Date: October 28, 2024
Reference: Verma et al. Clinical evaluation of a machine learning–based early warning system for patient deterioration. CMAJ September 2024
Guest Skeptic: Michael Page is currently the Director of Artificial Intelligence (AI) Commercialization at Unity Health Toronto. He leads an AI team intending to improve patient outcomes and healthcare system efficiency. Michael is a sessional lecturer within the Ivey Business School’s Executive MBA program, where he teaches a Technology and Innovation course. Previously, he held senior roles at the Vector Institute for AI, and the University of Toronto. Michael has over 15 years of experience building and leading corporate strategies for innovation, social impact, and research and development for various organizations.
Case: The Chief of Emergency Medicine (EM) at a large urban hospital recently approached the AI Committee at Unity Health, intrigued by the CMAJ article describing the apparent success of CHARTWatch in detecting early signs of patient deterioration. Their hospital has struggled with a growing number of adverse events that often occur without warning. With emergency department (ED) volumes rising, administrators are eager to explore AI-driven solutions to improve patient safety and reduce staff burnout. They want to know how CHARTWatch integrates with electronic health records (EHRs), whether it can adapt to their patient ED population, and how clinicians respond to using the tool. The Chief of EM wants to be sure that any new system they introduce will enhance workflow and not add to clinicians’ cognitive burden.
Background: There are many ways to define artificial intelligence. One definition of AI is a computer system capable of performing tasks that typically require human intelligence, such as pattern recognition, decision-making, and language processing. In recent years, advancements in AI—particularly machine learning and predictive analytics—have opened new frontiers in various industries, including healthcare.
In healthcare, AI is being leveraged to enhance clinical decision-making, streamline administrative processes, and improve patient outcomes. Machine learning algorithms, a core component of AI, can process vast amounts of data to identify patterns that might elude human clinicians. This predictive capability is transforming the way hospitals manage patient care, from optimizing staffing levels to providing personalized treatment recommendations.
A promising application of AI is the development of early warning systems to detect patient deterioration. These systems use real-time data from electronic health records (EHRs) and other sources to predict which patients are at risk of adverse outcomes, such as cardiac arrest or transfer to an intensive care unit (ICU) [1.2]. By alerting clinicians to potential problems before they become critical, AI-driven systems aim to reduce unplanned ICU transfers and improve survival rates.
Despite the potential benefits, integrating AI into clinical workflows presents challenges. Some studies suggest that the effectiveness of early warning systems varies widely [3], with factors such as alarm fatigue [4] and clinician engagement influencing outcomes. Moreover, there are ongoing debates about the balance between algorithmic precision and interpretability. Transparent, evidence-based deployment is essential to build trust and ensure these tools support rather than complicate clinical decision-making.
Clinical Question: Can the implementation of a real-time, machine learning-based early warning system (CHARTWatch) reduce adverse events and mortality in patients in the emergency department?
Reference: Verma et al. Clinical evaluation of a machine learning–based early warning system for patient deterioration. CMAJ September 2024
Population: Patients admitted to the General Internal Medicine (GIM) unit of an academic medical center
