Machine Learning Assisted Differentiation of Low Acuity Patients at Dispatch

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