The efficiency of STAT’s order throughout alert technology for the nurse’s mobile station in Taiwan: A trajectory study
Bibliographic record
Abstract
Nursing information system combined with computerized physician order entry is a new technology on patient medication safety. With the help of clinical decision support and the alert reminder of the mobile station, the medication administration will be more precise. The purpose of this study was to predict the change of time on Stat Order before and after the implementation of the decision alert system. Design: Throughout a longitudinal study and a nested design--level 1 was the frequency of time on Stat Order (including the baseline, one up to six times); level 2 was the subject (nurses). Settings: The subjects were divided into two groups: before (paper group) and after (computer group with flash of alert to remind nurses) implementation. The data of nursing information system was used to collect the administration medication of time on Stat Order. Participants: There were 198 nurses enrolled and 2,376 time on Stat Order in this study. The study was carried out between July to October 2008. STATA 12.0 was adopted for descriptive statistics and multilevel regression analysis. The result showed that the mixed regression model of time on Stat Order on computer group was significantly reduced than on paper group (95% CI: -99.05~-61.65, p < .001). Furthermore, it also showed a significantly reduced time on each visit (95% CI: -13.63~-9.89, p < .00). The inter-correlation value was .66~.67. We recommended integrating this new alert technology, a high quality and timely method, with nurse’s routine work to improve medication administration on health care service.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".