Learning from errors in radiology to improve patient safety.
Bibliographic record
Abstract
OBJECTIVE: To determine the views and practices of trainees and consultant radiologists about error reporting. STUDY DESIGN: Cross-sectional survey. PLACE AND DURATION OF STUDY: Radiology trainees and consultant radiologists in four tertiary care hospitals in Karachi approached in the second quarter of 2011. METHODOLOGY: Participants were enquired as to their grade, sub-specialty interest, whether they kept a record/log of their errors (defined as a mistake that has management implications for the patient), number of errors they made in the last 12 months and the predominant type of error. They were also asked about the details of their department error meetings. All duly completed questionnaires were included in the study while the ones with incomplete information were excluded. RESULTS: A total of 100 radiologists participated in the survey. Of them, 34 were consultants and 66 were trainees. They had a wide range of sub-specialty interest like CT, Ultrasound, etc. Out of the 100 responders, 49 kept a personal record/log of their errors. In response to the recall of approximate errors they made in the last 12 months, 73 (73%) of participants recorded a varied response with 1 - 5 errors mentioned by majority i.e. 47 (64.5%). Most of the radiologists (97%) claimed receiving information about their errors through multiple sources like morbidity/mortality meetings, patients' follow-up, through colleagues and consultants. Perceptual error 66 (66%) were the predominant error type reported. Regular occurrence of error meetings and attending three or more error meetings in the last 12 months was reported by 35% participants. Majority among these described the atmosphere of these error meetings as informative and comfortable (n = 22, 62.8%). CONCLUSION: It is of utmost importance to develop a culture of learning from mistakes by conducting error meetings and improving the process of recording and addressing errors to enhance patient safety.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".