Implementation of a multimodal patient safety improvement program “SafetyLEAP” in intensive care units
Bibliographic record
Abstract
Purpose Patient safety remains a top priority in healthcare. Many organizations have developed systems to monitor and prevent harm, and have invested in different approaches to quality improvement. Despite these organizational efforts to better detect adverse events, efficient resolution of safety problems remains a significant challenge. The authors developed and implemented a comprehensive multimodal patient safety improvement program called SafetyLEAP. The term "LEAP" is an acronym that highlights the three facets of the program including: a Leadership and Engagement approach; Audit and feedback; and a Planned improvement intervention. The purpose of this paper is to evaluate the implementation of the SafetyLEAP program in the intensive care units (ICUs) of three large hospitals. Design/methodology/approach A comparative case study approach was used to compare and contrast the adherence to each component of the SafetyLEAP program. The study was conducted using a convenience sample of three ( n=3) ICUs from two provinces. Two reviewers independently evaluated major adherence metrics of the SafetyLEAP program for their completeness. Analysis was performed for each individual case, and across cases. Findings A total of 257 patients were included in the study. Overall, the proportion of the SafetyLEAP tasks completed was 64.47, 100, and 26.32 percent, respectively. ICU nos 1 and 2 were able to identify opportunities for improvement, follow a quality improvement process and demonstrate positive changes in patient safety. The main factors influencing adherence were the engagement of a local champion, competing priorities, and the identification of appropriate resources. Practical implications The SafetyLEAP program allowed for the identification of processes that could result in patient harm in the ICUs. However, the success in improving patient safety was dependent on the engagement of the care teams. Originality/value The authors developed an evidence-based approach to systematically and prospectively detect, improve, and evaluate actions related to patient safety.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".