How can healthcare organizations learn more effectively from growing volumes of safety reports? In this episode of Key in to Quality, Mayo Clinic leaders explore how an AI-enabled safety platform uses multi-agent systems, predictive analytics and human-centered design to identify emerging risks earlier, reduce administrative burden and support proactive prevention of patient harm.
Twenty-five years after the landmark To Err Is Human report launched the modern patient safety movement, healthcare organizations face a new challenge: learning effectively from the growing volume of safety information they collect.
In this episode of Key in to Quality, a multidisciplinary Mayo Clinic team discusses a new AI-enabled safety platform designed to address what Mayo Clinic calls the “safety reporting paradox.” Using multiple AI agents, the platform reviews safety reports, identifies reportable events, detects serious harm and sentinel events and validates results while keeping safety professionals integral to the process.
The conversation also explores a pattern discovery agent that analyzes thousands of low-harm and no-harm events to identify emerging risks before serious harm occurs, helping shift safety efforts from reactive response to proactive learning.
Another development is the “patient safety vital sign,” an AI-enabled tool designed to provide real-time, individualized assessments of patient safety risk at the bedside.
The episode concludes by examining how AI can augment human expertise, reduce administrative burden and expand safety teams’ capacity to focus on investigation, improvement and prevention.