Accelerating the Appropriate Adoption of Artificial Intelligence in Health Care: Prioritizing IDEA to Champion a Collaborative Educational Approach in a Stressed System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it