Error reduction in trauma care: Lessons from an anonymized, national, multicenter mortality reporting system
Bibliographic record
Abstract
BACKGROUND Twenty years ago, the landmark report To Err Is Human illustrated the importance of system-level solutions, in contrast to person-level interventions, to assure patient safety. Nevertheless, rates of preventable deaths, particularly in trauma care, have not materially changed. The American College of Surgeons Trauma Quality Improvement Program developed a voluntary Mortality Reporting System to better understand the underlying causes of preventable trauma deaths and the strategies used by centers to prevent future deaths. The objective of this work is to describe the factors contributing to potentially preventable deaths after injury and to evaluate the effectiveness of strategies identified by trauma centers to mitigate future harm, as reported in the Mortality Reporting System. METHODS An anonymous structured web-based reporting template based on the Joint Commission on Accreditation of Healthcare Organizations taxonomy was made available to trauma centers participating in the Trauma Quality Improvement Program to allow for reporting of deaths that were potentially preventable. Contributing factors leading to death were evaluated. The effectiveness of mitigating strategies was assessed using a validated framework and mapped to tiers of effectiveness ranging from person-focused to system-oriented interventions. RESULTS Over a 2-year period, 395 deaths were reviewed. Of the mortalities, 33.7% were unanticipated. Errors pertained to management (50.9%), clinical performance (54.7%), and communication (56.2%). Human failures were cited in 61% of cases. Person-focused strategies like education were common (56.0%), while more effective system-based strategies were seldom used. In 7.3% of cases, centers could not identify a specific strategy to prevent future harm. CONCLUSION Most strategies to reduce errors in trauma centers focus on changing the performance of providers rather than system-level interventions such as automation, standardization, and fail-safe approaches. Centers require additional support to develop more effective mitigations that will prevent recurrent errors and patient harm. LEVEL OF EVIDENCE Therapeutic/Care Management, level V.
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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 | 0.183 | 0.279 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".