Impact of a multifaceted and multidisciplinary intervention on pain, agitation and delirium management in an intensive care unit: an experience of a Canadian community hospital in conducting a quality improvement project
Bibliographic record
Abstract
BACKGROUND: Clinical guidelines suggest that routine assessment, treatment, and prevention of pain, agitation, and delirium (PAD) is essential to improving patient outcomes as delirium is associated with increased mortality and morbidity. Despite the well-established improvements on patient outcomes, adherence to PAD guidelines is poor in community intensive care units (ICU). This quality improvement (QI) project aims to evaluate the impact of a multifaceted and multidisciplinary intervention on PAD management in a Canadian community ICU and to describe the experience of a Canadian community hospital in conducting a QI project. METHODS: A ten-member PAD advisory committee was formed to develop and implement the intervention. The intervention consisted of a multidisciplinary rounds script, poster, interviews, visual reminders, educational modules, pamphlet and video. The 4-week intervention targeted nurses, family members, physicians, and the multidisciplinary team. An uncontrolled, before-and-after study methodology was used. Adherence to PAD assessment guidelines by nurses was measured over a 6-week pre-intervention and over a 6-week post-intervention periods. RESULTS: Data on 430 and 406 patient-days (PD) were available for analysis during the pre- and post- intervention periods, respectively. The intervention did not improve the proportion of PD with guideline compliance to the assessment of pain (23.4% vs. 22.4%, p=0.80), agitation (42.9% vs. 38.9%, p=0.28), nor delirium (35.2% vs. 29.6%, p=0.10) by nurses. DISCUSSION: The implementation of a multifaceted and multidisciplinary intervention on PAD assessment did not result in significant improvements in guideline adherence in a community ICU. Barriers to knowledge translation are apparent at multiple levels including the personal level (low completion rates on educational modules), interventional level (under-collection of data), and organisational level (coinciding with hospital accreditation education). Our next steps include reintroduction of education modules using organisation approved platforms, updating existing ICU policy, updating admission order sets, and conducting audit and feedback.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 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".