Linking Novel Diagnostics With Data-Driven Clinical Decision Support in the Emergency Department
Overview
- Phase
- N/A
- Intervention
- Not specified
- Conditions
- Inpatient Hospitalization, Intensive Care Unit Admission, Inpatient Mortality, Sepsis and Septic Shock
- Sponsor
- Stocastic, LLC
- Enrollment
- 300000
- Locations
- 2
- Primary Endpoint
- Critical Care
- Last Updated
- 4 years ago
Overview
Brief Summary
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.
Investigators
Eligibility Criteria
Inclusion Criteria
- •Adult patients receiving care at a study site ED
Exclusion Criteria
- Not provided
Outcomes
Primary Outcomes
Critical Care
Time Frame: 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
Time Frame: 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
Time Frame: 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
Time Frame: 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
Time Frame: 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
Time Frame: 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