A review of biomedical and health informatics education: A workforce training framework
Bibliographic record
Abstract
Objective: The purpose of this paper is to review the current state of health information technology (HIT) training programs and identify limitations in workforce expectations and student/trainee level of preparedness. A framework is proposed to build a more effective training program, differentiate HIT and health informatics, and emphasize the critical role of interprofessional collaboration for informatics-related curriculum. We define interprofessionalism as the multi-sector collaborations among academia, industry (Health Care Organizations), and vendors to produce competent informaticians.Methods: Critical review of published HIT and health informatics curricular competencies was conducted, including those published by the Office of the National Coordinator (ONC) for HIT, the American Medical Informatics Association (AMIA), the International Medical Informatics Association (IMIA), and the Council on Accreditation for Health Informatics and Information Management. A review of literature related to HIT and health informatics education and training was also completed.Results: The paper presents a framework for promoting health informatics training with an interprofessional foundation. The core components of the curricular competencies include understanding the healthcare system, biomedical data, computer programming, data analytics, usability, and technology infrastructure. To effectively deliver the content, programs require collaboration between academic institutions, healthcare organizations, and industry vendors.Conclusions: HIT and health informatics-related training programs, in their current form, are not meeting industry needs. The proposed framework addresses the current limitations by providing unique pathways for content delivery by promoting interprofessional collaboration and partnerships between academia and industry.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".