Near Field Communication-Based System For Bedside Contraindication Verification
Bibliographic record
Abstract
Contraindication is one of the serious causes of medication administration errors that can lead to significant clinical consequences. Contraindication-related errors can happen at the prescribing stage by the physician, dispensing stage by the pharmacist, and administration stage by the nurse. The research to date has focused on introducing information technology interventions to trigger alerts about contraindications during the prescribing and dispensing stages. However, far too little attention has been paid to the administration stage. Therefore, in this paper, we propose a solution that considers the administration stage and helps nurses to expose contraindications prior to delivering medication to patients. We developed a prototype Near Field Communication-Based system for bedside contraindication verification. We used the Information System Research Framework to guide us through the design, implementation, and evaluation processes. We tested the usability of our system to evaluate the efficiency, effectiveness, perceived ease of use, and perceived usefulness, as well as defining its strengths and weaknesses from the nurses' perspectives.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".