Nature and outcomes of sanctioned medical misconduct in six international jurisdictions: a case series
Bibliographic record
Abstract
Objective The aim of this study was to determine the types of medical misconduct, the practitioner, specialities and jurisdictions at risk, patient outcomes and the sanctions imposed. Methods This study was a retrospective case series of 822 adverse medical tribunal determinations in Australia, New Zealand, Canada (Ontario, Alberta), Pennsylvania (USA), Singapore and Hong Kong in 2013-17. Results Inappropriate medical care and illegal or unethical prescribing were the most common types of misconduct. Misconduct varied with practitioner sex, international medical graduate status, speciality and jurisdiction (P<0.05). Cases of inappropriate medical care were more common in Singapore (46.7% of all Singapore cases; 95% confidence interval (CI) 31.9-62.0) and among surgeons (47.6% of all surgeon cases; 95% CI 36.5-58.8). Illegal or unethical prescribing was more common in Australia (31.1%; 95% CI 24.8-38.2) and among general or family practitioners (26.9%; 95% CI 20.0-35.0). Misconduct not related to patients was more common in Pennsylvania (30.3%; 95% CI 25.2-36.0) and among local graduates (20.5%; 95% CI 17.1-24.5). Sexual misconduct was more common in Australia (29.6%; 95% CI 23.4-36.6) and among males (19.6%, 95% CI 16.7-22.8). Healthcare dishonesty was more common in Hong Kong (21.8%; 95% CI 14.0-32.2) and among surgeons (13.4%; 95% CI 7.2-23.2). The most common patient outcomes were patient risk (40.6%; 95% CI 36.1-45.4) and death and actual physical harm combined (31.2%; 95% CI 26.9-35.7). Sanctions were most commonly suspension or deregistration. Deregistration was most common in cases of sexual misconduct. Conclusion Medical misconduct varies widely. Risk factors for particular misconduct types are apparent among jurisdictions and practitioner characteristics. The nature of patient harm varied by type of misconduct, with illegal unethical prescribing commonly leading to drug dependency and sexual misconduct leading to psychiatric injury. What is known about the topic? Medical misconduct is a continuing problem. Tribunals and medical boards sanction misconduct to protect patient safety and public trust. What does this paper add? Tribunals and boards differ in misconduct reporting and permitting public access to determinations. Types of misconduct vary between international jurisdictions, practitioner sex, international graduate status and speciality. Risk and physical injury (including death) are the most common patient outcomes. The nature of patient harm varied by type of misconduct, with illegal unethical prescribing commonly leading to drug dependency and sexual misconduct leading to psychiatric injury. What are the implications for practitioners? Medical colleges should tailor trainee programs to address the common types of misconduct within their specialities. Standardisation of misconduct reporting, and report access, across jurisdictions would facilitate ongoing surveillance and intervention evaluation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".