A literature review of the training offered to qualified prescribers to use electronic prescribing systems: why is it so important?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: A key element of the implementation and ongoing use of an electronic prescribing (ePrescribing) system is ensuring that users are, and remain, sufficiently trained to use the system. Studies have suggested that insufficient training is associated with suboptimal use. However, it is not clear from these studies how clinicians are trained to use ePrescribing systems or the effectiveness of different approaches. We sought to describe the various approaches used to train qualified prescribers on ePrescribing systems and to identify whether users were educated about the pitfalls and challenges of using these systems. METHODS: We performed a literature review, using a systematic approach across three large databases: Cumulative Index Nursing and Allied Health Literature, Embase and Medline were searched for relevant English language articles. Articles that explored the training of qualified prescribers on ePrescribing systems in a hospital setting were included. KEY FINDINGS: Our search of 'all training' approaches returned 1155 publications, of which seven were included. A separate search of 'online' training found three relevant publications. Training methods in the 'all training' category included clinical scenarios, demonstrations and assessments. Regarding 'online' training approaches; a team at the University of Victoria in Canada developed a portal containing simulated versions of electronic health records, where individuals could prescribe for fictitious patients. Educating prescribers about the challenges and pitfalls of electronic systems was rarely discussed. CONCLUSIONS: A number of methods are used to train prescribers; however, the lack of papers retrieved suggests a need for additional studies to inform training methods.
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.
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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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 it