Equity-Driven Diagnostic Excellence framework: An upstream approach to minimize risk of diagnostic inequity
Bibliographic record
Abstract
OBJECTIVES: Diagnostic errors represent the most common and costly preventable patient safety events, with historically marginalized populations disproportionately impacted due to systemic inequities in healthcare. Addressing these disparities requires embedding equity into every facet of the diagnostic process. The aim was to develop, refine, and validate a competency framework for Equity-Driven Diagnostic Excellence (DxEqEx). METHODS: A modified Delphi method was used, involving transdisciplinary diverse healthcare system participants, including patient advocates, physicians, nurses, and other healthcare professionals. Participants were guided through multiple rounds of feedback and ratings, assessing the importance, disciplinary relevance, feasibility, skill acquisition level required, granularity, and representativeness of the DxEqEx framework. RESULTS: Sixteen essential competencies have been identified, categorized into three domains: Intrapersonal, Team-based, and Structural. Participants rated the framework with high importance and strong relevance to their respective disciplines. However, the feasibility of implementing the framework varied, largely due to broader challenges within the healthcare system. The competencies were assessed as requiring a proficient skill level according to Dreyfus' model. The final round maintained strong ratings for granularity and representativeness, which supported the final version of the framework. CONCLUSIONS: The DxEqEx framework holds significant potential to proactively address the needs of historically marginalized patients throughout the diagnostic process. Future research should focus on participatory, resource-efficient implementation.
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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 | 0.097 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.032 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".