Accepted for/Published in: JMIR Research Protocols
Date Submitted: Sep 23, 2025
Date Accepted: Mar 30, 2026
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
SAFE: Investigating the implementation and impact of AI-Assisted Fall Prevention in Hospitals—A Protocol for a Multicentre, Multimethod Study in Sweden
ABSTRACT
Background:
Artificial intelligence (AI) holds considerable potential to enhance patient safety, particularly in the prevention of in-hospital falls. Recent advances in sensor-based AI systems allow for the analysis of complex, multimodal data to generate real-time alerts, enabling healthcare professionals to intervene before a fall occurs. By shifting from reactive responses to proactive risk management, these technologies may reduce fall incidence and improve care outcomes. As a result, hospitals across Europe are increasingly adopting such systems. Nevertheless, empirical evidence concerning their routine implementation remains limited, particularly concerning their impact on patient safety, clinical workflows, and the utilization of healthcare resources. Addressing these gaps is essential for effective and sustainable integration into hospital care.
Objective:
This paper outlines the protocol for a multicentre, multimethod project investigating the implementation and impact of AI-assisted fall prevention in Swedish hospitals.
Methods:
The project is a collaboration between Halmstad University and Västra Götalands Region (VGR) and will investigate a large-scale AI system implementation in VGR hospitals from 2026 to 2028 covering up to 2400 patient beds. Using surveys, interviews, observations, and retrospective studies, it will track the implementation and impact over time. Two learning labs involving patients, healthcare professionals, and healthcare leaders will be conducted to co-develop guidelines for the implementation of AI-assisted fall prevention.
Results:
The project will provide evidence-based insights and practical guidance on AI-assisted fall prevention. The findings will be relevant not only to patients, healthcare professionals, and hospital organizations but also to policymakers and stakeholders involved in the digital transformation of healthcare.
Conclusions:
Although VGR serves as the primary research setting, the project’s results will inform future similar initiatives in Sweden and offer transferable lessons for other healthcare systems internationally. The study will contribute to the evidence base on AI-assisted fall prevention in healthcare, supporting the responsible and scalable integration of such systems across diverse healthcare environments.
Citation
