Citizens' Options When Accessing and Sharing Health Information – An International Survey of IMIA Member Countries
Bibliographic record
Abstract
INTRODUCTION: Citizens' access to personal health information and information on prescription medication, options to share personal health data, and how these personal health data are kept secure, are all important themes in health informatics and therefore elaborated upon in this paper. METHODS: The empirical data stems from a survey that examines citizens' temporal access to laboratory test results, options for sharing patient-generated health data (PGHD) with health providers, methods to obtain supplementary information on prescription medication, and security issues pertaining to national standards, education, and experienced breaches. RESULTS: Results are based on answers from representatives in the International Medical Informatics Association (IMIA) member countries (n=28). Data shows that citizens' online access to test results is possible as soon as they are available in ten countries whereas nine countries have no norm or standard. The most common ways to provide citizens with supplementary information on prescription medication is through package inserts from manufacturers or paper medication information from pharmacies. PGHD is shared primarily in print or by showing the device to the health provider. Regarding e-health security, most countries have national standards for the security, however, less than half of the IMIA representatives answer that health professionals are required training in the national standards. Lastly, 16 of the 28 answers reply that there has been leaks leading to unauthorized access to health data. Future research should focus on how to provide citizens access to lab results according to their needs and examine how to include digital PGHD meaningfully into clinical practice.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".