Quality Measurement In The Emergency Department: Past And Future
Bibliographic record
Abstract
As the United States seeks to improve the value of health care, there is an urgent need to develop quality measurement for emergency departments (EDs). EDs provide 130 million patient visits per year and are involved in half of all hospital admissions. Efforts to measure ED quality are in their infancy, focusing on a small set of conditions and timeliness measures, such as waiting times and length-of-stay. We review the history of ED quality measurement, identify policy levers for implementing performance measures, and propose a measurement agenda. Initial priorities include measures of effective care for serious conditions that are commonly seen in EDs, such as trauma; measures of efficient use of resources, such as high-cost imaging and hospital admission; and measures of diagnostic accuracy. More research is needed to support the development of measures of care coordination and regionalization and the episode cost of ED care. Policy makers can advance quality improvement in ED care by asking ED researchers and organizations to accelerate the development of quality measures of ED care and incorporating the measures into programs that publicly report on quality of care and incentive-based payment systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.120 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.013 | 0.027 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".