Developing a set of performance measures to monitor and evaluate delirium care quality for older adults in the emergency department
Bibliographic record
Abstract
Background. Older adults are at high-risk of developing delirium in the emergency department (ED). However, it often goes undetected or undertreated. Performance measures (PMs) are needed to identify variations in delirium care quality and to help guide improvement strategies. Purpose. The purpose of this research was to develop a set of guideline-based PMs to monitor and evaluate delirium care quality for older ED patients. Methods. Research was conducted in two sequential phases, such that knowledge from Phase 1 was used to inform Phase 2. In the first phase, I conducted an umbrella review to identify and synthesize clinical practice guideline (CPG) recommendations for delirium care in older adults. The results of Phase 1 were used to develop a preliminary set of PMs, as well as their necessary precursory ‘quality statements’. In the second phase, I conducted a 3-round modified e-Delphi to reach clinical expert consensus on a final set of ED quality statements that are important and actionable for delirium care of older ED patients, and PMs that are necessary to evaluate this care. Results. In Phase 1, 5 of 10 CPGs met criteria for inclusion in the synthesis. Included recommendations (n = 78) were grouped into four categories: screening, diagnosis, risk reduction, and management. None of the included CPGs were ED-specific but many recommendations incorporated evidence from this setting. From this synthesis, a preliminary set of 10 quality statements and 24 PMs were developed. Twenty-two experts participated in Phase 2. Panelists reached consensus at or slightly below a priori criteria on nine quality statements, nine structure PMs, and 14 process PMs. Conclusion. This research created 23 PMs that a diverse group of experts agree should be used to monitor and evaluate delirium care quality for older adults in the ED. Developing this set of PMs advances the knowledge base by demonstrating how to develop new ED PMs instead of deriving them from previous work, as well as providing an example of rigorous methods to develop guideline-based PMs. Future research will test the feasibility of using these metrics to provide baseline data and guide delirium care improvement efforts in the ED.
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 | 0.203 | 0.343 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".