Evaluating the Effectiveness of Concurrent Review: Does It Improve Stroke Measure Results?
Bibliographic record
Abstract
BACKGROUND: Concurrent review is a quality improvement strategy in which patients are tracked from admission to discharge, and messages are communicated to the responsible physician when quality stroke measures have not been met. There is little research regarding interventions that might influence clinical practice patterns and improvement in compliance with core quality measures. This study sought to evaluate whether concurrent review implementation was associated with change in performance on stroke measure outcome data. METHODS: Randomly selected charts from 2 hospitals (A and B) during 3 time periods were reviewed. In period 1, neither hospital had a process for concurrent review. In period 2, hospital A, where concurrent review was implemented, was compared with hospital B without this process. In period 3, both hospitals had the process of concurrent review. Information on baseline demographics, insurance status, and length of stay was collected, as well as stroke performance measures. RESULTS: A total of 620 medical records were reviewed during the 3 time periods. Although the number of beds and annual stroke volume were higher at hospital B, patient characteristics were similar. During period 2, when hospital A implemented concurrent review and hospital B had not, a statistically significant higher compliance with performance in 7 stroke measures occurred in hospital A than in hospital B. In period 3, when both hospitals utilized concurrent review, no statistical significant differences occurred in 7 of the 10 stroke measures. CONCLUSION: Concurrent review is a quality improvement intervention that increases performance with stroke performance measures.
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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 | 0.192 | 0.505 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".