Should electronic differential diagnosis support be used early or late in the diagnostic process? A multicentre experimental study of Isabel
Bibliographic record
Abstract
BACKGROUND: Diagnostic errors unfortunately remain common. Electronic differential diagnostic support (EDS) systems may help, but it is unclear when and how they ought to be integrated into the diagnostic process. OBJECTIVE: To explore how much EDS improves diagnostic accuracy, and whether EDS should be used early or late in the diagnostic process. SETTING: 6 Canadian medical schools. A volunteer sample of 67 medical students, 62 residents in internal medicine or emergency medicine, and 61 practising internists or emergency medicine physicians were recruited in May through June 2020. INTERVENTION: Participants were randomised to make use of EDS either early (after the chief complaint) or late (after the complete history and physical is available) in the diagnostic process while solving each of 16 written cases. For each case, we measured the number of diagnoses proposed in the differential diagnosis and how often the correct diagnosis was present within the differential. RESULTS: EDS increased the number of diagnostic hypotheses by 2.32 (95% CI 2.10 to 2.49) when used early in the process and 0.89 (95% CI 0.69 to 1.10) when used late in the process (both p<0.001). Both early and late use of EDS increased the likelihood of the correct diagnosis being present in the differential (7% and 8%, respectively, both p<0.001). Whereas early use increased the number of diagnostic hypotheses (most notably for students and residents), late use increased the likelihood of the correct diagnosis being present in the differential regardless of one's experience level. CONCLUSIONS AND RELEVANCE: EDS increased the number of diagnostic hypotheses and the likelihood of the correct diagnosis appearing in the differential, and these effects persisted irrespective of whether EDS was used early or late in the diagnostic process.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".