Use of an Electronic Medication Administration Record (eMAR) for Surveillance of Medication Omissions: Results of a One Year Study of Antimicrobials in the Inpatient Setting
Bibliographic record
Abstract
INTRODUCTION: Medication administration omissions (MAO) are usually considered medication errors but not all MAO are clinically relevant. We determined the frequency of clinically relevant MAO of antimicrobial drugs in adult hospitals in Calgary, Alberta, Canada based on electronic medication administration record (eMAR). METHODS: We examined 2011 data from eMAR records on medical wards and developed a reproducible assessment scheme to categorize and determine clinical relevance of MAO. We applied this scheme to records from 2012 in a retrospective cohort study to quantify clinically relevant MAO. Significant predictors of clinically relevant MAO were identified. RESULTS: A total of 294,718 dose records were assessed of which 10,282 (3.49%) were for doses not administered. Among these 4903 (1.66% of total); 47.68% of MAO were considered clinically relevant. Significant positive predictors of clinically relevant MAO included inhaled (OR 4.90, 95% CI 3.54-6.94) and liquid oral (OR 1.32, 95% CI 1.18-1.47) route of medication compared to solid oral and irregular dose schedules. Evening nursing shift compared to night shift (OR 0.77 95% CI 0.70-0.85) and parenteral (OR 0.50, 95% CI 0.46-0.54) were negative predictors, The commonest reasons for relevant MAO were patient preference, unspecified reason, administration access issues, drug not available or patient condition. CONCLUSION: Assessment of MAO by review of computer records provides a greater scope and sample size than directly observed medication administration assessments without "observer" effect. We found that MAO of antimicrobials in inpatients were uncommon but were seen more frequently with orally administered antimicrobials which may have significance to antimicrobial stewardship initiatives.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".