The Effect of Pain on the Relationship Between Triage Acuity and Emergency Department Hospitalization Rate and Length of Stay
Bibliographic record
Abstract
OBJECTIVES: Little is known about the effect of pain on the relationship between triage and patient outcomes in United States emergency departments (ED). In this study we aimed to describe pain-associated ED visits and to explore how pain modifies the ability of ED triage to predict patient outcomes (hospitalization and ED length of stay [EDLOS)]. METHODS: We obtained data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), 2010-2021. Adult ED visits without missing data on pain score or triage level were included. We assessed pain scores at triage using a numeric rating scale (NRS) of 0-10. We further categorized the NRS scores into no (0), mild (1-3), moderate (4-6), and severe (7-10) pain. The five-level Emergency Severity Index was used for ED triage. The primary outcomes were hospital admission during the ED visit and EDLOS. For the analyses we used descriptive statistics and multivariable regression accounting for NHAMCS's complex survey design. RESULTS: Over the 12-year study period, there were 132,308 adult ED visits (representing 773,000,000 ED visits nationwide). Approximately 50% were triaged to level 3, followed by 30% to level 4. Approximately 45% reported severe pain, 21% moderate pain, 9% mild pain, and 25% no pain. Triage level 1 was associated with the highest rate of hospitalization (35%), with a gradual decrease in hospitalization rate from levels 2 to 4. Triage level 2 was associated with the longest mean EDLOS (5.6 hours), with a gradual decrease in EDLOS from levels 3 to 5. When stratified by pain intensity, the pattern of hospitalization altered in the mild and moderate pain groups. In these two pain-intensity groups, triage level 1 was associated with lower-than-expected odds of hospitalization, a 31% reduction suggested by the interaction term (adjusted odds ratio 0.69; 95% confidence interval .51-.92, P = .01). By contrast, the pattern of EDLOS persisted across all pain-intensity groups. CONCLUSION: Mild and moderate levels of pain intensity appear to negatively impact the ability of triage to predict hospitalization, resulting in overtriage among patients in these two pain-intensity groups. Pain intensity in the ED should be carefully evaluated to avoid overtriage and ensure the appropriate allocation of 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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".