The Development of Indicators to Measure the Quality of Clinical Care in Emergency Departments Following a Modified-Delphi Approach
Bibliographic record
Abstract
OBJECTIVE: To develop and apply a systematic approach to identify and define valid, relevant, and feasible measures of emergency department (ED) clinical performance. METHODS: An extensive literature review was conducted to identify clinical conditions frequently treated in most EDs, and clinically relevant outcomes to evaluate these conditions. Based on this review, a set of condition-outcome pairs was defined. An expert panel was convened and a Modified-Delphi process was used to identify specific condition-outcome pairs where the panel felt there was a link between quality of care for the condition and a specific outcome. Next, for highly rated condition-outcome pairs, specific measurable indicators were identified in the literature. The panelists rated these indicators on their relevance to ED performance and need for risk adjustment. The feasibility of calculating these indicators was determined by applying them to a routinely collected data set. RESULTS: Thirteen clinical conditions and eight quality-of-care outcomes (mortality, morbidity, admissions, recurrent visits, follow-up with primary care, length of stay, diagnostics, and resource use) were identified from the literature (104 pairs). The panel selected 21 condition-outcome pairs, representing eight of 13 clinical conditions. Then, the panel selected 29 specific clinical indicators, representing the condition-outcome pairs, to measure ED performance. It was possible to calculate eight of these indicators, covering five clinical conditions, using a routinely collected data set. CONCLUSIONS: Using a Modified-Delphi process, it was possible to identify a series of condition-outcome pairs that panelists felt were potentially related to ED quality of care, then define specific indicators for many of these condition-outcome pairs. Some indicators could be measured using an existing data set. The development of sound clinical performance indicators for the ED is possible, but the feasibility of measuring them will be dependent on the availability and accessibility of high-quality data.
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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 | 0.271 | 0.298 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".