Implementing Evidence‐Based Pain Management Interventions Into an Emergency Department: Outcomes Guided by Use of the Ottawa Model of Research Use
Bibliographic record
Abstract
AIM: To implement strategies to improve the care of patients with acute pain in the emergency department (ED). DESIGN: Pre-post implementation study using a Type 2 hybrid effectiveness-implementation design. METHODS: Implementation strategies were introduced and monitored through the Ottawa Model of Research Uses' assessment, monitoring and evaluation cycles, supported by focused and sustained facilitation. RESULTS: Improvements in time-to-analgesia within 30 min (21%-27%), administration of nurse-initiated analgesia (NIA) (17%-27%) and measurement of pain (65%-75%) were achieved post-implementation. NIA was the strongest predictor of receiving analgesia within 30 min. Adoption of pain interventions into practice was not immediate yet responded to sustained facilitation of implementation strategies. CONCLUSION: Collaboration with local clinicians to introduce simple interventions that did not disrupt workflow or substantially add to workload were effective in improving analgesia administration rates, and the proportion of patients receiving analgesia within 30 min. The assessment, monitoring and evaluation cycles enabled agile and responsive facilitation of implementation activities within the dynamic ED environment. Improvements took time to embed into practice, trending upward over the course of the implementation period, supporting the sustained facilitation approach throughout the study. IMPLICATIONS: Sustained adoption of evidence-based pain interventions into the care of people presenting to the ED with acute pain can be achieved through sustained facilitation of implementation. NIA should be at the centre of acute pain management in the ED. IMPACT: This study addressed the lingering gap between evidence and practice for patients with acute pain in the ED. Implementation of locally relevant/informed implementation strategies supported by focused and sustained facilitation improved the care of patients with acute pain in the ED. This research will have an impact on people presenting to EDs with acute pain, and on clinicians treating people with acute pain in the ED. Relevant equator guidelines were followed and the StaRI reporting method used. PATIENT OR PUBLIC CONTRIBUTION: No Patient or Public Contribution in this study.
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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 | 0.017 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".