Evaluation of Medication Incidents in a Long-term Care Facility Using Electronic Medication Administration Records and Barcode Technology
Bibliographic record
Abstract
Objective To describe the frequency, type, and severity of reported medication incidents that occurred at a long-term care facility (LTCF) despite electronic medication administration record and barcode-assisted medication administration (eMAR-BCMA) use. The study also contains analysis for the contribution of staff workarounds to reported medication administration errors (MAEs) using an established typology for BCMA workarounds, characterize if the eMAR-BCMA technology contributed to MAEs, and explore characteristics influencing incident severity. Design Retrospective incident report review. Setting A 239-bed LTCF in Alberta, Canada, that implemented eMAR-BCMA in 2013. Participants 270 paper-based, medication incident reports submitted voluntarily between June 2015 and October 2017. Interventions None. Results Most of the 264 resident-specific medication incidents occurred during the administration (71.9%, 190/264) or dispensing (28.4%, 75/264) phases, and 2.3% (6/264) resulted in temporary harm. Medication omission (43.7%, 83/190) and incorrect time (22.6%, 43/190) were the most common type of MAE. Workarounds occurred in 41.1% (78/190) of MAEs, most commonly documenting administration before the medication was administered (44.9%, 35/78). Of the non-workaround MAEs, 52.7% (59/112) were notassociated with the eMAR-BCMA technology, while 26.8% (30/112) involved system design shortcomings, most notably lack of a requirement to scan each medication pouch during administration. MAEs involving workarounds were less likely to reach the resident (74.4 vs 88.8%; relative risk = 0.84, 95% CI 0.72-0.97). Conclusion Administration and dispensing errors were the most reported medication incidents. eMAR-BCMA workarounds, and design shortcomings were involved in a large proportion of reported MAEs. Attention to optimal eMAR-BCMA use and design are required to facilitate medication safety in LTCFs.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".