Linking Novel Diagnostics With Data-Driven Clinical Decision Support in the Emergency Department
试验速览
- 阶段
- 不适用
- 入组人数
- 300,000
- 试验地点
- 2
- 主要终点
- Critical Care
研究概览
简要总结
The primary objective of this study is to validate the use of an electronic clinical decision support (CDS) tool, TriageGO with Monocyte Distribution Width (TriageGO-MDW), in the emergency department (ED). TriageGO-MDW is non-device CDS designed to support emergency clinicians (nurses, physicians and advanced practice providers) in performing risk-based assessment and prioritization of patients during their ED visit. This study will follow an effectiveness-implementation hybrid design via the following three aims (phases), to be executed sequentially:
(Aim 1) Validate the TriageGO-MDW algorithm locally using retrospective data at ED study sites.
(Aim 2) Deploy TriageGO-MDW integrated with the electronic medical record (EMR) and perform user assessment.
(Aim 3) Evaluate TriageGO-MDW in steady state with respect to clinical, process, and perceived utility outcomes.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Other
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Adult patients receiving care at a study site ED
排除标准
- 未提供
结局指标
主要结局
Critical Care
时间窗: during post-implementation steady state (approximately 3 months after intervention)
Admission to an intensive care unit within 24 hours of ED disposition; Prediction performance of machine learning algorithms that underly TriageGO-MDW for this outcome will be measured
In-Hospital Mortality
时间窗: during post-implementation steady state (approximately 3 months after intervention)
Death during index hospital encounter; Prediction performance of machine learning algorithms that underly TriageGO-MDW for this outcome will be measured
Septic Shock
时间窗: during post-implementation steady state (approximately 3 months after intervention)
Meeting septic shock criteria within 24 hours of ED arrival; Prediction performance of machine learning algorithms that underly TriageGO-MDW for this outcome will be measured
Emergent Surgery
时间窗: during post-implementation steady state (approximately 3 months after intervention)
procedure in the operating room within 12 hours of ED arrival; Prediction performance of machine learning algorithms that underly TriageGO-MDW for this outcome will be measured
Sepsis
时间窗: during post-implementation steady state (approximately 3 months after intervention)
Prediction performance of machine learning algorithms that underlie TriageGO-MDW for this outcome will be measured
Viral Infection
时间窗: during post-implementation steady state (approximately 3 months after intervention)
Testing positive for influenza or Covid-19 (SARS-CoV-2) infection within 24 hours of ED arrival; Prediction performance of machine learning algorithms that underly TriageGO-MDW for this outcome will be measured
次要结局
未报告次要终点