Physician Scores on a National Clinical Skills Examination as Predictors of Complaints to Medical Regulatory Authorities
Bibliographic record
Abstract
CONTEXT: Poor patient-physician communication increases the risk of patient complaints and malpractice claims. To address this problem, licensure assessment has been reformed in Canada and the United States, including a national standardized assessment of patient-physician communication and clinical history taking and examination skills. OBJECTIVE: To assess whether patient-physician communication examination scores in the clinical skills examination predicted future complaints in medical practice. DESIGN, SETTING, AND PARTICIPANTS: Cohort study of all 3424 physicians taking the Medical Council of Canada clinical skills examination between 1993 and 1996 who were licensed to practice in Ontario and/or Quebec. Participants were followed up until 2005, including the first 2 to 12 years of practice. MAIN OUTCOME MEASURE: Patient complaints against study physicians that were filed with medical regulatory authorities in Ontario or Quebec and retained after investigation. Multivariate Poisson regression was used to estimate the relationship between complaint rate and scores on the clinical skills examination and traditional written examination. Scores are based on a standardized mean (SD) of 500 (100). RESULTS: Overall, 1116 complaints were filed for 3424 physicians, and 696 complaints were retained after investigation. Of the physicians, 17.1% had at least 1 retained complaint, of which 81.9% were for communication or quality-of-care problems. Patient-physician communication scores for study physicians ranged from 31 to 723 (mean [SD], 510.9 [91.1]). A 2-SD decrease in communication score was associated with 1.17 more retained complaints per 100 physicians per year (relative risk [RR], 1.38; 95% confidence interval [CI], 1.18-1.61) and 1.20 more communication complaints per 100 practice-years (RR, 1.43; 95% CI, 1.15-1.77). After adjusting for the predictive ability of the clinical decision-making score in the traditional written examination, the patient-physician communication score in the clinical skills examination remained significantly predictive of retained complaints (likelihood ratio test, P < .001), with scores in the bottom quartile explaining an additional 9.2% (95% CI, 4.7%-13.1%) of complaints. CONCLUSION: Scores achieved in patient-physician communication and clinical decision making on a national licensing examination predicted complaints to medical regulatory authorities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".