Greek National E-Prescribing System: Preliminary Results of a Tool for Rationalizing Pharmaceutical Use and Cost
Bibliographic record
Abstract
<p><strong>BACKGROUND:</strong> 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.</p><p><strong>OBJECTIVE:</strong> Main objective of this study was to document the Greek experience with the new national e-prescribing system.</p><p><strong>METHODS:</strong> We analyse the dispensed prescriptions over the period 2013-2014, stratified into four levels: therapeutic subgroup, patent status, physician's specialty and geographical region.</p><p><strong>RESULTS</strong>: 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.</p><p><strong>CONCLUSIONS:</strong> 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.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".