A Safe Practice Standard for Barcode Technology
Bibliographic record
Abstract
OBJECTIVE: Safety advocates have identified barcode verification technology as an important tool to improve health-care practices. METHODS: We evaluated the evidence for the role of barcode technology in improving a wide range of medication safety outcomes across a broad range of settings. Important implementation issues were highlighted to guide standards for the safe adoption of barcode technology. RESULTS: Adverse drug events are common, occurring frequently in both inpatient and outpatient settings. Although approximately half of all preventable adverse drug events in inpatients result from medication errors arising from transcription, dispensing, and administration, these errors are far less likely to be caught than in any of the earlier stages of the medication use process and are therefore most amenable to improvement. When integrated with electronic medication administration records, barcode systems are associated with complete elimination of transcription errors. Furthermore, barcode-assisted dispensing systems are associated with 93% to 96% reductions in dispensing errors, and 85% reductions in potential adverse drug events in dispensing. Most studies have reported large and significant reductions in administration errors by up to 80% after implementation of barcode medication administration systems. Although most studies of barcode technology have been conducted in the adult inpatient setting, the limited data available also support their benefit in pediatric and outpatient settings. CONCLUSIONS: There is growing evidence for the efficacy of barcode solutions in improving overall medication safety. Standards for the implementation of barcode technology are proposed.
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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 | 0.106 | 0.205 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.019 | 0.019 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".