Accelerating the Appropriate Adoption of Artificial Intelligence in Health Care: Prioritizing IDEA to Champion a Collaborative Educational Approach in a Stressed System
Bibliographic record
Abstract
In a dynamic healthcare landscape, healthcare professionals (HCPs) must be proficient in artificial intelligence (AI). The Clinician Champions Program was created to address these AI education gaps. Over six weeks, three cohorts participated in this interprofessional program, featuring weekly assignments and a capstone project. This study employs a qualitative descriptive approach to assess the program’s effectiveness in enhancing knowledge, confidence, and skills in AI integration. With a 78% completion rate among 158 clinicians, the program utilized engaging methods, including case studies, capstone projects, and reflective learning to meet diverse learning needs. It also emphasized ethical considerations (e.g., IDEA framework) and the importance of extending educational opportunities to various healthcare professionals. The findings highlight the necessity of a diverse, equitable, and inclusive learning environment to bridge AI education gaps in healthcare. The program’s success supports the idea that enhancing AI knowledge and fostering confidence can lead to meaningful AI discussions in healthcare practice. This research offers insights for educators and institutions aiming to address the evolving healthcare needs through innovative interprofessional educational approaches.
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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 | 0.028 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".