Machine Learning Assisted Differentiation of Low Acuity Patients at Dispatch

NCT04757194 | Completed

• 1 other identifier: SVLC001
• Study Type: interventional
• Target: 2,499
• Locations: 1 country
• Sites: 2

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

Conditions

Emergencies

Outcome Measures

Primary Outcomes (1)

• Proportion of RCS where the first available ambulance was dispatched to the patient with the highest National Early Warning Score (NEWS).

  NEWS is a widely used and well-validated scoring algorithm based on objective patient vital signs, which are not causally dependent on the outcomes used to train the machine learning models investigated in this study. NEWS values will be based on the first set of vital signs obtained by ambulance nurses upon making contact with the patient. NEWS is measured on a 0-21 scale, with higher values corresponding to patients at higher risk for deterioration.

  Upon ambulance response (Within 8 hours of dispatch)

Secondary Outcomes (2)

• Difference in composite outcome measure score between patients with immediate vs. delayed response.

  Up to 30 days

• Difference in National Early Warning Score (NEWS) between patients with immediate vs. delayed response.

  Upon ambulance response (Within 8 hours of dispatch)

Study Arms (2)

Intervention

EXPERIMENTAL

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.

Diagnostic Test: openTriage - Alitis algorithm

Control

NO INTERVENTION

Ambulance dispatch per standard of care

Interventions

openTriage - Alitis algorithm 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.

Intervention

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• Uppsala University Hospital: lead
• Region Västmanland: collaborator

Study Sites (2)

Västmanland hospital Västerås

Västerås, Västmanland County, Sweden

Location

Uppsala University Hospital

Uppsala, Sweden

Location

Related Publications (2)

• 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.

  PMID: 31834920 BACKGROUND

• 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.

  PMID: 32198303 BACKGROUND

Related Links

• Source code for risk assessment tool used in intervention

MeSH Terms

Conditions

Emergencies

Condition Hierarchy (Ancestors)

Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Hans Blomberg, MD, PhD

  Uppsala University Hospital

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: INVESTIGATOR
• Masking Details: Analyst masked to treatment group allocation in final analysis. Outcomes extracted algorithmically from databases.
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Medical Director

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

Study Record Dates

• First Submitted: February 3, 2021
• First Posted: February 17, 2021
• Study Start: February 1, 2021
• Primary Completion: November 30, 2024
• Study Completion: November 30, 2024
• Last Updated: January 8, 2025

Record last verified: 2025-01

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, ANALYTIC CODE
• Time Frame: Upon publication
• Access Criteria: Researchers with ethics board approved research plan

Locations

SE (2)