Competencies for improving diagnosis: an interprofessional framework for education and training in health care
Bibliographic record
Abstract
Background Given an unacceptably high incidence of diagnostic errors, we sought to identify the key competencies that should be considered for inclusion in health professions education programs to improve the quality and safety of diagnosis in clinical practice. Methods An interprofessional group reviewed existing competency expectations for multiple health professions, and conducted a search that explored quality, safety, and competency in diagnosis. An iterative series of group discussions and concept prioritization was used to derive a final set of competencies. Results Twelve competencies were identified: Six of these are individual competencies: The first four (#1-#4) focus on acquiring the key information needed for diagnosis and formulating an appropriate, prioritized differential diagnosis; individual competency #5 is taking advantage of second opinions, decision support, and checklists; and #6 is using reflection and critical thinking to improve diagnostic performance. Three competencies focus on teamwork: Involving the patient and family (#1) and all relevant health professionals (#2) in the diagnostic process; and (#3) ensuring safe transitions of care and handoffs, and "closing the loop" on test result communication. The final three competencies emphasize system-related aspects of care: (#1) Understanding how human-factor elements influence the diagnostic process; (#2) developing a supportive culture; and (#3) reporting and disclosing diagnostic errors that are recognized, and learning from both successful diagnosis and from diagnostic errors. Conclusions These newly defined competencies are relevant to all health professions education programs and should be incorporated into educational programs.
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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 | 0.037 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".