Natural Language Processing Analysis of Australian Health Practitioner Disciplinary Tribunal Decisions, 1999-2026

Background: Australian health practitioners are regulated under the Health Practitioner Regulation National Law, with serious conduct matters referred to state and territory tribunals. Despite thousands of published tribunal decisions, no prior study has applied computational text analysis to this corpus. Manual coding has limited previous work to narrow slices of the problem -- typically one profession, one jurisdiction, or one misconduct type. Methods: Natural language processing was applied to 3,586 tribunal decisions (1999-2026) across all eight Australian jurisdictions. A multi-label text classifier (TF-IDF logistic regression) was trained on 367 annotated decisions to categorise misconduct across seven types. Outcomes were extracted via rule-based methods. Chi-square tests, Fisher's exact tests, and Mann-Kendall trend tests assessed associations and temporal trends. Benjamini-Hochberg false discovery rate correction was applied across 63 statistical tests. Classifier performance was evaluated on a held-out test set (n = 45) and error-adjusted prevalence estimates were computed using the Rogan-Gladen estimator. Results: Of 3,586 decisions, 2,428 (67.7%) involved disciplinary proceedings. Classifier performance varied by category (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 registration cancellation (40.2%). Significant increasing temporal trends were identified for boundary violations (tau = 0.296, p = 0.032), dishonesty/fraud (tau = 0.564, p < 0.001), professional conduct breaches (tau = 0.396, p = 0.004), and communication failures (tau = 0.667, p < 0.001). 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 pairwise effect sizes (Cramer's V up to 0.502). Conclusions: This study provides the first large-scale computational analysis of Australian health practitioner tribunal decisions, identifying temporal trends, jurisdictional variation, and misconduct-outcome associations across a corpus more than four times larger than any prior manual study. The significant jurisdictional variation raises questions about consistency of regulatory outcomes under the nationally uniform framework. The concentration of opioid medications in prescribing misconduct decisions is consistent with the documented burden of pharmaceutical opioid harm in Australia. Classifier limitations for rare misconduct categories (dishonesty/fraud, communication failures) mean that prevalence estimates for these types should be treated as lower bounds.