AI Integration for ezyVet Insurance Claims

The integration connects at two primary surfaces within ezyVet: the patient medical record and the billing/invoicing module. When a clinician finalizes a visit, the AI agent is triggered via ezyVet's API or a configured automation rule. It extracts the necessary data payload, which typically includes: the patient's Pet ID, Client ID, diagnosis codes, procedure codes from the invoice, clinical notes from the SOAP record, and any attached lab results or imaging reports. This structured and unstructured data forms the basis for the automated claim draft.

The core AI workflow performs three sequential tasks: 1) Form Population – mapping the extracted ezyVet data to the correct fields on the target insurer's claim form (e.g., Trupanion, Nationwide). 2) Document Assembly – identifying and attaching the required supporting medical records from the patient's file, which may involve fetching PDFs from the document manager. 3) Status Tracking – creating a custom object or note within ezyVet to log the submission ID, timestamp, and later, polling for adjudication status updates from the insurer's portal via API to update the record. This turns a 15-20 minute manual process into a task that completes in seconds, with a human-in-the-loop review step before final submission.

Rollout is typically phased, starting with a single high-volume insurer and a pilot group of veterinarians. Governance is critical: the AI's output should always route to a designated Insurance Claim Review queue in ezyVet for a staff member to verify accuracy and add any non-standard notes before submitting. This ensures compliance and maintains the veterinarian's final authority. The system also maintains a full audit trail, logging every data access, generation, and submission event for traceability. For practices, the impact is operational consistency and a shift in staff time from administrative data entry to higher-value client communication and follow-up on complex or denied claims.

The integration is built on ezyVet's REST API and webhook system, triggered by status changes in the Insurance Claim object. When a claim is marked 'Ready for Submission' or a new claim form is created, the system captures the claim ID, patient record, and associated clinical notes. This payload is securely queued (e.g., via AWS SQS or Azure Service Bus) to ensure reliability and handle retries if the AI service is temporarily unavailable. The core AI processing—extracting data from clinical notes, populating the ClaimForm fields, and identifying required attachments like lab reports or radiographs—occurs in a dedicated, isolated service layer. This design keeps logic and API keys outside of ezyVet's database for security and maintainability.

Post-processing, the AI service calls back to ezyVet's API to attach the populated PDF form to the claim record and link the identified supporting documents from the patient's file. For governance, every AI action is logged with a trace ID in a separate audit database, recording the source data, the model's reasoning (via logprobs or a summary), the user who triggered the action, and the final output. This creates a clear lineage for compliance reviews. The system can be configured for human-in-the-loop approval; for example, all claims over a certain dollar amount or for specific insurers can be routed to a billing coordinator's queue in ezyVet for final review before the status is auto-updated to 'Submitted'.

Rollout typically follows a phased approach: starting with a single practice location and a subset of straightforward claim types (e.g., wellness plans). During this pilot, the AI's field population accuracy is monitored against manual benchmarks. The architecture supports A/B testing by routing a percentage of claims through a shadow mode where AI output is generated but not applied, allowing for performance validation without affecting live operations. This event-driven, API-first design ensures the integration is non-invasive, scalable across multi-location practices, and can be extended to other automations, such as denial prediction or follow-up tracking. For a deeper look at connecting AI to veterinary data models, see our guide on AI Integration for Veterinary EHR Systems.

Insurance claim processing involves sensitive Protected Health Information (PHI) and financial data. Our integration architecture is designed to keep this data within your ezyVet environment. We use ezyVet's secure APIs to fetch only the necessary patient, visit, and invoice data for a specific claim. The AI processing—form filling, record selection, and status updates—occurs in a secure, isolated environment. All data exchanges are encrypted, and no PHI is stored in long-term external vector databases unless explicitly configured for your private instance. Audit logs track every AI-generated suggestion, form field populated, and document attached, creating a clear lineage from the original medical record to the submitted claim.

A successful rollout follows a phased, risk-managed approach:

• Phase 1: Draft Generation & Human-in-the-Loop. AI generates a complete claim draft within a dedicated ezyVet interface or queue. A claims coordinator reviews, edits, and submits it. This validates accuracy and builds team confidence.
• Phase 2: Conditional Automation. Rules are established (e.g., auto-submit for routine wellness claims under a certain dollar amount). The system flags exceptions for manual review based on diagnosis codes, invoice amounts, or missing records.
• Phase 3: Full Workflow Orchestration. AI manages the end-to-end process: triggering on invoice finalization, gathering records, populating the carrier-specific form, submitting via the payer's portal (using approved credentials), and logging the submission status back to the ezyVet patient record. Even at this stage, random audits and threshold-based alerts ensure ongoing quality control.

Governance is built into the workflow. You define which users or roles can approve AI-generated claims before submission. The system can be configured to require a veterinarian's co-signature on clinical notes used for claims. Performance is continuously monitored using key metrics like first-pass acceptance rate, time-to-submission, and manual touch rate. This data, visible in a dashboard or fed back into ezyVet reporting, allows you to measure ROI and refine the AI's logic, ensuring the integration scales safely and delivers consistent value. For related architectural patterns, see our guide on AI Integration for Veterinary EHR Systems.