_medical_business(self) -> Dict:
return {
"@context": "https://schema.org",
"@type": "MedicalClinic",
"name": self.data["name"],
"telephone": self.data["phone"],
"address": {
"@type": "PostalAddress",
"streetAddress": self.data["address"]["street"],
"addressLocality": self.data["address"]["city"],
"postalCode": self.data["address"]["zip"]
},
"geo": {
"@type": "GeoCoordinates",
"latitude": self.data["address"]["lat"],
"longitude": self.data["address"]["lng"]
},
"openingHoursSpecification": self._build_hours(),
"medicalSpecialty": [
{"@type": "MedicalSpecialty", "name": s}
for s in self.data["specialties"]
],
"aggregateRating": {
"@type": "AggregateRating",
"ratingValue": self.data["rating"],
"reviewCount": self.data["review_count"]
}
}
def build_physician_schemas(self) -> List[Dict]:
return [{
"@context": "https://schema.org",
"@type": "Physician",
"name": doc["name"],
"medicalSpecialty": doc["specialty"],
"qualification": doc["credentials"],
"availableLanguage": doc.get("languages", ["English"])
} for doc in self.data.get("physicians", [])]Measurable Impact
Measurement period: 6 months (July 2025 – January 2026)
“Patients were showing up at our old addresses because that’s what ChatGPT told them. We had a real patient safety concern. WebCore fixed the misinformation within weeks and built a system that keeps AI assistants permanently up to date. The 3.8x booking increase was a welcome bonus — eliminating patient confusion was the real win.”
— Dr. R.P., Chief Operating Officer, MedCore (NDA)
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