Vertical Patient Streaming in Emergency Departments
Bibliographic record
Abstract
Tackling hospital emergency department (ED) overcrowding is a paramount challenge for healthcare systems. To combat this issue, an innovative approach is to identify patients who can be served vertically (i.e., in a seated position) and route them to a dedicated area termed the Vertical Processing Pathway (VPP). Successfully implementing this design requires understanding which patients should be routed to the VPP and when. Currently, the decision to leverage the VPP is made in an ad-hoc fashion. To assist our partner hospital and other EDs in capturing the value of the VPP, we develop a machine learning model that provides personalized risk scores predicting whether each arriving patient will need an ED bed. We use these scores as input to a stochastic patient flow model and analytically characterize the optimal VPP policy that minimizes length of stay. Employing simulation analyses, we identify settings in which our proposed VPP design is preferable in terms of operational performance to traditional ED flow approaches, such as “fast track” or “physician in triage.” Finally, we derive an interpretable VPP patient streaming protocol and conduct a before-and-after experiment where we leverage empirical analyses to evaluate the impact of integrating it in practice. The implemented protocol led to an 11-minute (4.2%) reduction in ED length of stay without any adverse effect on quality-of-care outcomes. This effect was statistically significant and remained robust after controlling for confounding factors and endogeneity. Our work results in a VPP protocol generalizable to other EDs, offering operational improvements without requiring additional resources.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".