Machine Learning Assisted Differentiation of Low Acuity Patients at Dispatch (MADLAD)

Uppsala University Hospital

Status: Completed

Conditions: Emergencies

Treatments: Diagnostic Test: openTriage - Alitis algorithm

Study type: Interventional

Funder types: Other

Identifiers

NCT04757194

SVLC001

Details and patient eligibility

About

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

Enrollment: 2,499 patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Identification of a resource constrained situation by ambulance director (i.e., 2 or more patients awaiting an ambulance response)
• Assigned priority 2A or 2B (Low-priority ambulance response) by dispatch nurse call-taker
• Valid Swedish personal identification number collected at dispatch
• Age >= 18 years

Exclusion criteria

• Relevant calls received more than 30 minutes apart
• Logistical factors (eg. the patients' geographical locations) affect the ambulance assignment decision
• On scene risk factors (eg. a patient is outdoors and risks hypothermia) or risk mitigators (eg. healthcare staff already on-scene with a patient) affect the ambulance assignment decision

Trial design

Primary purpose: Health Services Research

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Single Blind

---

2,499 participants in 2 patient groups

Intervention

Experimental group

Description:

Calculation of risk assessment score by machine learning algorithm and display of risk assessment information to dispatch nurses. Staff encouraged but not required to comply with suggested ranking.

Treatment:

Diagnostic Test: openTriage - Alitis algorithm

Control

No Intervention group

Description:

Ambulance dispatch per standard of care