Clinical Informatics Competencies in the Emergency Medicine Specialist Training Standards of Five International Jurisdictions
Bibliographic record
Abstract
BACKGROUND: The field of clinical informatics (CI), and specifically the electronic health record, has been identified as a key facilitator to achieve a sustainable evidence-based health care system for the future. International graduate medical education (GME) programs have been challenged to ensure that their trainees are provided with appropriate skills to deliver effective and efficient health care in an evolving environment. OBJECTIVES: This study explored how international emergency medicine (EM) specialist training standards address competencies and training in relevant areas of CI. METHODS: A list of categories of CI competencies relative to EM was developed following a thematic review of published references documenting CI curriculum and competencies. Publicly available documents outlining core content, curriculum, and competencies from international organizations responsible for specialty GME and/or credentialing in EM for Australasia, Canada, Europe, the United Kingdom, and the United States were identified. These EM training standards were reviewed to identify inclusion of topics related to the relevant categories of CI competencies. RESULTS: A total of 23 EM curriculum documents were included in the review. Curricula content related to critical appraisal/evidence-based medicine, leadership, quality improvement, and privacy/security were included in all EM curricula. The CI topics related to fundamental computer skills, computerized provider order entry, and patient-centered informatics were only included in the EM curricula documents for the United States and were absent for the other jurisdictions. CONCLUSION: There is variation in the CI-related content of the international EM specialty training standards reviewed. Given the increasing importance of CI in the future delivery of health care, organizations responsible for training and credentialing specialist emergency physicians must ensure that their training standards incorporate relevant CI content, thus ensuring that their trainees gain competence in essential aspects of CI.
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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 | 0.031 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".