Machine Learning Assisted Differentiation of Low Acuity Patients at Dispatch

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Clinical Phase

NA

Total Enrollment

2499 participants

Study Classification

INTERVENTIONAL

Study Start Date

2021-02-01

Study Completion Date

2024-11-30

Brief Summary

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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

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Detailed Description

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Conditions

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Emergencies

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Groups of patients experiencing a resource constrained situation randomized 1:1 at time of inclusion to control/intervention arms

Primary Study Purpose

HEALTH_SERVICES_RESEARCH

Blinding Strategy

SINGLE

Investigators

Analyst masked to treatment group allocation in final analysis. Outcomes extracted algorithmically from databases.

Study Groups

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Intervention

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.

Group Type EXPERIMENTAL

openTriage - Alitis algorithm

Intervention Type DIAGNOSTIC_TEST

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

Control

Ambulance dispatch per standard of care

Group Type NO_INTERVENTION

No interventions assigned to this group

Interventions

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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.

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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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

Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No