Implementation of a multicomponent intervention sign to reduce delirium in orthopaedic inpatients (MIND-ORIENT): a quality improvement project
Bibliographic record
Abstract
Delirium is a serious and common condition that leads to significant adverse health outcomes for hospitalised older adults. It occurs in 30%-55% of patients with hip fractures and is one of the most common postoperative complications in older adults undergoing orthopaedic surgery. Multicomponent, non-pharmacological interventions can reduce delirium incidence by up to 30% but are often challenging to implement as part of routine care. We identified a gap in the delivery of non-pharmacological interventions on an orthopaedic unit. This project aimed to implement a bedside sign on an orthopaedic unit to reduce the occurrence of delirium by prompting staff to use multicomponent evidence-based delirium prevention strategies for at-risk older adults. Quality improvement methods were used to integrate and optimise the use of a bedside 'delirium prevention' sign on an orthopaedic unit.The sign was implemented in four target rooms and sign completion rates increased from 47% to 83% (95% CI 71.7% to 94.9%; p<0.001) over a 10-month period. The sign did not have a significant impact on delirium prevalence. The mean Confusion Assessment Method (CAM)+ rate during the baseline period was 8% with an absolute increase in the intervention period to 11.4% (95% CI 7.2% to 15.8%; p=0.31). There were no significant shifts or trends in the run chart for the proportion of patients with CAM+ scores over time. The sign was well received by staff, who reported it was a worthwhile use of time and prompted use of non-pharmacological interventions. This quality improvement project successfully integrated a novel, low-cost, feasible and evidence-based approach into routine clinical care to support staff to deliver non-pharmacological interventions. Given the increased pressures on front-line staff in hospital, tools that reduce cognitive load at the bedside are important to consider when caring for a vulnerable older adult patient population.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".