Improving trauma tertiary survey performance and missed injury identification using an education-based quality improvement initiative
Bibliographic record
Abstract
BACKGROUND: Performance of a trauma tertiary survey (TTS) reduces rates of missed injuries, but performance has been inconsistent at trauma centers. The objectives of this study were to assess whether quality improvement (QI) efforts would increase the frequency of TTS documentation and determine if TTS documentation would increase identification of traumatic injuries. Our hypothesis was that QI efforts would improve documentation of the TTS. METHODS: Before-and-after analysis of QI interventions at a level 1 trauma center was performed. The interventions included an electronic template for TTS documentation, customized educational sessions, and emphasis from trauma leadership on TTS performance. The primary outcome was documentation of the TTS. Detection of additional injuries based on tertiary evaluation was a secondary outcome. Associations between outcomes and categorical patient and encounter characteristics were assessed using χ2 tests. RESULTS: Overall, 592 trauma encounters were reviewed (296 preimplementation and 296 postimplementation). Trauma tertiary survey documentation was significantly higher after implementation of the interventions (30.1% preimplementation vs. 85.1% postimplementation, p < 0.001). Preimplementation documentation of the TTS was less likely earlier in the academic year (14.3% first academic quarter vs. 46.5% last academic quarter, p < 0.001), but this temporal pattern was no longer evident postimplementation (88.5% first academic quarter vs. 77.9% last academic quarter, p = 0.126). Patients were more likely to have a missed traumatic injury diagnosed on TTS postimplementation (1.7% in preimplementation vs. 5.7% postimplementation, p = 0.009). CONCLUSION: Documentation of the TTS and missed injury detection rates were significantly increased following implementation of a bundle of QI interventions. The association between time of year and documentation of the TTS was also attenuated, likely through reduction of the resident learning curve. Targeted efforts to improve TTS performance may improve outcomes for trauma patients at teaching hospitals. LEVEL OF EVIDENCE: Care management, Level IV.
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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 | 0.020 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".