Geriatric Delirium Care: Using Chart Audits to Target Improvement Strategies
Bibliographic record
Abstract
BACKGROUND: Our hospital identified delirium care as a quality improvement target. Baseline characterization of our delirium care and deficits was needed to guide improvement efforts. METHODS: Two inpatient units were selected: 1) A general internal medicine unit with a focus on geriatrics, and 2) a surgical unit. Retrospective chart audits were conducted for all patients over age 50 admitted during a one-month period to compare delirium care with best practice guideline (BPG) recommendations, and to determine the incidence of missed cases of delirium and negative outcomes in patients with delirium. The aim was to gather local data to prioritize improvement efforts and mobilize stakeholders. RESULTS: 186 charts were reviewed: 17 patients had physician-diagnosed delirium, 21 patients had missed delirium, and 148 patients had no delirium. Compliance with delirium BPGs was variable, but generally poor. There was a trend towards missed delirium and physician-diagnosed delirium being associated with greater odds of having above-median length of stay and lower odds of discharge home compared to no delirium diagnosis. CONCLUSION: Overall, the chart audits confirmed delirium underrecognition and poor adherence to best practices in delirium management. Granular analysis of this data was used to mobilize stakeholders and prioritize improvement plans.
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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 | 0.053 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".