Effect of a multimethod quality improvement intervention on antipsychotic medication use among residents of long-term care
Bibliographic record
Abstract
BACKGROUND: Antipsychotic medications are used to address neuropsychiatric symptoms associated with dementia. Evidence suggests that among older adults with dementia, their harms outweigh their benefits. A quality improvement initiative was conducted to address inappropriate antipsychotic medication use in long-term care (LTC) in the province of Alberta. METHODS: We conducted a multimethod evaluation of the provincial implementation of the project in 170 LTC sites over a 3-year project period incorporating a quasi-experimental before-after design. Using a three-component intervention of education and audit and feedback delivered in a learning workshop innovation collaborative format, local LTC teams were supported to reduce the number of residents receiving antipsychotic medications in the absence of a documented indication. Project resources were preferentially allocated to supporting sites with the highest baseline antipsychotic medication use. Changes in antipsychotic medication use, associated clinical and economic outcomes, and the effects of the project on LTC staff, physicians, leaders and administrators, and family members of LTC residents were assessed at the conclusion of the implementation phase. RESULTS: The province-wide initiative was delivered with a 75% implementation fidelity. Inappropriate antipsychotic medication use declined from 26.8% to 21.1%. The decrease was achieved without unintended consequences in other outcomes including physical restraint use or aggressive behaviours. The project was more expensive but resulted in less inappropriate use of antipsychotics than the pre-project period (incremental cost per inappropriate antipsychotic avoided of $5 678.71). Accounts from family, organisational leaders, and LTC staff were supportive of the project activities and outcomes. CONCLUSION: This quality improvement initiative was successfully delivered across an entire delivery arm of the continuing care sector. Quality of care in LTC was improved.
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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 | 0.011 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".