Implementation of E-prescription for Multidose Dispensed Drugs: Qualitative Study of General Practitioners’ Experiences
Bibliographic record
Abstract
BACKGROUND: Increased use of pharmaceuticals challenges both capacity and safety related to medication management for patients and changes in how general practitioners (GPs) and other health personnel interact with and follow up with patients. E-prescribing of multidose drug dispensing (eMDD) is 1 of the national measures being tested in Norway. OBJECTIVE: The objective of this study is to explore GPs' experiences with the challenges and benefits of implementing eMDD in Norway. METHODS: Qualitative in-depth and group interviews were conducted with a total of 25 GPs between 2018 and 2020. Transcribed files were saved in NVivo to conduct a step-by-step content analysis. NVivo is a software tool for organizing, managing, and analyzing qualitative data. RESULTS: The study revealed that eMDD offers many benefits. At the same time, there are several challenges related to information, training, and initiation, as well as to the responsibility for the medication, interactions, and the risk of incorrect medication. An important activity in the start-up phase was an information meeting with pharmacies and technology suppliers, as well as exchanging information and instructions with pharmacies on how to get started. Four analytic themes emerged through the extraction of data: (1) start-up with eMDD ("Be patient"); (2) the need for training; (3) interaction, safety, and efficiency; and (4) the working day with eMDD. CONCLUSIONS: There is a variation in different GPs' needs regarding training and information, and considerable variation in competence and motivation related to the use of digital tools. There are also different degrees of understanding the everyday work of the other actors in the medication chain. In particular, the harmonization of medication lists related to the use of time, expenditures, and challenges with technological solutions in the introduction phase was emphasized as a challenge. Overall, GPs who have started using the system report great benefits; these are largely related to an increased overview of patients' total medication lists, less time spent on prescribing prescriptions, and increased collaboration with pharmacies and nurses, both in service from providers in homes and in nursing homes.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".