Implementation of an ED surge management platform: a study protocol
Bibliographic record
Abstract
BACKGROUND: Emergency departments (EDs) around the world are struggling with long wait times and overcrowding. To address these issues, a quality improvement program called SurgeCon was created to improve ED efficiency and patient satisfaction. This paper presents a framework for managing and evaluating the implementation of an ED surge management platform. Our framework builds on the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework to structure our approach and the Consolidated Framework for Implementation Research (CFIR) to guide our choice of outcome variables and scalability. METHODS: Four hospital EDs will receive the SurgeCon quality improvement intervention. Using a stepped wedge cluster design, each ED will be randomized to one of four start dates. Data will be collected before, during, and after the implementation of the intervention. RE-AIM will be used to guide the assessment of SurgeCon, and guided by CFIR, we will measure ED key performance indicators (KPI), patient-reported outcomes, and implementation outcomes related to SurgeCon's scalability, adaptability, sustainability, and overall costs. Participants in this study consist of patients who visit any of the four selected EDs during the study period, providers/staff, and health system managers. A mixed-methods approach will be utilized to evaluate implementation outcomes. DISCUSSION: This study will provide important insight into the implementation and evaluation techniques to enhance uptake and benefits associated with an ED surge-management platform. The proposed framework bridges research and practice by involving researchers, practitioners, and patients in the implementation and evaluation process, to produce an actionable framework that others can follow. We anticipate that the implementation approach would be generalizable to program implementations in other EDs. TRIAL REGISTRATION: • Name of the registry: ClinicalTrials.gov • Trial registration number: NCT04789902 • Date of registration: 03/10/2021.
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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 | 0.087 | 0.039 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.043 | 0.015 |
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".