Strategic leadership in patient safety through AI-enabled fall prevention technology

ABSTRACT Background: Inpatient falls present significant clinical and financial challenges, yet traditional prevention strategies, like bed alarms, often fail due to a multitude of reasons, such as alarm fatigue and high operational costs. Purpose: Evaluate the implementation of an AI-enabled light detection and ranging (LiDAR) sensor platform designed to mitigate inpatient falls. Methods: This quality improvement project was conducted at Emory Healthcare (EHC) utilizing the CFIR 2.0 to help guide the implementation process of deploying the LiDAR sensor platform in a pilot unit with spread to several hospitals across the system. Local and system nursing leadership, along with the system’s digital team, developed the initiative, deployed the technology, and refined the process. Process and outcome metrics were collected and monitored. Results: Implementation of the LiDAR sensor platform on the pilot unit with strong local nursing leadership demonstrated a reduction in total falls and falls with injury. The implementation process that was developed with the pilot unit was utilized to implement the technology at five other hospitals across the system with their local nursing leadership. Conclusions: Integrating the LiDAR sensor platform offered a scalable, nurse-centric solution that improved patient safety outcomes for EHC. Nurse leaders are essential when implementing innovative technology and/or process changes at hospitals to drive financial stewardship and optimize the nursing workforce.