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Machine Learning Assisted Differentiation of Low Acuity Patients at Dispatch

NCT04757194 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 2499

Last updated 2025-01-08

No results posted yet for this study

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

Conditions

  • Emergencies

Interventions

DIAGNOSTIC_TEST

openTriage - Alitis algorithm

A machine learning algorithm (Gradient boosting) applied to structured data collected in the Alitis Clinical Decision Support system, patient demographics, and free-text notes.

Sponsors & Collaborators

  • Region Västmanland

    collaborator OTHER

  • Uppsala University Hospital

    lead OTHER

Principal Investigators

  • Hans Blomberg, MD, PhD · Uppsala University Hospital

Study Design

Allocation
RANDOMIZED
Purpose
HEALTH_SERVICES_RESEARCH
Masking
SINGLE
Model
PARALLEL

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2021-02-01
Primary Completion
2024-11-30
Completion
2024-11-30

Countries

  • Sweden

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04757194 on ClinicalTrials.gov