240 Electronic personal health records for mobile populations: a rapid systematic literature review
Bibliographic record
Abstract
Abstract EP3.5, e-Poster Terminal 3, September 5, 2025, 13:05 - 13:30 Mobile populations, including refugees, asylum seekers and undocumented migrants, face challenges in access, continuity and quality of healthcare, among others, due to lack of available health records. Our study aimed to examine the current landscape of Electronic Personal Health Records (EPHRs) developed for and used by mobile populations. A rapid systematic literature review was conducted, identifying relevant publications through searches in Embase, PubMed, Scopus, and grey literature. The literature search yielded 2303 articles, with 74 remaining after title and abstract screening. After full-text screening, 10 scientific articles and 9 grey literature records were included in a qualitative data synthesis. Six distinct EPHRs were identified, differing in how they centralize health records, ranging from ‘digital vaults‘ to comprehensive systems, from smartphone apps to web-based apps, and from offline functionalities to the necessity of being connected to the internet. Moreover, they differ in additional functionalities, and the level of patient autonomy granted. Limited evidence exists on their impact on health outcomes or continuity of care, and user adoption remains a critical challenge. Key elements in the development and implementation of EPHRs include ensuring a high level of data security and co-designing easy to use EPHRs. The review indicates a need for future research on user-experiences and their impact on the health outcomes of mobile populations.
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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 | 0.038 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.028 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".