Ignite Creation Date:
2025-12-25 @ 1:31 AM
Ignite Modification Date:
2025-12-27 @ 11:05 PM
Study NCT ID:
NCT04757194
Status:
COMPLETED
Last Update Posted:
2025-01-08
First Post:
2021-02-03
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Machine Learning Assisted Differentiation of Low Acuity Patients at Dispatch
Sponsor:
Uppsala University Hospital
Organization:
Study Overview
Official Title:
Machine Learning Assisted Differentiation of Low Acuity Patients at Dispatch: A Randomized Controlled Trial
Status:
COMPLETED
Status Verified Date:
2025-01
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
MADLAD
Brief Summary:
BACKGROUND:
At Emergency Medical Dispatch (EMD) centers, Resource Constrained Situations (RCS) where there are more callers requiring an ambulance than there are available ambulances are common. At the EMD centers in Uppsala and Västmanland, patients experiencing these situations are typically assigned a low-priority response, are often elderly, and have non-specific symptoms. Machine learning techniques offer a promising but largely untested approach to assessing risks among these patients.
OBJECTIVES:
To establish whether the provision of machine learning-based risk scores improves the ability of dispatchers to identify patients at high risk for deterioration in RCS.
DESIGN:
Multi-centre, parallel-grouped, randomized, analyst-blinded trial.
POPULATION:
Adult patients contacting the national emergency line (112), assessed by a dispatch nurse in Uppsala or Västmanland as requiring a low-priority ambulance response, and experiencing an RCS.
OUTCOMES:
Primary:
1\. Proportion of RCS where the first available ambulance was dispatched to the patient with the highest National Early Warning Score (NEWS) score
Secondary:
* Difference in composite risk score consisting of ambulance interventions, emergent transport, hospital admission, intensive care, and mortality between patients receiving immediate vs. delayed ambulance response during RCS.
* Difference in NEWS between patients receiving immediate vs. delayed ambulance response during RCS.
INTERVENTION:
A machine learning model will estimate the risk associated with each patient involved in the RCS, and propose a patient to receive the available ambulance. In the intervention arm only, the assessment will be displayed in a user interface integrated into the dispatching system.
TRIAL SIZE:
1500 RCS each consisting of multiple patients randomized 1:1 to control and intervention arms
At Emergency Medical Dispatch (EMD) centers, Resource Constrained Situations (RCS) where there are more callers requiring an ambulance than there are available ambulances are common. At the EMD centers in Uppsala and Västmanland, patients experiencing these situations are typically assigned a low-priority response, are often elderly, and have non-specific symptoms. Machine learning techniques offer a promising but largely untested approach to assessing risks among these patients.
OBJECTIVES:
To establish whether the provision of machine learning-based risk scores improves the ability of dispatchers to identify patients at high risk for deterioration in RCS.
DESIGN:
Multi-centre, parallel-grouped, randomized, analyst-blinded trial.
POPULATION:
Adult patients contacting the national emergency line (112), assessed by a dispatch nurse in Uppsala or Västmanland as requiring a low-priority ambulance response, and experiencing an RCS.
OUTCOMES:
Primary:
1\. Proportion of RCS where the first available ambulance was dispatched to the patient with the highest National Early Warning Score (NEWS) score
Secondary:
* Difference in composite risk score consisting of ambulance interventions, emergent transport, hospital admission, intensive care, and mortality between patients receiving immediate vs. delayed ambulance response during RCS.
* Difference in NEWS between patients receiving immediate vs. delayed ambulance response during RCS.
INTERVENTION:
A machine learning model will estimate the risk associated with each patient involved in the RCS, and propose a patient to receive the available ambulance. In the intervention arm only, the assessment will be displayed in a user interface integrated into the dispatching system.
TRIAL SIZE:
1500 RCS each consisting of multiple patients randomized 1:1 to control and intervention arms
Detailed Description:
None
Study Oversight
Has Oversight DMC:
False
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: