Physician Billing Integrity Analytics

ML anomaly detection across provincial physician claims with peer-group comparison. Review capacity redirected from complaint-driven to highest-signal cases.

Machine learning anomaly detection across the full provincial physician claims dataset, with peer-group comparison as the primary signal - what a physician bills relative to peers with similar patient mix and practice context. The system ranks candidates for human review rather than making autonomous findings, keeping reviewers in control while pointing them at the cases that warrant attention.

Review capacity redirected from complaint-driven to highest-signal cases.