Impact of a standardized admissions process using a nurse intermediary
Bibliographic record
Abstract
Objective: Transitions of care, including those between the Emergency Department (ED) and Internal Medicine (IM) for hospital admissions are complicated, variable processes that impact efficiency and patient safety. At our institution, a new, standardized admissions process that involved a nurse coordinator intermediary who served a dual role of facilitating admissions and overseeing bed board was implemented in July 2017. We aimed to evaluate the impact of the new process on ED throughput and safety outcomes of admitted patients.Methods: A retrospective analysis of the admissions process for patients at an urban, academic ED was conducted over a 4-month period preceding and following process implementation. ED metrics, including admission decision to ED departure time, were reviewed. In addition, the number of admitted patients upgraded to the intensive care unit (ICU) via a rapid response team (RRT-ICU) within 24 hours of admission and direct physician-physician handoffs were analyzed via surveys of both IM and EM physicians.Results: A total of 1,109 admissions were reviewed. The new admissions process resulted in a statistically significant decrease in boarding times for admitted ED patients (p = .03). The number of RRT-ICUs within 24 hours of admission did not change as a result of the intervention (p = .5). Direct physician handoffs increased, but not significantly, according to surveys of IM (p = .39) and EM physicians (p = .34).Conclusions: The implementation of a standardized admissions process utilizing a nurse intermediary improved provider communication and ED throughput without negatively impacting patient safety.
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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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".