‘How are we going to harm the next trauma patient?’ Trauma care providers’ perspective on potential harm to trauma patients
Bibliographic record
Abstract
Background: The question, "How will the next patient be harmed?" is a component of strategies used to identify latent safety risks in healthcare. We sought to survey a broad audience attending the 2023 annual conference of the American College of Surgeons-Trauma Quality Improvement Program to record their perception of the risks that might lead to patient harm at their own trauma centers. Methods: Attendees were surveyed with a single free-text question "How are we going to harm the next patient?" using a quick response code. All responses were categorized into clustered themes. To report the results using a standardized reporting taxonomy, the responses were also classified according to the Joint Commission (JC) patient safety event taxonomy for near misses and adverse events. Results were reported as counts and as proportions of responders. Results: During the 3-day duration of the conference, 64 participants provided 80 responses. Provider-related risk (n=16, 25.0%) was the most commonly reported category, followed closely by practice management guideline related (n=14, 21.9%) and communication gaps or failures (n=12, 18.8%). "Clinical performance" was the most commonly reported subclassification in the main category "type" of the JC patient safety event taxonomy (n=34, 53.1%), followed by patient management (n=30, 46.9%). "Human error" was the most common subclassification in the main category "cause" (n=48, 75.0%). Conclusions: Human failures, rather than systems issues, were perceived to be responsible for the majority of potential harm in trauma patients by a broad audience of trauma care providers. These results require amplified focus on strategies that decrease the impact of the human element while enhancing process standardization and addressing barriers to the implementation of processes and guidelines. It might also suggest an opportunity to bring forward alternative conceptual frameworks to advance safety in trauma care.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".