Why verifying diagnostic decisions with a checklist can help: insights from eye tracking
Bibliographic record
Abstract
Making a diagnosis involves ratifying or verifying a proposed answer. Formalizing this verification process with checklists, which highlight key variables involved in the diagnostic decision, is often advocated. However, the mechanisms by which a checklist might allow clinicians to improve their verification process have not been well studied. We hypothesize that using a checklist to verify diagnostic decisions enhances analytic scrutiny of key variables, thereby improving clinicians' ability to find and fix mistakes. We asked 16 participants to verify their interpretation of 12 electrocardiograms, randomly assigning half to be verified with a checklist and half with an analytic prompt. While participants were verifying their interpretation, we tracked their eye movements. We analyzed these eye movements using a series of eye tracking variables theoretically linked to analytic scrutiny of key variables. We found that more errors were corrected using a checklist compared to an analytic prompt (.27 ± .53 errors per ECG vs. .04 ± .43, F 1,15 = 8.1, p = .01, η (2) = .20). Checklist use was associated with enhanced analytic scrutiny in all eye tracking measures assessed (F 6,10 = 6.0, p = .02). In this experiment, using a key variable checklist to verify diagnostic decisions improved error detection. This benefit was associated with enhanced analytic scrutiny of those key variables as measured by eye tracking.
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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 | 0.017 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".