Physician engagement in organisational patient safety through the implementation of a Medical Safety Huddle initiative: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Efforts to increase physician engagement in quality and safety are most often approached from an organisational or administrative perspective. Given hospital-based physicians' strong professional identification, physician-led strategies may offer a novel strategic approach to enhancing physician engagement. It remains unclear what role medical leadership can play in leading programmes to enhance physician engagement. In this study, we explore physicians' experience of participating in a Medical Safety Huddle initiative and how participation influences engagement with organisational quality and safety efforts. METHODS: We conducted a qualitative study of the Medical Safety Huddle initiative implemented across six sites. The initiative consisted of short, physician focused and led, weekly meetings aimed at reviewing, anticipating and addressing patient safety issues. We conducted 29 semistructured interviews with leaders and participants. We applied an interpretive thematic analysis to the data using self-determination theory as an analytic lens. RESULTS: The results of the thematic analysis are organised in two themes, (1) relatedness and meaningfulness, and (2) progress and autonomy, representing two forms of intrinsic motivation for engagement that we found were leveraged through participation in the initiative. First, participation enabled a sense of community and a 'safe space' in which professionally relevant safety issues are discussed. Second, participation in the initiative created a growing sense of ability to have input in one's work environment. However, limited collaboration with other professional groups around patient safety and the ability to consistently address reported concerns highlights the need for leadership and organisational support for physician engagement. CONCLUSION: The Medical Safety Huddle initiative supports physician engagement in quality and safety through intrinsic motivation. However, the huddles' implementation must align with the organisation's multipronged patient safety agenda to support multidisciplinary collaborative quality and safety efforts and leaders must ensure mechanisms to consistently address reported safety concerns for sustained physician engagement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".