Examining the Relationship of an All-Cause Harm Patient Safety Measure and Critical Performance Measures at the Frontline of Care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 it