Safe Medication Swallowing in Dysphagia: A Collaborative Improvement Project
Bibliographic record
Abstract
Episodes of choking during medication administration to patients with dysphagia prompted a chart audit and caregiver interview to identify system problems that allowed inappropriate drug administration to occur. Sixty elderly patients residing on two patient care areas in a 500-bed complex continuing care facility were studied. The audit explored the actual nursing medication administration methods and compared this to the information obtained from various communication tools including instructions that appeared on the medication administration record (MAR), the current diet order, the recommendations of the speech-language pathologist (SLP) and comments on the nursing care plan. The audit yielded a number of discrepancies between nursing actions and the instructions obtain from these sources. We proposed that changes to the process of communicating medication swallowing recommendations among team members would lead to greater patient safety. Major practice changes included the use of standardized language by the SLP when making recommendations, the writing of SLP recommendations in the doctor's orders, the inclusion of SLP recommendations on the MAR and the creation of a "dysphagia alert" on the pharmacy computer system. An educational intervention was conducted to implement process changes. Its effectiveness was evaluated using a pre- and post-test and a participant satisfaction survey. A post-implementation audit showed compliance with the practice change. In summary, process changes were implemented to improve compliance with SLP medication-related swallowing recommendations and to prevent the inadvertent prescribing, dispensing or crushing of sustained-release medications in patients with dysphagia.
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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 | 0.037 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.007 |
| 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".