Hamilton Acute Pain Service Safety Study
Bibliographic record
Abstract
BACKGROUND: Although intravenous patient-controlled analgesia opioids and epidural analgesia offer improved analgesia for postoperative patients treated on an acute pain service, these modalities also expose patients to some risk of serious morbidity and even mortality. Root cause analysis, a process for identifying the causal factor(s) that underlie an adverse event, has the potential to identify and address system issues and thereby decrease the chance of recurrence of these complications. METHODS: This study was designed to compare the incidence of adverse events on an acute pain service in three hospitals, before and after the introduction of a formal root cause analysis process. The "before" cohort included all patients with pain from February 2002 to July 2007. The "after" cohort included all patients with pain from January 2009 to December 2009. RESULTS: A total of 35,384 patients were tracked over the 7 yr of this study. The after cohort showed significant reductions in the overall event rate (1.47 vs. 2.35% or 1 in 68 vs. 1 in 42, the rate of respiratory depression (0.41 vs. 0.71%), the rate of severe hypotension (0.78 vs. 1.34%), and the rate of patient-controlled analgesia pump programming errors (0.0 vs. 0.08%). Associated with these results, the incidence of severe pain increased from 6.5 to 10.5%. To achieve these results, 26 unique recommendations were made of which 23 being completed, 1 in progress, and 2 not completed. CONCLUSIONS: Formal root cause analysis was associated with an improvement in the safety of patients on a pain service. The process was effective in giving credibility to recommendations, but addressing all the action plans proved difficult with available resources.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".