Implementation of delirium screening in the emergency department: A qualitative study with early adopters
Bibliographic record
Abstract
INTRODUCTION: Delirium affects 15% of older adults presenting to emergency departments (EDs) but is detected in only one-third of cases. Evidence-based guidelines for ED delirium screening exist, but are underutilized. Frontline staff perceptions about delirium and time and resource constraints are known barriers to ED delirium screening uptake. Early adopters of ED delirium screening can offer valuable lessons about successful implementation. METHODS: We conducted semi-structured interviews with clinician-administrators leading ED delirium screening initiatives from 20 EDs in the United States and Canada. Interviews focused on experiences of planning and implementing ED delirium screening. Interviews lasted 15 to 50 minutes and were digitally recorded and transcribed. To identify factors that commonly impacted implementation of ED delirium screening, we used constructs from the Consolidated Framework for Implementation Research (CFIR), an Implementation Science framework widely used to evaluate healthcare improvement initiatives. RESULTS: Overall, notable facilitators of successful implementation were having institutional and ED leadership support and designated clinical champions to longitudinally engage and educate frontline staff. We found specific examples of factors affecting implementation drawn from the following seven CFIR constructs: (1) intervention complexity, (2) intervention adaptability, (3) external policies and incentives, (4) peer pressure from other institutions, (5) the implementation climate of the ED, (6) staff knowledge and beliefs, and (7) engaging deliverers of intervention, that is, frontline ED staff. CONCLUSION: Implementing ED delirium screening is complex and requires institutional resources as well as clinical champions to engage frontline staff in a sustained fashion.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".