A Quality Improvement Project to Reduce Falls and Improve Medication Management
Bibliographic record
Abstract
This paper describes the implementation of a medication management model within a medical-center based home health agency. The model was integrated into the agency's quality improvement falls prevention program and was selected in part because it directly addressed two medication-related accreditation standards for home health care agencies. During a five-month period, a staff pharmacist conducted medication reviews for 228 HHA patients who met the program's inclusion criteria. Thirty-three percent of these patients required some type of follow-up to resolve potential medication-related problems. By far, falls were the most common reason for referral, with 71 patients, or 30% of all participating patients, referred to the pharmacist due to a recent fall. From a quality improvement standpoint, the program met and even exceeded expectations in that it enabled staff to identify a serious threat to patient safety-medication-related problems, especially falls--and gave them the tools to resolve these potential problems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".