Do differences in emergency medical services (EMS) response time to an arrest account for the survival differences between EMS-witnessed and bystander-witnessed out of hospital cardiac arrest?

Introduction: Out-of-hospital cardiac arrests (OHCA) witnessed by Emergency Medical Services (EMS) are reported to have more favourable survival than bystander-witnessed arrests, even after adjusting for patient and arrest factors known to be associated with increased OHCA survival. This study aims to determine whether the survival advantage in EMS-witnessed arrests can be attributed to differences in the EMS response time to the arrest.

Methods: Using registry data we conducted a retrospective, population-based cohort study of bystander- and EMS-witnessed OHCAs of medical aetiology who received an EMS resuscitation attempt in Western Australia between 2018-2021.

Out-of-hospital cardiac arrest in residential aged care facilities is independently associated with lower survival in Perth, Australia.

Aim: To compare out-of-hospital cardiac arrest (OHCA) characteristics and outcomes between people aged ≥ 65 years who arrested in a residential aged care facility (RACF) versus a private residence in Perth, Australia.

Methods: We undertook a retrospective cohort study of OHCA cases attended by emergency medical services (EMS) in Perth, January 2018-December 2021. OHCA patient and event characteristics and survival outcomes were compared via univariate analysis.

Use of ketamine wafer for pain management by volunteer emergency medical technicians in rural Western Australia.

Objective: To describe the use of sublingual ketamine wafers administered by volunteer emergency medical technicians (EMTs) for pain management to patients in rural Western Australia (WA).

Methods: This retrospective cohort study included patients older than 12 years who were attended by volunteer EMTs in Esperance, Lancelin and Kalbarri, WA and received analgesic medications from 2018 to 2021. Patients who received ketamine wafers with/without other analgesics were compared to (i) patients who received only oral paracetamol and (ii) patients who received inhalational methoxyflurane without ketamine wafers with/without paracetamol.

Ambulance dispatch prioritisation for traffic crashes using machine learning: A natural language approach.

Introduction: Demand for emergency ambulances is increasing, therefore it is important that ambulance dispatch is prioritised appropriately. This means accurately identifying which incidents require a lights and sirens (L&S) response and those that do not. For traffic crashes, it can be difficult to identify the needs of patients based on bystander reports during the emergency phone call; as traffic crashes are complex events, often with multiple patients at the same crash with varying medical needs.

A predictive ambulance dispatch algorithm to the scene of a motor vehicle crash: the search for optimal over and under triage rates.

Background: Calls for emergency medical assistance at the scene of a motor vehicle crash (MVC) substantially contribute to the demand on ambulance services. Triage by emergency medical dispatch systems is therefore important, to ensure the right care is provided to the right patient, in the right amount of time. A lights and sirens (L&S) response is the highest priority ambulance response, also known as a priority one or hot response.