Connecting primary care clinics and community pharmacies through a nationwide electronic prescribing network: A qualitative study
Bibliographic record
Abstract
Background The use of medication is at the heart of primary care, but is also the cause for major health concerns. It is therefore important to examine the prescription of medication process. Objective This study identifies the barriers and facilitators perceived by community pharmacists and primary care physicians concerning the adoption of a nationwide electronic prescribing (e-prescribing) network in the province of Quebec, Canada. Methods We used purposive sampling to identify the most intensive users of the e-prescribing network. We conducted phone and in-person interviews. Interviews were transcribed, and we analysed their content with NVivo, using the clinical adoption framework (CAF) for the codification of the data. Results We interviewed 33 pharmacists, 2 pharmacy technicians, 11 physicians and 3 clinic managers. Adoption of the e-prescribing network was fairly low. The respondents underlined adaptation of their work environment, openness to change and perception of benefits as facilitators to the adoption of the network. However, important barriers were perceived, including system quality issues and paper prescriptions being the only legal document in the prescribing process. Even if respondents recognised that the e-prescribing network can offer substantial benefits to the prescribing process, issues still persisted and raised barriers to the full use of such a network, especially in a context where different local information systems are connected within a nationwide e-prescribing network. Conclusion This study, based on the CAF, provides a better understanding of the factors related to the adoption of a nationwide e-prescribing network connecting primary care clinics and community pharmacies. Where this study fits in? Electronic prescribing could improve the processes related to the use and dispensation of drugs in health care, but their adoption is still poor in the North American context. Research on the topic of adoption of electronic prescribing is still limited, particularly among health professionals other than physicians. This study, based on the clinical adoption framework (CAF), provides a better understanding of the factors related to the adoption of a nationwide electronic prescribing network connecting primary care clinics and community pharmacies in the province of Quebec, Canada.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".