Protocol for stepped wedge cluster randomized trial to evaluate the effects of SurgeCon: A quality improvement emergency department surge management platform (Preprint)
Bibliographic record
Abstract
BACKGROUND Despite many efforts, long wait time and overcrowding at Emergency Departments (EDs) have remained a significant health system issue in Canada. For several years, Canada has had one of the longest wait times among Organisation for Economic Co-operation and Development OECD countries. From the patient’s perspective, the challenge has been described as “patients wait in pain or discomfort for hours before being seen at EDs”. In this study, we propose an innovative quality-improvement intervention called SurgeCon that includes a protocol-driven software platform and several other initiatives to reduce wait times and improve the sustainability of health systems without significant workforce changes. We piloted SurgeCon at an ED in Carbonear, Newfoundland and Labrador (NL) and found there was a 32% reduction in ED wait time. OBJECTIVE Our primary objectives of the trial are to evaluate the effects of SurgeCon on ED performance based on length of stay (LOS), time to physician’s initial assessment (PIA), and the number of patients leaving the ED without being seen by a physician (LWBS), patient satisfaction and patient-reported experience with ED wait times. The ultimate goal of this study is to create better value care by reducing the per-patient cost of delivering ED services. METHODS This study will investigate the effects of SurgeCon on health system key performance outcomes and patient-reported experience and satisfaction. The study uses a comparative effectiveness-implementation hybrid design. This type of hybrid design has been recommended to help achieve rapid translational gains that can hasten the movement of interventions from research to practice to public health impact. In our hybrid design, we will use a pragmatic stepped wedge cluster randomized trial (SW-CRT) design that enrols four 24/7 on-site ED physician support (category A) hospitals into a 30-month trial. All clusters (hospitals) start with a baseline period of “usual care” and are randomized to determine the order and timing of transitioning to “intervention care” until all hospitals are exposed to the intervention condition for the remainder of the study. RESULTS Data collection for this study is ongoing. To date, 15 randomly selected patients have participated in telephone interviews concerning patient-reported experiences and patient satisfaction with ED wait times. CONCLUSIONS By evaluating the mechanisms behind the use of SurgeCon, we hope to be able to improve wait times and create better value ED care in this healthcare context. CLINICALTRIAL This study is registered in ClinicalTrials.gov NCT04789902
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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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.127 | 0.016 |
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".