Impact of a Triage Liaison Physician on Emergency Department Overcrowding and Throughput: A Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Triage liaison physicians (TLPs) have been employed in overcrowded emergency departments (EDs); however, their effectiveness remains unclear. OBJECTIVES: To evaluate the implementation of TLP shifts at an academic tertiary care adult ED using comprehensive outcome reporting. METHODS: A six-week TLP clinical research project was conducted between December 9, 2005, and February 9, 2006. A TLP was deployed for nine hours (11 AM to 8 PM) daily to initiate patient management, assist triage nurses, answer all medical consult or transfer calls, and manage ED administrative matters. The study was divided into three two-week blocks; within each block, seven days were randomized to TLP shifts and the other seven to control shifts. Outcomes included patient length of stay, proportion of patients who left without complete assessment, staff satisfaction, and episodes of ambulance diversion. RESULTS: TLPs assessed a median of 14 patients per shift (interquartile range, 13-17), received 15 telephone calls per shift (interquartile range, 14-20), and spent 17-81 minutes per shift consulting on the telephone. The number of patients and their age, gender, and triage score during the TLP and control shifts were similar. Overall, length of stay was decreased by 36 minutes compared with control days (4:21 vs. 4:57; p = 0.001). Left without complete assessment cases decreased from 6.6% to 5.4% (a 20% relative decrease) during the TLP coverage. The ambulance wait time and number of episodes of ambulance diversion were similar on TLP and control days. CONCLUSIONS: A TLP improved important outcomes in an overcrowded ED and could improve delivery of emergency medical care in similar tertiary care EDs.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".