21 Delirium and frailty prevention across Fraser health: the frail elderly-older adult network journey
Bibliographic record
Abstract
Introduction Hospital acquired Delirium (HAD) is the third highest hospital harm within Fraser Health Authority (FHA), Vancouver, British Columbia (BC), Canada, often contributing to morbidity requiring alternative levels of care after discharge, mortality, hospital cost, chance of readmission, length of stay, and predisposition to dementia. A patient safety priority, FHA reported 1544 hospital acquired delirium cases in Fiscal Year 2021/22 for an estimated cost of $18.6 million. In BC, 20% of our senior population are frail which can lead to reduction in function and quality of life, which is compounded by a lack of community resources to keep pace with aging population. Upstream frailty prevention is key to slow down, prevent or reverse decline enabling a return to individual’s baseline function. Methods A prospective interventional quality improvement study was done from April 2021 until July 2023 collecting baseline Confusion Assessment Method (CAM) and Preprinted Order (PPO) utilization prior to standardizing documentation by imbedding the CAM in the nursing flowsheets and increasing access to the PPO in the hospitalist admission packs. Weekly Geriatric Education Series as well as annual Delirium Summits solidified context for these changes. The effects on both CAM and PPO utilization were then audited and visualized on annotated control charts. Results CAM utilization showed a sustained increase from baseline of 25% in April 2021 to 100% by Dec 2022 with a corresponding jump in PPO utilization from less than 10% in April 2021 to 80% by Jan 2023 (figures 1–3). Moving the system now towards upstream Frailty prevention will thread success all the way to the community. Conclusions Embedding changes in the infrastructure and consistent education go hand-in-hand in sustaining meaningful changes. Multimodal CAM and Frailty education with foundational Geriatric training sessions enable success in the right context.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".