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 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 | 0.035 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".