Implementation of surgical debriefing programs in large health systems: an exploratory qualitative analysis
Bibliographic record
Abstract
BACKGROUND: The role of the "debrief" to address issues related to patient safety and systematic flaws in care is frequently overlooked. In our study, we interview surgical leaders who have developed successful strategies of debriefing within a comprehensive program of quality improvement. METHODS: Semi-structured interviews of four implementation leaders were performed. The observations, beliefs and strategies of surgical leaders are compared and contrasted. Common themes are identified related to program success and failure. Quality and safety researchers performed, coded and categorized the interviews and coordinated the analysis and interpretation of the results. The authors from the four institutions aided in interpretation and framing of the results. RESULTS: The debriefing programs evaluated were part of comprehensive quality improvement projects. Seven high-level themes and 24 subthemes were identified from the interviews. Themes related to leadership included early engagement, visible ongoing commitment and enforcement. Success appeared to depend upon meaningful and early debriefing feedback. The culture of safety that promoted success included a commitment to open and fair communication and continuous improvement. There were many challenges to the success of debriefing programs. The loss of institutional commitment of resources and personnel was the instigating factor behind the collapse of the program at Michigan. Other areas of potential failure included communication issues and loss of early and meaningful feedback. CONCLUSIONS: Leaders of four surgical systems with strong debriefing programs report success using debriefing to improve system performance. These findings are consistent with previously published studies. Success requires commitment of resources, and leadership engagement. The greatest gains may be best achieved by programs that provide meaningful debriefing feedback in an atmosphere dedicated to open communication.
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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 | 0.026 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".