CoolFace
Datasetpublic

HaseebDev/hipaa_dental_tickets

Hipaa_Dental_Tickets (Synthetic B2B Dataset Preview) Add me on Discord: xomohappy for access support, delivery questions, or product questions about this premade commercial dataset. This is a premium, privacy-compliant, industry-safe synthetic dataset simulating HIPAA Dental Office Support Tickets for B2B applications. About this Dataset This dataset is generated programmatically using large language models combined with a strict data curation and validation… See the full description on the dataset page: https://huggingface.co/datasets/HaseebDev/hipaa_dental_tickets.

sourceHugging Facecc-by-nc-4.0updated 2mo agoView on Hugging Face
0likes33downloads
Dataset Card

HipaaDentalTickets (Synthetic B2B Dataset Preview)

Add me on Discord: `xomohappy` for access support, delivery questions, or product questions about this premade commercial dataset.

This is a premium, privacy-compliant, industry-safe synthetic dataset simulating HIPAA Dental Office Support Tickets for B2B applications.

About this Dataset

This dataset is generated programmatically using large language models combined with a strict data curation and validation layer.

  • Privacy-safe: Contains synthetic names, phone numbers, and company identifiers.
  • Structured and verified: Schema-validated, formatted, and deduplicated to reduce model overfitting risk.
  • Target AI use case: HealthTech AI startups building scheduling and patient intake agents.
  • Category: Healthcare

Schema Definition

Each record contains the following fields:

  • ticket_id (str): The pre-assigned unique Ticket ID (e.g. DENT-2026-XXXX)
  • patient_name (str): The pre-assigned Patient Name
  • patient_phone (str): The pre-assigned Patient Phone
  • inquiry_category (str): Billing, Scheduling, Medical Question, Emergency, or Insurance
  • patient_message (str): Detailed patient message with natural typos, colloquialisms, and expressions of concern
  • receptionist_response (str): A professional, polite, and helpful receptionist response adhering to HIPAA
  • clinical_notes (str): Fictional internal clinical notes for the dentist using standard terminology

Get the Commercial Version

Need a larger dataset for production fine-tuning? The matching private repo is HaseebDev/hipaa_dental_tickets-commercial and is available under a commercial license.

  • Payment: Lemon Squeezy hosted checkout.
  • Delivery: Lemon Squeezy checkout with secure commercial delivery after purchase
  • Typical commercial package: 10,000+ records, schema documentation, and quality audit report.

[Open the commercial access page](https://haseebdev.github.io/synthetic-data-checkout/commercial-access.html?niche=hipaa_dental_tickets&demo_repo=HaseebDev%2Fhipaa_dental_tickets&commercial_repo=HaseebDev%2Fhipaa_dental_tickets-commercial&checkout_url=https%3A%2F%2Fsyntheticdatasetlab.lemonsqueezy.com%2Fcheckout%2Fbuy%2F431fde4c-a3ff-4587-825b-7ec496b1e98e%3Fcheckout%255Bcustom%255D%255Bdataset%255D%3Dhipaa_dental_tickets%26checkout%255Bcustom%255D%255Bdemo_repo%255D%3DHaseebDev%252Fhipaa_dental_tickets%26checkout%255Bcustom%255D%255Bcommercial_repo%255D%3DHaseebDev%252Fhipaa_dental_tickets-commercial%26utm_source%3Dhuggingface%26utm_medium%3Ddataset_card%26utm_campaign%3Dhipaa_dental_tickets)

Quality Audit

Dataset Quality Report

Executive Summary

MetricValue
StatusPASS
Quality Score94/100
Records Audited100
NicheHIPAA Dental Office Support Tickets
CategoryHealthcare

Quality Gates

DimensionResult
Schema Conformance100.0% (100/100)
Duplicate Rows0
Duplicate Identity Values0
Non-Synthetic Phone Risk0
Email Address Count0
Max Fuzzy Similarity0.375
Average Words Per Descriptive Field145.03
Vocabulary Diversity0.846
Repeated Phrase Count1192
Domain Keyword Coverage0.7

Buyer Assurance

This dataset was checked with deterministic local tooling before publication. The audit verifies schema consistency, uniqueness, text depth, duplicate risk, repeated wording, domain terminology coverage, and obvious PII leakage patterns.

Findings

  • MINOR: 1192 repeated four-word phrase pattern(s) detected.