Tracewell
An inference-level audit trail that binds each clinical AI interaction to human identity, model context, policy action, and downstream system effect in a tamper-evident record.
Health systems can often prove that a clinician accessed an application, but not exactly what a clinical AI received, returned, and caused during that session. Network and access logs stop at the boundary where medico-legal questions begin: the prompt, response, model version, human identity, policy decision, and downstream clinical action. When an interaction is challenged, the compliance team reconstructs the most consequential evidence from systems that were never designed to preserve one coherent event.
The health-system compliance or risk owner accountable for clinical AI use and for producing defensible evidence when an interaction is questioned.
The idea reconciles broader clinical-AI use with the evidence required to investigate a consequential interaction.
Current breach-cost and audit-field signals create a live driver without one decisive dated mandate.
Several cross-references and inbound links support an agent-audit family, though only within one vertical.
The product resolves a sharp tension between clinical-AI adoption and the absence of inference-level, clinician-bound evidence, in a blind spot left by access-level SIEM and HIPAA tooling.
The timing rests on a live cost and category signal rather than a single mandate, and any claim that the record is court-admissible requires jurisdiction-specific evidentiary validation.
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