Effectiveness of a Multi-component Delirium Prevention Program Implemented on General Medicine Hospital Units: an Interrupted Time Series Analysis
Bibliographic record
Abstract
BACKGROUND: Delirium is among the most prevalent harmful events in hospitals that is associated with an elevated risk for severe outcomes such as functional decline, falls, longer length of stay, and increased mortality. OBJECTIVE: To evaluate the impact of the implementation of a multi-component delirium program on the prevalence of delirium and the incidence of falls among patients staying on general medicine inpatient hospital units. DESIGN: A pre-post intervention study using retrospective chart abstraction and interrupted time series analysis. COHORT: Patients were selected from adult patients that stayed at least 1 day on one of the five general medicine units in a large community hospital in Ontario, Canada. A total of 16 random samples of 50 patients per month for 8 consecutive months pre-intervention (October 2017 to May 2018) and 8 months post intervention (January 2019 to August 2019) were selected for a total of 800 patients. There were no exclusion criteria. INTERVENTION: The delirium program included multiple components: education of staff and hospital leadership, twice per day bed-side screen for delirium, non-pharmacological and pharmacological prevention, and intervention strategies and a delirium consultation team. MEASUREMENT: Delirium prevalence was assessed using the evidence-based delirium chart abstraction method, CHART-del. Demographic data as well as fall incidence were also collected. RESULT: Our evaluation showed that the implementation of a multicomponent delirium program led to a reduction in delirium prevalence and fall incidences. The reduction in both delirium and falls was the largest for patients in the ages between 72 and 83 years old and varied across inpatient units. CONCLUSION: A multi-component delirium program to improve the prevention, recognition, and management of delirium reduces the prevalence of delirium and fall incidence among patients in general medicine units.
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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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".