Case Study: Using Electronic Medication Administration Record to Enhance Medication Safety and Improve Efficiency in Long-Term Care Facilities
Bibliographic record
Abstract
The electronic medication administration record (eMAR) has been used in hospitals and acute care facilities in Canada for over a decade. Unfortunately, the Canadian continuing care sector has been slow to adopt eMAR usage. Medication delivery in long-term care has traditionally been through paper-based orders and manual documentation in the paper medication administration record. The effectiveness of this manual system as it relates to medication incidents, patient safety and nursing efficiency is not well understood because most of the information is based on anecdotal evidence. Peer-reviewed scientific literature supports the premise that the eMAR, compared to the MAR, is more efficient, significantly reduces medication incidents, promotes patient safety and improves workflow efficiency. In April 2016, the Brenda Strafford Foundation committed to implementing the eMAR at each of our three long-term care facilities to improve medication delivery, reducing and eliminating medication incidents and evaluating the benefits of the electronic system. Under the direction of the clinical team, including nurses, physicians, pharmacists, and the software provider/vendor, an electronic system was developed and new processes for medication delivery were instituted within eight months of starting the project. Since the past year, the evaluation of the eMAR at the Brenda Strafford Foundation demonstrated a reduction in medication delivery time allowing for more time for direct care and a decrease in medication incidents, which directly affects resident health and safety. Nursing and the healthcare aides trained in medication management were surveyed and indicated that the eMAR provides a holistic view of the resident and provides important information readily available to improve the quality of resident care.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".