Use of performance reports among trauma medical directors and programme managers in the American College of Surgeons’ Trauma Quality Improvement Program: a qualitative analysis
Bibliographic record
Abstract
BACKGROUND: The American College of Surgeons' Trauma Quality Improvement Program (TQIP) provides trauma centres with performance reports on their processes and outcomes of care relative to their peers. This study explored how performance reports are used by trauma centre leaders to engage in performance improvement and perceived barriers to use. STUDY DESIGN: Qualitative focus group study with trauma medical directors (TMDs) and trauma programme managers (TPMs) in US trauma centres. Consistent with qualitative descriptive analysis, data collection and interpretation were inductively and iteratively completed. Major themes were derived using a constant comparative technique. RESULTS: Six focus groups were conducted involving 22 TMDs and 22 TPMs. Three major themes were captured: (1) technical uses of performance reports; (2) cultural uses of performance reports; (3) opportunities to enhance the role and value of TQIP. First, technical uses included using reports to assess data collection procedures, data quality and areas of poor performance relative to peers. In this domain, barriers to report use included not trusting others' data quality and challenges with report interpretation. Second, reports were used to influence practice change by fostering inter-specialty discussions, leveraging resources for quality improvement, community engagement and regional collaboratives. Perceived lack of specialist engagement was viewed as an impediment in this domain. Lastly, identified opportunities for TQIP to support report use involved clarifying the relationship between verification and performance reports, and increasing partnerships with nursing associations. CONCLUSION: Trauma centre improvement leaders indicated practical and social uses of performance reports that can affect intention and ability to change. Recommendations to optimise programme participation include a focus on data quality, adequate resource provision and enhanced support for regional collaboratives.
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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".