Greek National E-Prescribing System: Preliminary Results of a Tool for Rationalizing Pharmaceutical Use and Cost
Bibliographic record
Abstract
BACKGROUND: In Greece, due to the ongoing economic crisis a number of measures aiming at rationalising expenditure implemented. A new e-prescribing system, under a unified healthcare fund was the main pillar of these reforms focus on monitoring and auditing prescribing patterns. OBJECTIVE: Main objective of this study was to document the Greek experience with the new national e-prescribing system. METHODS: We analyse the dispensed prescriptions over the period 2013-2014, stratified into four levels: therapeutic subgroup, patent status, physician's specialty and geographical region. RESULTS: Data analysis offered a comprehensive insight into pharmaceutical expenditure over the timeframe and revealed discrepancies regarding composition of spending, brand-generic substitution within certain therapeutic subgroups, physicians' prescribing behaviour based on medical specialty, therapeutic subgroup as well as regional per capita measures. CONCLUSIONS: E-prescribing system is a valuable tool providing sound information to health policymakers in order to monitor and rationalize pharmaceutical expenditure, in value and volume terms.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".