Development and Implementation of Rapid Discharge Plan in a Municipal Healthcare System
Bibliographic record
Abstract
In scenarios where patient demand exceeds hospital capacity, such as natural disasters, terrorist attacks, or staffing shortages, the rapid discharge of patients who are identified through reverse triage methodologies can create surge capacity. Numerous resources and studies have evaluated this concept, but current tools tend to be extensive and siloed, which may make them difficult to use during emergencies. To prepare the largest municipal healthcare system in the United States for situations requiring rapid patient discharge, New York City Health + Hospital’s Central Office Emergency Management sought to develop a short, synthesized, and user-friendly plan. After consulting experts and reviewing existing peer-reviewed papers, gray literature, and internal facility documents, the team created a 7-page Response Action Checklist - Risk-based, Abbreviated, Patient Identification Discharge Tool (RAPID) to synthesize important content. This tool was successfully utilized during a resident labor action in May 2023, illustrating that its utility may extend beyond the system in which it was used. Future work should be done to validate and improve upon this tool.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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; a candidate call from one teacher head, 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".