Knowledge translation and process improvement interventions increased pain assessment documentation in a large quaternary paediatric post-anaesthesia care unit
Bibliographic record
Abstract
BACKGROUND: Due to inadequate pain assessment documentation in our paediatric post-anaesthetic care unit (PACU), we were unable to monitor pain intensity, and target factors contributing to moderate and severe postoperative pain in children. The purpose of this study was to improve pain assessment documentation in PACU through a process improvement intervention and knowledge translation (KT) strategy. The study was set in a PACU within a large university affiliated paediatric hospital. Participants included PACU and Acute Pain Service nursing staff, administrative staff and anaesthesiologists. METHODS: The Plan-Do-Study-Act method of quality improvement was used. Benchmark data were obtained by chart review of 99 patient medical records prior to interventions. Data included pain assessment documentation (pain intensity score, use of validated pain intensity measure) during PACU stay. Repeat chart audit took place at 4, 5 and 6 months after the intervention. INTERVENTION: Key informant interviews were conducted to identify barriers to pain assessment documentation. A process improvement was implemented whereby the PACU flowsheets were modified to facilitate pain assessment documentation. KT strategy was implemented to increase awareness of pain assessment documentation and to provide the knowledge, skill and judgement to support this practice. The KT strategy was directed at PACU nursing staff and comprised education outreach (educational meetings for PACU nurses, discussions at daily huddles), reminders (screensavers, bedside posters, email reminders) and feedback of audit results. RESULTS: The proportion of charts that included at least one documented pain assessment was 69%. After intervention, pain assessment documentation increased to >90% at 4 and 5 months, respectively, and to 100% after 6 months. CONCLUSION: After implementing process improvement and KT interventions, pain assessment documentation improved. Additional work is needed in several key areas, specifically monitoring moderate to severe pain, in order to target factors contributing to significant postoperative pain in children.
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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 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".