Challenges to the implementation of a nationwide electronic prescribing network in primary care: a qualitative study of users’ perceptions
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: The objective of this study was to identify physicians' and pharmacists' perceptions of the challenges and benefits to implementing a nationwide electronic prescribing network linking medical clinics and community pharmacies in Quebec, Canada. METHODS: Forty-nine people (12 general practitioners, 2 managers, 33 community pharmacists, and 2 pharmacy staff members) from 40 points of care (10 primary care clinics (42% of all the connected sites) and 30 community pharmacies (44%)) were interviewed in 2013. Verbatim transcripts were analyzed using thematic analysis. RESULTS: A low level of network use was observed. Most pharmacists processed e-prescriptions by manual entry instead of importing electronically. They reported concerns about potential errors generated by importing e-prescriptions, mainly due to the instruction field. Paper prescriptions were still perceived as the best means for safe and effective processing of prescriptions in pharmacies. Speed issues when validating e-prescription messages were seen as an irritant by physicians, and resulted in several of them abandoning transmission. Displaying the medications based on the dispensing data was identified as the main obstacle to meaningful use of medication histories. CONCLUSIONS: Numerous challenges impeded realization of the benefits of this network. Standards for e-prescription messages, as well as rules for message validation, need to be improved to increase the potential benefits of e-prescriptions. Standard drug terminology including the concept of clinical medication should be developed, and the implementation of rules in local applications to allow for the classification and reconciliation of medication lists from dispensing data should be made a priority.
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 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 | 0.014 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".