Medication administration technologies and patient safety: a mixed-method systematic review
Bibliographic record
Abstract
BACKGROUND: Healthcare leaders need evidence-based information on nursing medication administration technologies to guide the design of improvements to patient safety. AIM: The aim of this study was to evaluate the research evidence on relationships between the use of medication administration technologies and incidence of medication administration incidents and preventable adverse drug events to inform decision-making about existing technology options. DATA SOURCES: Thirteen electronic databases and seven relevant patient safety websites were searched for the years 1980-2009. REVIEW METHODS: A mixed-method systematic literature review of research on medication administration technologies and associated links to patient safety, operationalized as medication administration incidents and preventable adverse drug events, was conducted. RESULTS: Twelve studies (two qualitative, five pre- and postinterventions and five correlational) met the inclusion criteria. All were assessed as medium quality with low generalizability of study findings. Only two studies sampled more than one hospital and none of the studies was driven by an explicit theoretical framework. The studies included in this review are generally positive towards medication administration technologies and their potential benefits, yet the level of evidence overall is equivocal. The majority of studies pointed to the development of workarounds by nurses following medication administration technology implementation that could compromise patient safety. CONCLUSION: More theoretically driven research is needed to determine which medication administration technologies should be implemented in what ways to most effectively reduce medication administration incidents and preventable adverse drug events and minimize the development of potentially unsafe workarounds. Further evidence is required to accurately assess the actual contribution of medication administration technologies for improving patient safety.
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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 | 0.040 | 0.134 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.022 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".