Advances in Health Informatics Education: Educating Students at the Intersection of Health Care and Information Technology
Bibliographic record
Abstract
The paper describes the authors' work in the area of health informatics (HI) education involving emerging health information technologies. A range of information technologies promise to modernize health care. Foremost among these are electronic health records (EHRs), which are expected to significantly improve and streamline health care practice. Major national and international efforts are currently underway to increase EHR adoption. However, there have been numerous issues affecting the widespread use of such information technology, ranging from a complex array of technical problems to social issues. This paper describes work in the integration of information technologies directly into the education and training of HI students at both the undergraduate and graduate level. This has included work in (a) the development of Web-based computer tools and platforms to allow students to have hands-on access to the latest technologies and (b) development of interdisciplinary educational models that can be used to guide integrating information technologies into HI education. The paper describes approaches that allow for remote hands-on access by HI students to a range of EHRs and related technology. To date, this work has been applied in HI education in a variety of ways. Several approaches for integration of this essential technology into HI education and training are discussed, along with future directions for the integration of EHR technology into improving and informing the education of future health and HI professionals.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".