Scope of Policy Issues in eHealth: Results From a Structured Literature Review
Bibliographic record
Abstract
BACKGROUND: eHealth is widely used as a tool for improving health care delivery and information. However, distinct policies and strategies are required for its proper implementation and integration at national and international levels. OBJECTIVE: To determine the scope of policy issues faced by individuals, institutions, or governments in implementing eHealth programs. METHODS: We conducted a structured review of both peer-reviewed and gray literature from 1998-2008. A Medline search for peer-reviewed articles found 40 papers focusing on different aspects of eHealth policy. In addition, a Google search found 20 national- and international-level policy papers and documents. We reviewed these articles to extract policy issues and solutions described at different levels of care. RESULTS: The literature search found 99 policy issues related to eHealth. We grouped these issues under the following themes: (1) networked care, (2) interjurisdictional practice, (3) diffusion of eHealth/digital divide, (4) eHealth integration with existing systems, (5) response to new initiatives, (6) goal-setting for eHealth policy, (7) evaluation and research, (8) investment, and (9) ethics in eHealth. CONCLUSIONS: We provide a list of policy issues that should be understood and addressed by policy makers at global, jurisdictional, and institutional levels, to facilitate smooth and reliable planning of eHealth programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.064 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.025 |
| Insufficient payload (model declined to judge) | 0.004 | 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; both teacher heads agree on what is shown here.
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".