Examining the Relationship of an All-Cause Harm Patient Safety Measure and Critical Performance Measures at the Frontline of Care
Bibliographic record
Abstract
BACKGROUND: In 2015, the Institute of Medicine Vital Signs report called for a new patient safety composite measure to lessen the reporting burden of patient harm. Before this report, two patient safety organizations had developed an electronic all-cause harm measurement system leveraging data from the electronic health record, which identified and grouped harms into five broad categories and consolidated them into one all-cause harm outcome measure. OBJECTIVES: The objective of this study was to examine the relationship between this all-cause harm patient safety measure and the following three performance measures important to overall hospital safety performance: safety culture, employee engagement, and patient experience. METHODS: We studied the relationship between all-cause harm and three performance measures on eight inpatient care units at one hospital for 7 months. RESULTS: The findings demonstrated strong correlations between an all-cause harm measure and patient safety culture, employee engagement, and patient experience at the hospital unit level. Four safety culture domains showed significant negative correlations with all-cause harm at a P value of 0.05 or less. Six employee engagement domains were significantly negatively correlated with all-cause harm at a P value of 0.01 or less, and six of the ten patient experience measures were significantly correlated with all-cause harm at a P value of 0.05 or less. CONCLUSIONS: The results show that there is a strong relationship between all-cause harm and these performance measures indicating that when there is a positive patient safety culture, a more engaged employee, and a more satisfying patient experience, there may be less all-cause harm.
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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 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".