Optimal governance of patient safety: A qualitative study on barriers to and facilitators for effective internal audit
Bibliographic record
Abstract
Objective: While internal audits are widely used, insight into the essential components of the internal audit to govern patient safety is limited. The aim of this study is to explore factors that hinder and stimulate internal audits as an effective patient safety governance tool for hospital boards.Methods: A qualitative interview study in six Dutch hospitals. Interviews (n = 43) were held with auditees, quality officers, boards of directors and boards of supervisors. Data were collected and analysed using Grounded Theory.Results: Barriers and facilitators were classified into 14 categories from which four themes emerged: (1) board positioning of audits, (2) organisation and content of audits, (3) competences and composition of audit team, and (4) cultural factors and attitudes towards auditing.Conclusions: We found two themes consisting of factors related to the audit itself (organisation and content of audits, and competences and composition of audit team) and two themes consisting of contextual factors (board positioning of audits, and cultural factors and attitudes towards auditing). These may contribute to support for auditing and to the generation of reliable audit results, which subsequently could result in effective audits for governance of patient safety. Hospital boards and executives can optimise the patient safety auditing system in their hospitals by increasing active leadership engagement, by promoting audits as an opportunity for staff to learn from safety problems (rather than a mandatory examination instrument) and by providing vital resources for a smooth audit process, such as a medical specialist in the audit team.
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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 | 0.020 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".