NLP Analysis of Australian Health Practitioner Disciplinary Tribunal Decisions, 1999–2026

Abstract Natural language processing was applied to 3,586 Australian health practitioner tribunal decisions (1999–2026) to identify patterns in professional misconduct, outcomes, and temporal trends at a scale impractical through manual analysis. A text classification approach categorised 2,428 disciplinary decisions across seven misconduct types with acceptable accuracy for the major categories (per-class F1 0.47–0.82). Boundary violations were the most prevalent misconduct type (30.2%), followed by dishonesty/fraud (29.7%) and professional conduct breaches (28.0%). Reprimand was the most common outcome (53.0%), followed by cancellation (40.2%). Significant increasing trends were identified for boundary violations, dishonesty/fraud, professional conduct breaches, and communication failures. Boundary violations were associated with higher cancellation odds (OR = 1.36, p < 0.001). Opioid medications appeared in 67% of prescribing misconduct decisions. Significant jurisdictional variation in both misconduct types and outcomes was observed, with large effect sizes between major jurisdictions. The findings provide an empirical foundation for monitoring disciplinary trends under the National Law.