Digital Health in Physicians' and Pharmacists' Office: A Comparative Study of e-Prescription Systems' Architecture and Digital Security in Eight Countries
Bibliographic record
Abstract
e-Prescription systems are key components and drivers of digital health. They can enhance the safety of the patients, and are gaining popularity in health care systems around the world. Yet, there is little knowledge on comparative international analysis of e-Prescription systems' architecture and digital security. We report, in this study, original findings from a comparative analysis of the e-Prescription systems in eight different countries, namely, Canada, United States, United Kingdom, Australia, Spain, Japan, Sweden, and Denmark. We surveyed the databases related to pharmacies, eHealth, e-Prescriptions, and related digital health websites for each country, and their system architectures. We also compared the digital security and privacy protocols in place within and across these digital systems. We evaluated the systems' authentication protocols used by pharmacies to verify patients' identities during the medication dispensing process. Furthermore, we examined the supporting systems/services used to manage patients' medication histories and enhance patients' medication safety. Taken together, we report, in this study, original comparative findings on the limitations and challenges of the surveyed systems as well as in adopting e-Prescription systems. While the present study was conducted before the onset of COVID-19, e-Prescription systems have become highly relevant during the current pandemic and hence, a deeper understanding of the country systems' architecture and digital security that can help design effective strategies against the pandemic. e-Prescription systems can help reduce physical contact and the risk of exposure to the virus, as well as the wait times in pharmacies, thus enhancing patient safety and improving planetary health.
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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 | 0.013 |
| 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 | 0.003 |
| Open science | 0.000 | 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".