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AnnouncementJuly 25, 2026•United States

Quadline Systems Codifies Zero-PHI Model Training Policy Across All AI Workflows

Executive Summary

Quadline Systems has formalized a zero-PHI model training policy across all AI workflows, ensuring patient communications, clinical notes, and intake data are never fed into public LLMs or third-party training corpora.

UNITED STATES — July 25, 2026 — Quadline Systems today announced the formalization of its zero-PHI model training policy, codifying a safeguard that has governed its AI workflows since inception.

Under the policy, no protected health information — including patient communications, clinical notes, and intake data — is fed into public large language models or third-party training corpora. Patient data is processed on BAA-backed infrastructure and is not used to train external AI systems.

“Healthcare AI that trains on patient data without explicit, documented safeguards is a liability practice owners should not have to carry,” a Quadline Systems spokesperson said. “Zero-PHI model training is a structural property of our system, not a setting that can be toggled off.”

The policy applies across all AI workflows in the Practice Revenue Operating System, including the autonomous marketing coworker Sandra, conversational intake, and patient messaging. Patient interactions are processed for the purpose of serving that patient and are not retained for model improvement on external systems.

The policy is documented in the Quadline Systems HIPAA Compliance Framework and is available to practice partners. It underpins every deployment and is verified through the audit-ready infrastructure that records live system state.

About Quadline Systems — Quadline Systems builds the Patient Acquisition & Practice Revenue Operating System for healthcare practices on audit-ready, BAA-backed infrastructure.

Notice to Healthcare Providers & Readers:

The information provided in this press dispatch is for informational and educational purposes regarding practice revenue infrastructure, carrier messaging compliance, and digital intake standards. Statements regarding performance represent system design goals and typical operational outcomes. Individual practice results vary based on existing scheduling software, local search dynamics, and patient engagement workflow.

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