Quality improvement in hospitals: barriers and facilitators
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine quality improvement (QI) initiatives in acute care hospitals, the factors associated with success, and the impacts on patient care and safety. Design/methodology/approach An extensive online survey was completed by senior managers responsible for QI. The survey assessed QI project types, QI methods, staff engagement, and barriers and factors in the success of QI initiatives. Findings The response rate was 37 percent, 46 surveys were completed from 125 acute care hospitals. QI initiatives had positive impacts on patient safety and care. Staff in all hospitals reported conducting past or present hand-hygiene QI projects and C. difficile and surgical site infection were the next most frequent foci. Hospital staff not having time and problems with staff prioritizing QI with other duties were identified as important QI barriers. All respondents reported hospital leadership support, data utilization and internal champions as important QI facilitators. Multiple regression models identified nurses' active involvement and medical staff engagement in QI with improved patient care and physicians' active involvement and medical staff engagement with greater patient safety. Practical implications There is the need to study how best to support and encourage physicians and nurses to become more engaged in QI. Originality/value QI initiatives were shown to have positive impacts on patient safety and patient care and barriers and facilitating factors were identified. The results indicated patient care and safety would benefit from increased physician and nurse engagement in QI initiatives.
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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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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 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".