Clinical trial NCT04757194: Machine Learning Assisted Differentiation of Low Acuity Patients at Dispatch
Official title: Machine Learning Assisted Differentiation of Low Acuity Patients at Dispatch: A Randomized Controlled Trial
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Emergencies
Lead sponsor: Uppsala University Hospital
Sponsor class: OTHER
Collaborators: name: Region Västmanland
First posted: 2021-02-17
Enrollment: 2499
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Intervention model description: Groups of patients experiencing a resource constrained situation randomized 1:1 at time of inclusion to control/intervention arms
Primary purpose: HEALTH_SERVICES_RESEARCH

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


Eligibility criteria: 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

Primary outcomes:
measure: Proportion of RCS where the first available ambulance was dispatched to the patient with the highest National Early Warning Score (NEWS).
timeFrame: Upon ambulance response (Within 8 hours of dispatch)
Secondary outcomes:
measure: Difference in composite outcome measure score between patients with immediate vs. delayed response.
timeFrame: Up to 30 days
measure: Difference in National Early Warning Score (NEWS) between patients with immediate vs. delayed response.
timeFrame: Upon ambulance response (Within 8 hours of dispatch)
Other outcomes:
Locations:
city: Västerås
country: Sweden
facility: Västmanland hospital Västerås
state: Västmanland
city: Uppsala
country: Sweden
facility: Uppsala University Hospital
Study officials:
affiliation: Uppsala University Hospital
name: Hans Blomberg, MD, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Spangler D, Hermansson T, Smekal D, Blomberg H. A validation of machine learning-based risk scores in the prehospital setting. PLoS One. 2019 Dec 13;14(12):e0226518. doi: 10.1371/journal.pone.0226518. eCollection 2019.
citation: Spangler D, Edmark L, Winblad U, Collden-Benneck J, Borg H, Blomberg H. Using trigger tools to identify triage errors by ambulance dispatch nurses in Sweden: an observational study. BMJ Open. 2020 Mar 19;10(3):e035004. doi: 10.1136/bmjopen-2019-035004.