Reasons for Physician Non-Adherence to Electronic Drug Alerts
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
CONTEXT: Many adverse drug errors may be prevented through electronic order entry systems that provide decision support to physicians by screening prescriptions for dosing errors, drug-disease, drug-allergy and drug-drug interactions. The adherence to such decision aids is varied and the reasons for this variance not well understood. OBJECTIVE: To assess the feasibility and performance auto-mated drug alerts within an electronic decision support system for physician prescribing. METHODS: Drug alert data were collected from a pilot project with 30 participating general practitioners who were provided with interactive electronic prescription capabilities through a personal digital assistant (PDA). RESULTS: 66,642 electronic prescriptions resulted in a total of 1,869 drug alerts. The most common alert types were analysed, along with reasons for non-adherence to automated drug alerts. CONCLUSIONS: Non-adherence to alert information appears to be associated with additional knowledge of the clinical situation, beyond that inherent in the decision support tool, for the specific patient context. Further work is required to understand how best to provide this type of support to physicians.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 it