Design and Implementation of a Trauma Care Bundle at a Community Hospital
Bibliographic record
Abstract
The Niagara Health System (NHS) in Ontario, Canada is comprised of three non-designated trauma center (NTC) hospitals which provide primary care to approximately 100 trauma patients annually. NTCs often lack standardized resources such as trauma surgeons, trauma-trained emergency room physicians, Advanced Trauma Life Support certified staff, trauma protocols, and other resources commonly found at designated trauma centers. Studies indicate that these differences contribute to poorer outcomes for trauma patients treated at community hospitals in Ontario, including the NTC hospitals of the NHS. In other settings healthcare checklists and bundles have proven effective in streamlining processes to ensure effective, efficient and timely patient care. Quality Improvement (QI) tools and methods were used to design, implement, and evaluate a trauma care bundle at one of the NHS's community hospitals. We assessed outcome and process measures through a chart audit of all trauma care patients in the NHS from July 2015 - November 2015. A Safety Attitudes Questionnaire (SAQ) was administered to health system staff who were involved in the pilot to assess balancing measures. Between July-November 2015, 39 patients were treated at the St. Catharines Hospital that were identified as either Canadian Triage and Acuity Scale (CTAS) I or CTAS II trauma patients. Of those 39 major trauma patients, 15 received care using the trauma care bundle, representing a 38% uptake. Patients who received care with the trauma bundle had an average Emergency Department (ED) length of stay (LOS) of 1.7 hours, compared with those patients in whom the bundle was not used, whose average ED LOS was 3.4 hours. The SAQ administered to ED physicians who used the bundle (n=10) highlighted the impact on ED patient safety. These early findings suggest that the bundle provides a substantial improvement to the current trauma care process within the Niagara Health System.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".