Going Mobile: How Mobile Personal Health Records Can Improve Health Care During Emergencies
Bibliographic record
Abstract
Personal health records (PHRs), in contrast to electronic health records (EHRs) or electronic medical records (EMRs), are health records in which data are accessible to patients and not just providers. In recent years, many systems have enabled PHRs to be available in a mobile format. Mobile PHRs (mPHRs) allow patients to access health information via the Internet or telecommunication devices, such as mobile phones, personal digital assistants, and tablet computers. mPHRs have the potential to help patients and providers identify medical conditions and prescriptions from numerous locations, which may minimize medical errors and identify improvements to health behaviors during emergencies, when patients present to a new provider, or EHRs are not accessible. Despite their benefits, numerous challenges inhibit the adoption and further development of mPHRs, including integration into overall health technology infrastructure and legal and security concerns. This paper identifies the benefits of mPHRs during emergencies and the remaining challenges impeding full adoption and use, and provides recommendations to federal agencies to enhance support and use of mPHRs.
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 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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".