Enhancing anesthesia safety: evaluating artificial intelligence and augmented reality in reducing medication errors
Bibliographic record
Abstract
Medication errors (MEs) continue to impose severe health risks and financial burdens on healthcare systems despite current measures. This thesis investigates integrating Artificial Intelligence (AI) and Augmented Reality (AR) technologies to enhance precision and safety in medication administration. Medication errors within anesthesia are alarmingly prevalent (5.3% of medication administrations result in ME) and often result in adverse drug events (ADEs). The result is that an ME occurs once per 20 anesthetics, with more than one-third resulting in an ADE. To address this, this thesis proposes a novel approach involving AI and AR to provide real-time, context-sensitive decision support during medication administration. AI algorithms are designed to analyze continuous patient data and clinical environments to predict and prevent potential MEs. At the same time, AR systems project critical medication information directly into the clinician's visual field, enhancing situational awareness and accuracy. There is a significant gap in the literature focusing on the direct clinical application of AI and AR in anesthesia. The potential of these technologies to significantly reduce MEs is important for further research to improve medication safety in anesthesia.--Author's abstract
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".