From Reporting to Improving: How Root Cause Analysis in Teams Shape Patient Safety Culture
Bibliographic record
Abstract
Christos Tsamasiotis,1 Gaelle Fiard,2 Pierre Bouzat,3 Patrice François,1 Guillaume Fond,4 Laurent Boyer,4 Bastien Boussat1,5 1Department of Clinical Epidemiology, Grenoble-Alps University Hospital; Laboratory TIMC-IMAG, UMR 5525 Joint Research Unit, National Center for Scientific Research, Faculty of Medicine, Grenoble Alps University, Grenoble, France; 2Department of Urology, Grenoble Alpes University Hospital, CNRS, Grenoble INP, TIMC, Université Grenoble Alpes, Grenoble, France; 3Department of Anaesthesia and Intensive Care, Centre Hospitalier Universitaire Grenoble, University Grenoble Alpes; INSERM U1216, Grenoble Institut Neurosciences, Grenoble, France; 4AP-HM, Aix-Marseille University, School of Medicine - La Timone Medical Campus, UR3279: Health Service Research and Quality of Life Center (CEReSS), Marseille, France; 5O’Brien Institute for Public Health, University of Calgary, Calgary, Alberta, CanadaCorrespondence: Bastien Boussat, Service d’épidémiologie et évaluation médicale, CHU Grenoble-Alpes, Boulevard de la Chantourne, La Tronche, 38700, France, Tel +33 4 76 76 69 96, Email bboussat@chu-grenoble.frBackground: Given the increasing focus on patient safety in healthcare systems worldwide, understanding the impact of Continuous Quality Improvement Programs (QIPs) is crucial. QIPs, including Morbidity and Mortality Conferences (MMCs) and Experience Feedback Committees (EFCs), have been identified as effective strategies for enhancing patient safety culture. These programs engage healthcare professionals in the identification and analysis of adverse events to foster a culture of safety (ie the product of individual and group value, attitudes, and perceptions about quality and safety). This study aimed to determine whether patient safety culture differed regarding care provider participation in MMCs and EFCs activities.Methods: A cross-sectional web-only survey was conducted in 2022 using the Hospital Survey on Patient Safety Culture (HSOPS) among 4780 employees at an 1836-bed, university-affiliated hospital in France. We quantified the mean differences in the 12 HSOPS dimension scores according to MMCs and EFCs participation, using Cohen d effect size. We performed a multivariate analysis of variance to examine differences in dimension scores after adjusting for background characteristics.Results: Of 4780 eligible employees, 1457 (30.5%) participated in the study. Among the respondents, 571 (39.2%) participated in MMCs or EFCs activities. Participants engaged in MMCs or EFCs reported significantly higher scores in six out of twelve HSOPS dimensions, particularly in “Nonpunitive response to error”, “Feedback and communication about error”, and “Organizational learning” (Overall effect size = 0.14, 95% confidence interval = 0.11 to 0.17, P< 0.001). Notably, involvement in both MMCs and EFCs was associated with higher improvements in patient safety culture compared to non-participation or singular involvement in either program. However, certain dimensions such as “Staffing”, “Hospital management support”, and “Hospital handoffs and transition” showed no significant association with MMCs or EFCs participation, highlighting broader systemic challenges.Conclusion: The study confirms the positive association between participation in MMCs or EFCs and an enhanced culture of patient safety, emphasizing the importance of such programs in fostering an environment conducive to learning, communication, and nonpunitive responses to errors. While MMCs or EFCs are effective in promoting certain aspects of patient safety culture, addressing broader systemic challenges remains crucial for comprehensive improvements in patient safety.Keywords: patient safety culture, quality improvement programs, morbidity and mortality conferences, experience feedback committees
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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 | 0.153 | 0.510 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.023 | 0.016 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".