A Multimodal Evaluation of an Emergency Department Electronic Tracking Board Utility Designed to Optimize Stretcher Utilization
Bibliographic record
Abstract
Objectives The primary objective of this study was to evaluate the impact of an electronic tracking board feature encouraging staff to prompt optimal patient location on total stretcher time (TST) amongst patients moved to a chair in an internal emergency department (ED) waiting room. As a secondary objective, we also sought to identify facilitators and barriers to the tool’s use amongst the ED staff. Methods Using an administrative database, a retrospective cohort design was used to compare TST between visits where the tool was used and not used amongst patients relocated from initial assessment space to a chair over an 11.5 month period. A mixed-methods design was used to investigate facilitators and barriers to the tool’s use amongst the ED staff. Response proportions were used to report Likert scale questions; thematic analysis was used to code themes. Results A total of 56,852 patients met the inclusion criteria and were moved to a chair. The tool was used 4,301 times, with “OK for chairs” selected for 3,917/56,852 (6.9%) patients and “not OK for chairs” selected 384/56,852 (0.7%) times. Patient characteristics were similar between both groups. Median interquartile range (IQR) TST amongst patients moved to a chair via the prompt was shorter than when the prompt was not used (148.2 (112.6) mins vs 154.4 (115.4) mins, p = 0.005). A total of 125 questionnaires were completed; 95% of staff were aware of the tool and 70% agreed/strongly agreed the tool could improve ED flow. Commonly reported physician barriers to use were forgetting to use the tool; common nursing barriers were lack of chair space and increased workload. Conclusions Despite low function use, prompt use was associated with reduced TST amongst ED patients relocated to a chair.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".