Debiasing and Educational Interventions in Medical Diagnosis: A Systematic Review
Bibliographic record
Abstract
Background: The prevalence of cognitive bias and its contribution to diagnostic errors has been documented in recent research. Debiasing interventions or educational initiatives are key in reducing the effects and prevalence of cognitive biases, contributing to the prevention of diagnostic errors. The objectives of this review were to 1) characterize common debiasing strategies implemented to reduce diagnosis-related cognitive biases, 2) report the cognitive biases targeted, and 3) determine the effectiveness of these interventions on diagnostic accuracy. Methods: Searches were conducted on April 25, 2022, in MEDLINE, EMBASE, Healthstar, and PsycInfo. Studies were included if they presented a debiasing intervention which aimed to improve diagnostic accuracy. The Rayyan review software was used for screening. Quality assessments were conducted using the JBI Critical Appraisal Tools. Extraction, quality assessment and analysis were recorded in Excel. Results: Searches resulted in 2232 studies. 17 studies were included in the final analysis. Three major debiasing interventions were identified: tool use, education of biases, and education of debiasing strategies. All intervention types reported mixed results. Common biases targeted include confirmation, availability, and search satisfying bias. Conclusion: While all three major debiasing interventions identified demonstrate some effectiveness in improving diagnostic accuracy, included studies reported mixed results when implemented. Furthermore, no studies examined decision-making in a clinical setting, and no studies reported long-term follow-up. Future research should look to identify why some interventions demonstrate low effectiveness, the conditions which enable high effectiveness, and effectiveness in environments beyond vignettes and among attending physicians. PROSPERO registration number: CRD42022331128
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.133 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".