Common issues in the medication use processes in nursing homes: a review of medication use quality improvement strategies
Bibliographic record
Abstract
Background: A continuous evaluation of safety practices in nursing homes and long-term care (LTC) facilities is needed.Numerous studies have highlighted the deficiencies in safety processes of nursing homes compared with institutional practicedespite the fact that many residents in the nursing home setting also suffer from complex medical conditions and are receivingmultiple medications to treat the comorbidities. As part of larger grant initiative, an extensive search was conducted to identifycommon problems in nursing facilities and potential strategies for improvement. This review highlights common problems in themedication use process in nursing facilities and strategies to improve processes and general resident safety based upon pertinentfindings from the literature. Findings: There are proven medication safety strategies utilized in institutions that should be a foundational practice in nursingand LTC facilities. These strategies include, but are not limited to, reduction of polypharmacy and increased sensitivity to andprioritization of medication therapy monitoring processes, which will assist in the creation of a culture of safety. Specific goals ofthis process includes frequent education and encouraged use of best practices surrounding handling of high-alert medications,proper drug administration ( e.g. , crushing medications), drug interactions and reporting of adverse medication events. Conclusion: Medication use process failures are common in nursing and LTC facilities. However, an integrated approach canbe used to mitigate some of the common problems. Pharmacists and nurses should work more closely to implement provenmedication safety strategies. Implementing these strategies in concert with improved communication between team members canbenefit residents by preventing avoidable events and hospitalizations.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".