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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".