Electronic Medication Administration Records in Long‐Term Care Facilities: A Scoping Review
Bibliographic record
Abstract
OBJECTIVES: To map the extent, range, and nature of research on the effectiveness, level of use, and perceptions about electronic medication administration records (eMARs) in long-term care facilities (LTCFs) and identify gaps in current knowledge and priority areas for future research. DESIGN: Scoping review of quantitative and qualitative literature. SETTING: Literature review. PARTICIPANTS: Original research relating to eMAR in LTCF was eligible for inclusion. MEASUREMENTS: We systematically searched MEDLINE, CINAHL, Scopus, ProQuest, and the Cochrane Library and performed general and advanced searches of Google to identify grey literature. Two authors independently screened for eligibility of studies. Independent reviewers extracted data regarding country of origin, design, study methods, outcomes studied, and main results in duplicate. RESULTS: We identified 694 articles, of which 34 met inclusion criteria. Studies were published between 2006 and 2016 and were mostly from the United States (n=25). Twenty studies (59%) used quantitative methods, including surveys and analysis of eMAR data; 7 (21%) used qualitative methods, including interviews, focus groups, document review, and observation; and 7 (21%) used mixed methods. Three major research areas were explored: medication and medication administration error rates (n=11), eMAR benefits and challenges (n=19), and eMAR prevalence and uptake (n=15). Evidence linking eMAR use and reductions in medication errors is weak because of suboptimal study design and reporting. The majority of studies were descriptive and documented inconsistent benefits and challenges and low levels of eMAR implementation. CONCLUSION: Further investigation is required to rigorously evaluate the effect of standalone eMAR systems on medication administration errors and patient safety, the extent of eMAR implementation, pharmacists' perceptions, and cost effectiveness of eMAR systems in LTCF.
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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 | 0.053 | 0.193 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.032 | 0.036 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| 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".