Development of Nurse-Sensitive, Emergency Department–Specific Quality Indicators Using a Modified Delphi Technique
Bibliographic record
Abstract
BACKGROUND: There is no identified set of nursing-sensitive, emergency department (ED)-specific quality indicators. PURPOSE: The purpose of this study was to address the gap in quality indicators specific to the emergency care environment and identify a list of nursing-sensitive, ED-specific quality indicators across ED populations and phases of the ED visit for further development and testing. METHODS: A modified Delphi technique was used to reach initial consensus. RESULTS: Four thematic groups were identified, and quality indicators within each were rank ordered. Of the 4 groups, 21 quality indicators were identified: triage (6) was ranked highest, followed by special populations (4), transitions of care (4), and medical/surgical (7). CONCLUSIONS: Many of the recommended metrics were questionable because they are nonspecific to the ED setting or subject to influences in the emergency care environment. Some identified priorities for quality indicator development were unsupported; we recommend that alternate methodologies be used to identify critical areas of quality measurement.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".