The Current State and Usage of European Electronic Cross-border Health Services (eHDSI)
Bibliographic record
Abstract
INTRODUCTION: European Union intends to enable cross-border health services through a program referred to as "MyHealth@EU". The first main service is the dispensation of medicine by interlinking national electronic prescription systems. The second one is the Patient Summary, which enables providing the basic set of patients' medical data. METHODS: The contemporary technical documentation of the project was studied and selected published Key Performance Indicators of the project were analyzed. Where necessary, data were acquired directly from the European Commission. RESULTS: Data from the start of the project (fourth quarter of 2019) until the second quarter of 2022 were analyzed. During this time both the overall number of EU countries with operational cross-border healthcare and their particular abilities in both services have risen. At present, there are eleven countries with capabilities in at least one of the services, of which nine have reported transactions. More countries are in the test phase now and will join the operational phase of the project shortly. DISCUSSION AND CONCLUSION: Nevertheless, the program is still used mostly for testing purposes. It seems that only electronic prescription and dispensation are commonly and widely used so far and only Estonian and Finnish patients usually get their medication dispensed abroad. The rest of the operational countries is still at present missing country pairs with a strong cross-border use case.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".