Back to the future: Can conversation analysis be used to judge physicians’ malpractice history?
Bibliographic record
Abstract
In its monograph Crossing the Quality Chasm, the Institute of Medicine asserted that 44,000 to 98,000 lives are lost every year due to avoidable medical errors, more than 80% of which involved breakdowns in communication. Medical malpractice claims also involve errors that cause harm, including death. Reasons for malpractice claims have been investigated using variables such as age, race, country of origin, and gender none of which are predictive. One promising area that has not systematically been studied is the role of face-to-face communication in malpractice claims. To better understand this phenomenon, we tape-recorded 125 doctors (divided equally between surgeons and primary care practitioners), each with 10 consecutive patients. Half of these doctors had been sued at least twice, while the rest had never been sued. We then did a qualitative analysis based on a single taped encounter per doctor using conversation analysis (CA), in order to try to identify which doctors had claims or no-claims histories. While we were able to identify two out of every three no-claims primary care doctors, we were much less successful in identifying those with claims. Surprisingly, in the surgeon group, predictions based on CA were worse than by chance probability. We discuss the implications of our findings for the field of outcome-based communication analysis.
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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 | 0.030 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".