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.
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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 | 0.058 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.052 | 0.063 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| 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".