Characteristics and contributing factors of diagnostic error in surgery: analysis of closed medico-legal cases and complaints in Canada
Bibliographic record
Abstract
Background: Diagnostic errors lead to patient harm; however, most research has been conducted in nonsurgical disciplines. We sought to characterize diagnostic error in the pre-, intra-, and postoperative surgical phases, describe their contributing factors, and quantify their impact related to patient harm. Methods: We performed a retrospective analysis of closed medico-legal cases and complaints using a database representing more than 95% of all Canadian physicians. We included cases if they involved a legal action or complaint that closed between 2014 and 2018 and involved a diagnostic error assigned by peer expert review to a surgeon. Results: We identified 387 surgical cases that involved a diagnostic error. The surgical specialties most often associated with diagnostic error were general surgery (n = 151, 39.0%), gynecology (n = 71, 18.3%), and orthopedic surgery (n = 48, 12.4%), but most surgical specialties were represented. Errors occurred more often in the postoperative phase (n = 171, 44.2%) than in the pre- (n = 127, 32.8%) or intra-operative (n = 120, 31.0%) phases of surgical care. More than 80% of the contributing factors for diagnostic errors were related to providers, with clinical decision-making being the principal contributing factor. Half of the contributing factors were related to the health care team (n = 194, 50.1%), the most common of which was communication breakdown. More than half of patients involved in a surgical diagnostic error experienced at least moderate harm, with 1 in 7 cases resulting in death. Conclusion: In our cohort, diagnostic errors occurred in most surgical disciplines and across all surgical phases of care; contributing factors were commonly attributed to provider clinical decision-making and communication breakdown. Surgical patient safety efforts should include diagnostic errors with a focus on understanding and reducing errors in surgical clinical decision-making and improving communication.
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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 | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".