Barriers and facilitators to implementing electronic prescription: a systematic review of user groups' perceptions
Bibliographic record
Abstract
OBJECTIVE: We conducted a systematic review identifying users groups' perceptions of barriers and facilitators to implementing electronic prescription (e-prescribing) in primary care. METHODS: We included studies following these criteria: presence of an empirical design, focus on the users' experience of e-prescribing implementation, conducted in primary care, and providing data on barriers and facilitators to e-prescribing implementation. We used the Donabedian logical model of healthcare quality (adapted by Barber et al) to analyze our findings. RESULTS: We found 34 publications (related to 28 individual studies) eligible to be included in this review. These studies identified a total of 594 elements as barriers or facilitators to e-prescribing implementation. Most user groups perceived that e-prescribing was facilitated by design and technical concerns, interoperability, content appropriate for the users, attitude towards e-prescribing, productivity, and available resources. DISCUSSION: This review highlights the importance of technical and organizational support for the successful implementation of e-prescribing systems. It also shows that the same factor can be seen as a barrier or a facilitator depending on the project's own circumstances. Moreover, a factor can change in nature, from a barrier to a facilitator and vice versa, in the process of e-prescribing implementation. CONCLUSIONS: This review summarizes current knowledge on factors related to e-prescribing implementation in primary care that could support decision makers in their design of effective implementation strategies. Finally, future studies should emphasize on the perceptions of other user groups, such as pharmacists, managers, vendors, and patients, who remain neglected in the literature.
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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 | 0.032 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".