White Paper · Version 0.4
The Dental RCM Error Taxonomy
A technical framework for AI-assisted detection of billing, eligibility, treatment-plan, claim, payment-posting, and EOB inconsistencies in dental revenue cycle workflows.
Gaurav Basra · CEO of Basra Consulting Services / 1DentalAI.com
Research Summary
Dental RCM errors usually become expensive because they are found late: after patient communication, claim submission, payer response, payment posting, or patient billing.
This framework organizes 30 recurring error patterns across six workflow stages so software and staff can identify high-probability inconsistencies before they become denials, write-offs, disputes, or rework.
The operating principle is AI-assisted review with human approval. The taxonomy is designed to support detection, prioritization, audit evidence, and review queues, not autonomous billing decisions.
Operational Applications
Treatment-plan quality checks before estimates are shown to patients.
Eligibility and benefits review for exhausted maximums, waiting periods, frequencies, downgrades, and coordination-of-benefits uncertainty.
Claim-readiness review for attachments, narratives, provider identifiers, duplicate submissions, and date-of-service consistency.
ERA/EOB reconciliation for allowed-amount mismatch, patient-responsibility mismatch, and duplicate payment posting.
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