Computerized provider order entry and patient safety: A scoping review
Bibliographic record
Abstract
As health systems in Canada are being modernized with the use of technologies, digital health tools are now increasingly being used to improve patient safety. Computerized Provider Order Entry (CPOE) is now being used in Canada and the technology may have an important impact on patient safety. The objective of this scoping review is to explore the impact of CPOE on patient safety in health care settings. Four databases were searched for studies related to CPOE and patient safety. Following title, abstract and then full text review, twelve studies were selected for further analyses. Several key themes emerged from the literature. The findings revealed several important themes: (1) the implementation of CPOE is an important aspect of patient safety, (2) comparisons of CPOE implementations across multiple sites or facilities were made, (3) the end-user experience of using CPOE was important, and (4) the evaluation of CPOE is key to establishing risk frameworks. Risk mitigation strategies and lessons for academia and industry are discussed. Overall, the scoping review revealed that although patient safety can be improved using CPOE, there is a large difference in realized impacts among healthcare systems.
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.
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 | 0.012 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".