Emergency System Strain, Code Black Events, and Climate Change in Thunder Bay, Ontario: A Data-Driven Evaluation of Contributing Factors and Predictive Insights

Abstract

Background: Code Black ambulance events, a blackout period in emergency medical services, occur when paramedics are unable to be dispatched on new calls. Typically, due to delays in transferring previous patients from paramedic care to hospital staff in the ED, Code Blacks pose a serious risk to public health and safety. At the Thunder Bay Regional Health Sciences Centre (TBRHSC) and Superior North EMS, Code Black events have become a common occurrence. This Master's thesis aims to investigate the underlying causes of Code Black events and EMS strain by analyzing ED and EMS data. It will also examine the influence of climate change and extreme weather conditions on Code Blacks.

Research Question: The objective of this thesis is to understand the factors contributing to Code Black events and EMS strain in Thunder Bay, and how an exploratory analysis of ED, EMS, and climate data, along with machine learning methods, can be used to understand system overload.

Methods: This retrospective observational study integrates perspectives from public health, emergency medicine, and climate change to address the research question. ED, EMS, and climate data will be used to identify patterns and predictive indicators of Code Black events at TBRHSC. A variety of methods will be employed to address the research question, including descriptive statistical analyses, data visualization techniques, and machine learning approaches.

Expected Outcomes: The goal of this thesis is to identify the factors contributing to Code Black events and system strain to develop predictive insights that support equitable and resilient resource planning.