Improving timely analgesia administration for musculoskeletal pain in the emergency department
Why this work is in the frame
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Bibliographic record
Abstract
Delays to adequate analgesia result in worse patient care, decreased patient and provider satisfaction and increased patient complaints. The leading presenting symptom to emergency departments (EDs) is pain, with approximately 34 000 such patients per year in our academic hospital ED and 3300 visits specific for musculoskeletal (MSK) injuries. Our aim was to reduce the time-to-analgesia (TTA; time from patient triage to receipt of analgesia) for patients with MSK pain in our ED by 55% (to under 60 min) in 9 months' time (May 2018). Our outcome measures included mean TTA and ED length of stay (LOS). Process measures included rates of analgesia administration and of use of medical directives. We obtained weekly data capture for Statistical Process Control (SPC) charts, as well as Mann-Whitney U tests for before-and-after evaluation. We performed wide stakeholder engagement, root cause analyses and created a Pareto Diagram to inform Plan-Do-Study-Act (PDSA) cycles, which included: (1) nurse-initiated analgesia at triage; (2) a new triage documentation aid for medication administration; (3) a quick reference medical directive badge for nurses; and (4) weekly targeted feedback of the project's progress at clinical team huddle. TTA decreased from 129 min (n=153) to 100 min (22.5%; n=87, p<0.05). Special cause variation was identified on the ED LOS SPC chart with nine values below the midline after the first PDSA. The number of patients that received any analgesia increased from 42% (n=372) to 47% (n=192; p=0.13) and those that received them via medical directives increased from 22% (n=154) to 44% (n=87; p<0.001). We achieved a significant reduction of TTA and an increased use of medical directives through front-line focused improvements.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it